Cardiovascular system and its functions under both physiological and pathophysiological conditions have been studied for centuries. One of the most important steps in the cardiovascular research was the possibility to record cardiac electrical activity. Since then, numerous modifications and improvements have been introduced; however, an electrocardiogram still represents a golden standard in this field. This paper overviews possibilities of ECG recordings in research and clinical practice, deals with advantages and disadvantages of various approaches, and summarizes possibilities of advanced data analysis. Special emphasis is given to state-of-the-art deep learning techniques intensely expanded in a wide range of clinical applications and offering promising prospects in experimental branches. Since, according to the World Health Organization, cardiovascular diseases are the main cause of death worldwide, studying electrical activity of the heart is still of high importance for both experimental and clinical cardiology. Cardiovascular disorders are the major cause of death in developed countries. Due to the change of lifestyle, their incidence increases recently also in countries where cardiovascular morbidity and consequent mortality have not been considered a problem until now. Enormous economic and social burden of such situation promotes further research of both physiological and pathophysiological cardiovascular processes.Cardiac action starts with electrical event—membrane depolarization—which is then followed by a mechanical response, e.g., cardiac muscle contraction. After this event, called systole, the cardiac cycle continues with diastole (membrane repolarization and consequent muscle relaxation). Obtaining quite detailed information about electrical activity of the heart seems to be technically easier than obtaining comparably detailed information about mechanical events.The first attempts to study electrical processes related to cardiac action were performed already in the 19th century , based ovia a cannula inserted into the aorta of the fully explanted heart. The hydrostatic pressure of the solution closes the aortic valve, and solution enters the coronary arteries, leaves via sinus coronarius, and drips off.Concurrently to above-described milestones of ECG development and its establishment into routine clinical use, researchers were interested in electrical changes accompanying the heart activity other than whole-body models. The fundamental approach in this area is the isolated heart model. First, successful isolation and perfusion of frog heart were achieved by Elias Cyon and Carl Ludwig . Then, aA model of isolated heart perfused according to Langendorff  is the first experimental setting which enables recording of electrical activity of the heart in the style which resembles the ECG . Of courFrom Einthoven’s first ECG recording system till now, ECG recording has undergone huge transformation from a full-analog system to fully computerized ECG recorders . RegardlThe 12-lead ECG configuration gives spatial information about the cardiac electric activity . Since EReconstruction of the cardiac vector movement from the 12-lead ECG is—despite all modifications—arduous and imagination-demanding. For such analysis, orthogonal leads are much more useful. Orthogonal leads record the electric activity from three perpendicular axes of the body . Frank leads are one of the most clinically relevant orthogonal systems.The standard 12-lead system is mostly used for ambulatory ECG recording. Also, such recording is used during the exercise stress test. Ambulatory 24 h ECG monitoring—Holter monitor—is employed in diagnostics of paroxysmal cardiac events . The Holter monitor may use a 12-lead system; however, modern devices record two or three modified leads only . In caseA typical ECG curve shows five deflections labeled P, Q, R, S, and T. The P wave reflects depolarization of the atria. Deflections Q, R, and S create a thin complex—QRS complex, which represents ventricular depolarization. The R peak is always positive (pointing upward). Deflection Q is always the negative deflection preceding R peak. Deflection S is always the negative deflection following R peak. Depending on the orientation of ECG lead, some of the deflections of the QRS complex may not be expressed. The fifth deflection—the T wave—reflects repolarization of the ventricles. Between the end of P wave and the onset of QRS complex, a period of isoelectric line called the PQ segment is found. The PQ segment represents atrioventricular (AV) conduction or AV delay. The PQ segment together with the P wave forms a PQ interval. The PQ interval duration reflects propagation of depolarization from the atria to the ventricles. Between the end of QRS complex and the onset of T wave, there is a period of isoelectric line called the ST segment. During the ST segment, all the ventricular cardiomyocytes are fully depolarized, resting in the plateau phase of the action potential. Complete electric revolution of the ventricle is represented by a QT interval—the period from the onset of the QRS complex to the end of the T wave. The distance of two consecutive R peaks—RR interval—represents the duration of one electric cycle of the heart; therefore, it defines the heart rate. The amplitude of deflections as well as polarity of P and T waves depends on the orientation of ECG lead. This topic is out of the scope of this article, and therefore, it is not discussed here.ECG recording plays an irreplaceable role in diagnostics of various cardiovascular diseases. Diagnostics of arrhythmias is completely based on an ECG record. Moreover, the QT interval prolongation—an independent risk factor for ventricular arrhythmias—can be detected exclusively from an ECG. Typical ECG changes, such as the ST segment elevation and T wave alteration, are related to coronary artery disease and myocardial infarction. According to the specific changes in the 12-lead ECG record, an occlusion of coronary artery can be localized. Also, left ventricular hypertrophy can be diagnosed with high sensitivity and specificity from the ECG record . Also, oECG may also help in diagnostics of numerous non-cardiovascular diseases . TypicalInnovative approaches of ECG recording bring new challenges in ECG interpretation. Reduction of the employed electrodes leads to the reduction of ECG lead number. Recently, diagnostic potential of reduced-lead ECG was discussed during the PhysioNet/Computing in Cardiology Challenge . The resThe quality (sampling rate and resolution) of the ECG record is important particularly in animals of high heart rate .If the electric activity is recorded directly from the surface of the heart, the term “electrogram” (EG) should be preferred. In isolated heart models, originally the electric activity was recorded by needle or hook electrodes attached directly into the cardiac muscle. However, mechanical irritation caused by electrode attachment may induce ventricular arrhythmias. To minimize it, only one pair of electrodes is usually attached, and thus, one EG lead is recorded. A less-invasive approach is possible if the isolated heart is immersed into the bath containing saline . Then, an EG is recorded contact-less by electrodes placed on the inner surface of the bath . VariousTo study more complex responses of the cardiovascular system including neuro-humoral regulatory mechanisms, a whole-body animal model has to be engaged. Various animal species are used to study heart electric activity—including zebrafish, small rodents , rabbit, dog, pig, and others. A broad range of methods are used to record the ECG in animals. Generally, the methods can be divided into two groups: 1) methods used in anesthetized animals and 2) methods enabling ECG recording in conscious animals.In small animals, including rodents and rabbit, the ECG is frequently recorded in general anesthesia . In anesECG recording in zebrafish represents a specific chapter. In the last two decades, zebrafish became a valuable model in experimental cardiology due to its fast reproduction, low maintenance cost, easy breeding, possibility of genetic manipulation, extensive developmental characterization, and optical transparency . MoreoveIf the unwanted effect of general anesthesia may interfere with the experimental purpose, ECG recording in conscious animals is preferred. Even in small animals, the ECG can be recorded using telemetric monitoring . TelemetIf a short-term ECG record is required, non-invasive methods are a good choice. In large well-trainable animals, it is possible to record the ECG by standard stick-on electrodes. Such procedure requires specific training, during which the animal learns to remain motionless for several minutes. In small animals, non-invasive devices usually consist of a platform equipped with electrodes, on which an animal puts its paws . In suchIn experimental animals, interpretation of the ECG is usually more challenging than the recording. Lack of standardization in animal ECG recording and less evidence of normal  values of ECG parameters make the interpretation difficult. The differences in cardiac electric activity among various species also must be considered—especially differences in body size, heart anatomy, and cardiac ionic channel types and their distribution and regulation. The most striking difference in ECG among the species is the RR interval duration . In mammThe ECG of the most frequently used laboratory animals—rat and mouse—is quite distinct from that of humans. There is no distinct ST segment in rat and mouse ECG curves due to short ventricular action potential with minimal plateau phase. The T wave begins immediately after QRS complex. Also, the resting heart rate is significantly higher than that of humans . Moreover, significant differences between various strains of the same species were repeatedly reported .After the ECG is recorded, its pre-processing and analysis may start. The ECG analysis is meant to be a process which gives valuable information about the examined subject  in terms of cardiac electrophysiology. The early analyses of ECG in the beginning of the 20th century were performed only for measuring time relations in the signal. The introduction of computer technology in the middle of the 20th century opened new possibilities for the analysis of ECG. Despite all the advantages of computer technology, manual evaluation of the ECG by a cardiologist remains an indispensable part of diagnosis. The following paragraphs briefly summarize the development of ECG analysis, the basic principles of the main techniques in clinical and experimental perspectives, and, finally, the main current issues and future directions in this field. Many physiologists at the end of the 19th century were convinced that the mechanical contraction of the heart is preceded by an electrical action that could be measured. In the first attempts to record electrical potentials of the heart muscle, only heart depolarization and repolarization were recognizable . This leAlong with the improvement of electrical activity recording, the first attempt to quantitatively describe how the heart function is transcribed into the ECG has been done. Numerous equations have been developed to identify the relationships between the heart function and ECG patterns. These equations helped to discover phenomena, which were not visible at the first glance. One of such measures, outlasting in the clinical and experimental use to present, is the QT interval corrected by Bazett’s formula . It reflFrom today’s perspective, these pioneers have come a long way. The foundations laid by them made it possible to develop the robust and reliable computational methods in the second half of the 20th century.Rapid development of computer technologies in the second half of the 20th century led to the extensive automation of various processes traditionally performed by human experts. In medicine, along with analysis of complex imaging data , the diagnostics of cardiac disorders intensely uses advanced, highly intelligent computer-aided systems. Probably, the most evident benefit of the computer algorithms is in their ability to process a huge amount of data in a very short time e.g., , a proceFrom medical point of view, use of the most advanced methods for ECG analysis offers a simple, widely available, cheap diagnostics tool. By accurate, sensitive, fast, and robust detection of pathological patterns in the ECG, these methods contribute to early diagnosis, selecting the correct treatment strategy, better outcomes, and improved life quality of the patients on the one side and to minimized mental and time demands of clinical staff and decreased national healthcare financial resources on the other side. Automatic prediction systems are also useful in preventive medicine.Of course, computer algorithms make errors, especially when a low-quality signal or signal with unknown  abnormality is analyzed. The computer more likely fails when similar manifestations correspond to different pathologies. In contrast to the computer algorithms, human experts use clinical information about the patient and intuition. Therefore, all statements of the computer systems must be over-read by skilled physicians . Neverthvia decision rules pre-determined in cooperation with clinician experts; 3) by using machine learning (ML) tools—so-called supervised learning algorithms, which are able to learn the interpretations from training data equipped with ground truth labels with no need for expert rules; and 4) by using state-of-the-art tools—so-called deep learning (DL) models.Traditional computer-aided ECG interpretation consists of the following steps: pre-processing, computation of so-called features, selection of the most relevant ones, and, finally, decision-making , top. ECFeature-based decision-making is usually provided by ML models, from simple linear discriminant analysis (LDA) or logistic regression to more advanced k-nearest neighbor (k-NN), decision tree, random forest, support vector machine (SVM), multiple-layer perceptron (MLP), etc. From mathematical background, most are based on using one or combination of several linear or non-linear equations, which map current input data (ECG samples or features) into the predicted output (label/diagnosis)  with feedforward information flow and recurrent neural net (RNN) with some feedback connections—are mostly used. CNNs process the input with a set of special filters to extract hidden, high-resolution patterns . RNNs caThe clinical ECGs are usually corrupted with various types of noise, which may lead to errors in visual inspection and to inaccurate function of automatic delineation and interpretation algorithms. The low-frequency baseline wander, power-line interference (50/60 Hz), and high-frequency noise are those seen in raw ECG. The first one is primarily caused by respiratory movements or poor skin–electrode contact and may cause inaccurate measurement of ST segment, wave amplitudes, R-peak detection, etc. The power-line interference of high magnitude makes the analysis of low-voltage P waves impossible. The high-frequency noise, representing the myopotentials generated during skeletal muscle contraction, complicates the ECG delineation. Digital filters, implemented in a high-pass, band-pass, or low-pass mode with appropriate cut-off frequencies, are usually used to suppress above disturbances in the ECG . The desAfter proper noise elimination, the R peaks and/or other fiducial points can be detected. Probably, the most famous and frequently used is the rather robust and simple Pan–Tompkins algorithm . Many otComplete delineation of ECG can be performed by the CNN–LSTM model with a sensitivity of 97.95% . AnotherAnother interesting study was focused on creating the mathematical models able to “extract” the waves from the ECG to make further detection of the fiducial points easier . This meAlmost all existing methods for ECG pre-processing and analysis have been initially established for human signals. The morphology characteristics of animal ECG are like human ones. However, some differences in heart rate and ECG wave amplitudes and duration exist due to differences in ion channel expression  as well The median filter can be successfully applied on clinical and experimental ECG data to remove low-frequency baseline wander, which complicates the signal delineation and decreases the reliability of morphology features . For elimination of broad-band noise (such as muscle activity) in the ECG, the above-mentioned decomposition of the entire signal by discrete wavelet transform and reconstruction of “clean” ECG after removing some components containing noise is more suitable than standard linear filtration . This meThe narrow-band signals created by ECG wavelet decomposition are often used for accurate detection of waves and QRS complexes. First, signals with well prominent waves of interest are selected, and then the complexes are detected by threshold, which can be constant or adaptively changing according to current signal maximum amplitude or variability. This QRS detection technique was shown as more effective and accurate  than the Pan–Tompkins algorithm  in equine ECGs . This technique can be used for complete delineation of the entire ECG , as presented by The first derivative of ECG and further searching for rapid deviations can be used for complete delineation of the signal as shown in detail by - Time-domain features primarily representing 1) the rhythm character of the signal in terms of heart rate and duration of some important intervals , 2) the morphology of ECG curve in terms of amplitude, duration, area under the curve, direction, and other shape-related characteristics of the particular waves, and 3) the statistical characteristics of ECG distribution .- Frequency-domain or time–frequency domain features creating the new representation of ECG by using special transformation (such as Fourier transform (FT), short-term Fourier transform (STFT), and continuous or discrete wavelet transform (CWT/DWT)) and exploring the ECG spectral content and its changes in time.- Non-linear features revealing non-linear, complex, dynamic character of ECG (hidden to the above methods) based on the chaos theory and information theory .Features should represent well ECG patterns characteristic for investigated phenomenon . The quantitative representation of heart electrical activity by a limited number of highly specific features allows to construct the efficient, robust, fast, and reliable methods for automatic identification and differentiation of various cardiac conditions, as will be shown below. Generally, three main groups of “hand-crafted” ECG features are used in computer-aided systems e.g., :- Time-dSignificant temporal features, intensely used in various areas, are those describing the heart rate variability (HRV). Initially, HRV analysis was introduced to investigate the vago-sympathetic balance and corresponding phenomena . These ft-test and others), or their combinations , decision tree, etc.) or searches among the feature set until the combination leading to the best performance of the model is found  , etc.), discriminating abilities of separate features  .Feature-based diagnostics tools have been used for several decades and still play a crucial role in clinical and experimental research, primarily due to their transparency. Below, recent ECG applications intensely using these methods are reported.Multi-lead ECG can be used to accurately detect left ventricular hypertrophy, which is a less expensive, less time-consuming, and widely available alternative to the golden standard imaging techniques. Morphological features  extracted from CWT-transformed ECGs perform well with ML classifiers, with the most promising accuracy (up to 97.8%) using multi-layer perceptrons .One example of using the morphology features derived from P–QRS–T segments is the recognition of different beat types, such as normal, supraventricular, and ventricular ectopic beats and fusion of ventricular and normal beats. The SVM model trained on these features performs with overall accuracy 97.8% . The arrA novel tool based on the multi-lead ultra-high-frequency ECG and QRS complex features has been recently introduced. It provides the quantitative description of ventricular dyssynchrony with a great potential for selecting the patients for cardiac resynchronization therapy and improving the therapy application . One of A recent prospective study presents temporal, spectral, and non-linear HRV parameters as the valuable prognostic tool for evaluation of mortality risks in patients with coronary artery disease .The most challenging recent application using HRV features is atrial fibrillation (AFIB) detection e.g., . Timely Novel multi-scale entropy has been shown as an accurate HRV-based tool for prediction of the malignant ventricular arrhythmia, even using a simple random forest classifier . StandarA method based on only six time- and frequency-domain HRV features and a simple k-NN classifier can predict the sudden cardiac death from five-minute RR interval signals recorded by an implantable cardioverter defibrillator with an accuracy of 91.5% .The random survival forest (RSF) ML model was used to predict spontaneous ventricular tachycardia and ventricular fibrillation events in young and adult patients with long QT syndrome (LQTS) . The modThe basic temporal ECG features such as heart rate and PR and QT interval duration as well as QRS complex duration are routinely used to monitor the character of impulse conduction through different parts of the heart. The assessment of the combination of these features is especially useful when evaluating the possible effects of drugs.Detailed analysis of QT duration in rabbit protocols with drug-induced long-QT or short-QT syndrome indicated the possible effect of acute mechano-electrical function on long-QT syndrome–related arrhythmogenesis . GeneralHRV in the isolated heart model can serve to investigate the intrinsic mechanisms of the cardiac rhythm regulation with no effect of sympathetic/parasympathetic factor . SpectraAccording to comprehensive overview by The TINN feature has been found as associated with severity of myxomatous mitral valve disease in dogs  and was Morphological features were found to accurately detect ischemia in the Langendorff-perfused isolated rabbit heart with normal anatomy as well as spontaneously increased left ventricular mass . The rhyThe use of ML-based methods in experimental or veterinary context is usually limited by a small amount of collected data. In case of long-term experiments, however, these techniques could be very helpful to carry out fast, robust, and reliable analysis. For example, Many “all-in-one” solutions are working with a raw ECG or the pre-processed, noise-free ECG or 2D “image” representations of ECG created by converting the 1D signal into the so-called spectrogram  or, more straightforwardly, by saving the ECG segments in some image format for further analysis. These solutions often use the hybrid model, when the first part  extracts the most reliable features from the input and the second part  provides the final decision-making .Here, the overview of the most recent DL approaches is introduced. To our best knowledge, there is no all-in-one DL solution in experimental physiology applications due to limited data sources. We believe that this gap will be removed soon due to transfer of learning techniques we address in the last section and illustrate the example from the veterinary field.Many approaches are focused on specific problems, which allows to reach promising results with a relatively simple model architecture and low time and computational requirements even in case of limited data amount. Great improvements based on DL method use were achieved in the detection of acute myocardial infarction and stable ischemic heart disease, with the detection accuracy in the range of 83–99.9% for different model configurations  . MyocardThe accuracy of left ventricular hypertrophy detection reaches up to 85.8% when using CNN-extracted ECG features in combination with six-layer-perceptron–extracted demographic features, which outperform routine Sokolow-Lyon criteria and visual inspection  . KhurshiLeft ventricular systolic dysfunction (LVSD) in critically ill patients can be accurately (71–76%) detected from ECG, which is less time-consuming and less expensive than usually used transthoracic echocardiography. Additional benefit of the DL method is its robustness and efficiency in both patients with and without atrial fibrillation, which is commonly associated with LVSD . The latAccording to recent meta-analysis , CNN or AFIB detection and differentiation between terminating and non-terminating AFIB episodes were recently performed via time–frequency ECG representation (by chirplet transform) and 2D CNN–biLSTM classifier with an accuracy of 99 and 75.9%, respectively . The autvia the ResNet model with the accuracy of 79.4%. This approach offers fast, inexpensive identification of the patients with a high likelihood of AFIB by timely revealing of the structural changes in atria before the presence of any symptoms.According to interesting retrospective research , an eighDetection of abnormal cardiac rhythms , which can respond to electrical shock therapy with further expected restoration of normal sinus rhythm and, consequently, normal cardiac pump function, achieved the best accuracy (91.14%) for the CNN model .Recently, there is an effort to design approaches for differentiation of dozens of arrhythmias in ECG by only one complex model. The 1D-CNN proposed by According to Compared to the CNN, ResNet, and CNN–biLSTM, the hybrid CNN–transformer model combined with temporal ECG features achieved higher performance in recognizing nine different beat types . The traHowever, adaptation of existing complex architectures, initially proposed for image  or natural language  analysis, has strong limitations as reported by the systematic study of Transfer learning is a research method in machine learning, where knowledge from one area is applied to solve the different, but still relevant issue. Transfer learning, when applying by proper way, may solve many different problems. One of them is a problem with heterogeneity of patients’ data. It would be practically useful, if once the model is trained on some data , so that it could be directly applied on data with different diagnostics background . Most probably, however, this will not work well due to highly inconsistent character of patterns from two datasets. Nevertheless, successful results can be obtained when training model on one (freely available and extended) dataset and re-training it later on the other dataset consisting of samples of interest . In this case, the knowledge learned during the first training and saved in the network weights is used for analysis of the second dataset. During the re-training procedure, the weights from the first layers of the pre-trained model (extracting the relevant features from input data) remain with no changes, and only last layers (providing the classification or prediction itself) are re-trained using new data, which is computationally effective. This transferring of knowledge between the domains seems to be a way for effective usage of the models, well-established in other areas, directly in medical applications, with no extra time and source investments. It could be of special importance in problems, where there are not enough data for efficient training of the model.DL architectures, initially proposed for image analysis, speech recognition, and other data, can be successfully used for ECG interpretation after appropriate modifications, as has been shown in clinical applications. The most exciting and beneficial application of transfer learning was shown in animal data analysis, when the standard use of DL methods is strongly limited by a small amount of available data. A few published reports using DL for animal data analysis are focused mainly on image processing, e.g., detection of canine mammary tumor , detectivia a newly proposed symmetric projection attractor calculated from two-lead ECG. They were used to distinguish between wild-type mice and Scn5+/− mutant mice suffering from impaired function of cardiac sodium channel.From all the above, it seems that DL models can learn not only general but also more specific patterns during transfer learning. Although the DL methods have been recently applied exclusively in veterinary medicine where relatively large amounts of data are available, it seems to be reasonable—due to transfer learning technique—to expect expansion of DL models in experimental cardiology.The lack of data often concerns only certain groups of arrhythmias, such as those with rare incidence in the population or paroxysmal ones, where the ECG manifestation cannot be easily recorded during standard clinical examination. During the training process, the model extracts important information from input samples and uses it for final decision-making. In case of a severely imbalanced train dataset , the model will “focus” on the abnormalities from the majority group(s) and will ignore those from the minority group(s). Most of available ECG databases include much more normal sinus rhythm data than abnormal ones and, thus, are imbalanced. There are some techniques which reduce or eliminate this problem and, thus, ensure effective training of the model.The simplest way to make the size of particular categories equal is resampling of the training dataset, where the majority class is reduced by random selection of the desired number of samples (undersampling) or the minority class is extended by random repeating of selected samples (oversampling). However, removing of samples from the training set may lead to loss of important information. Simple repeating of minority samples will not make the dataset variable and representative and may not ensure effective model training. Therefore, more sophisticated—so-called augmentation—methods are usually applied.Augmentation techniques generate new training samples by adding some perturbation in data resulting in improved robustness of the model. First, some manipulations can be applied on initial data, such as random scaling, flipping, shifting, and noising ECG, to achieve accurate detection of multiple arrhythmias . The samFrom above reports, synthetic ECGs are realistic enough to be used in practice and, consequently, contribute to the expansion of high-performance ML or DL techniques in a wide range of clinical and experimental applications. In the latter area, besides all the above, augmentation approaches, based on data generated by mathematical computational models, seem to have special potential. Models of different complexity—from molecular to organ levels—are available  for generating the relevant, realistic, clearly interpretable electrophysiological data.Effective interaction of a human expert  with the computer-aided system requires trust in the computer’s decision-making. Therefore, an adoption of DL-based diagnostic systems in the routine clinical practice is limited, despite their high performance. As a result, there is a big effort in creating the tools helping to uncover the processes behind the model’s prediction. Such “explaining” methods may help the experts to understand the computer-aided tools and to “safely” use their output to make the final decision. Of course, rather transparent feature-based methods with simple ML models  can be used. These are, however, less successful as DL-based systems (see above). It seems to be more suitable to use the high-performance, though non-transparent, DL algorithms and apply additional techniques to “open” the black box. Many different methods and algorithms have been proposed in the last decade to solve this issue.Probably, the easiest solution is to visualize the outputs from those layers of deep networks, which generate the features from input data . The generated feature maps can indicate the parts of the ECG playing the most important role in resulting diagnosis , etc.. TMost comprehensive explanation can be obtained by special algorithms, such as Shapley Additive Explanations (SHAP), which assigns the importance weight to each sample by exploring the gradient, calculated when the sample enters the model . These wA huge amount of long-term ECG data requires a lot of time for detailed data labeling, which is crucial for accurate diagnosis and for potential use when creating the ML- or DL-based computer-aided systems. The labeling process is extremely time-consuming and expensive. Especially when each beat should be labeled separately, the risk of misinterpretations due to intensive cognitive load or poor experience of the physician is high. Therefore, multiple-instance learning (MIL) enabling model training using only global labels with no beat-wise annotations can be very useful. MIL is a type of supervised learning, which works with labeled bags of data (instead of the labeled instances) . When prThe number of DL studies focused on cardiac arrhythmia detection and classification has intensively grown during the last five years, most probably due to the PhysioNet/Computing in Cardiology Challenge conducted in 2017, where an extended, highly variable database was introduced to the wide audience . Many chAnother issue, which needs to be solved soon, is a lack of the guidelines for creating and evaluating the diagnostic ML and DL algorithms. The present article reviews current perspectives of ECG recording and analysis. It presents a unique combination of clinical and experimental points of view. This approach may attract a broad range of readers—not only researchers in the area of cardiology but also biomedical engineers, mathematicians, and last but not least clinicians. Looking at the same topic from various angles and summarizing information obtained from various models may help interested readers of numerous specializations understand each other better. As a result, it might help to plan future research activities of multidisciplinary teams, consisting of both researchers and clinicians.In the 21st century, recording of electrical activity of the heart muscle in the form of electrocardiogram may appear rather obsolete. The truth is that numerous sophisticated methods enable the researchers and clinicians to obtain information about electrical and mechanical activities of the heart and visualize these processes. On the contrary, new ways of ECG recording together with advanced methods of its analysis open new possibilities for its use.


Information on electrocardiogram (ECG) has not been quantified in obstructive coronary artery disease (ObCAD), despite the deep learning (DL) algorithm being proposed as an effective diagnostic tool for acute myocardial infarction (AMI). Therefore, this study adopted a DL algorithm to suggest the screening of ObCAD from ECG.ECG voltage-time traces within a week from coronary angiography (CAG) were extracted for the patients who received CAG for suspected CAD in a single tertiary hospital from 2008 to 2020. After separating the AMI group, those were classified into ObCAD and non-ObCAD groups based on the CAG results. A DL-based model adopting ResNet was built to extract information from ECG data in the patients with ObCAD relative to those with non-ObCAD, and compared the performance with AMI. Moreover, subgroup analysis was conducted using ECG patterns of computer-assisted ECG interpretation.The DL model demonstrated modest performance in suggesting the probability of ObCAD but excellent performance in detecting AMI. The AUC of the ObCAD model adopting 1D ResNet was 0.693 and 0.923 in detecting AMI. The accuracy, sensitivity, specificity, and F1 score of the DL model for screening ObCAD were 0.638, 0.639, 0.636, and 0.634, respectively, while the figures were up to 0.885, 0.769, 0.921, and 0.758 for detecting AMI, respectively. Subgroup analysis showed that the difference between normal and abnormal/borderline ECG groups was not notable.ECG-based DL model showed fair performance for assessing ObCAD and it may serve as an adjunct to the pre-test probability in patients with suspected ObCAD during the initial evaluation. With further refinement and evaluation, ECG coupled with the DL algorithm may provide potential front-line screening support in the resource-intensive diagnostic pathways. Electrocardiogram (ECG) is a mainstay in the diagnosis of acute myocardial infarction (AMI) with biomarkers: a rise in troponin with at least one value > 99th percent upper reference limit –3. In emOn the other hand, the resting 12-lead ECG has not been critical for screening and diagnosing coronary artery disease (CAD) in patients with stable chest pain and suspected angina pectoris, but it remains an indispensable component of an initial evaluation . AccordiRecently, deep learning (DL) algorithms have demonstrated good to excellent performance in detecting AMI using ECG signals. A review study revealed the accuracy ranged from 80.6 to 99.9% for normal versus AMI detection in 11 DL-based models, and the other review study showed it from 83 to 99.9% in six DL models , 8. AlthTherefore, a DL-based model was developed using ECG to suggest the need for further investigation for ObCAD in patients with chest pain and suspected ObCAD. Moreover, the performance of the model was evaluated to test the validity for screening ObCAD and compare it with that of AMI.This investigation was a retrospective observational study of consecutive patients who received coronary angiography (CAG) for suspected CAD in a single tertiary hospital. The patients were eligible if they were aged 18 years or older and underwent CAG due to suspected ObCAD from October 27, 2008, to August 21, 2020, at the Inha University Hospital, which was a university teaching hospital in Incheon, which had a population of 2,922,121 inhabitants in 2020, in South Korea. It has Regional Cardiocerebrovascular Centers (RCCVCs), established by the Ministry of Health and Welfare in the Incheon district.The digital, standard 10-second, 12-lead ECG was acquired in the supine position during the study period. ECG was acquired at a sampling rate of 500 Hz using a GE-Marquette ECG machine , and the raw data on ECG were extracted from the MUSE data management system .The ECG was selected in a window of interest for each participant for analysis because most of the study participants had multiple ECG records over the study period. The index date and time were defined as the date and time when CAG started, and the window of interest was defined as the preceding seven days before the index date. The ECG within a week before CAG was selected for analysis. This window of interest was chosen under the assumption that the ECG within a week would have clues on the quantitative coronary angiography (QCA) stenosis in patients with ObCAD. Any patient who did not have an ECG in the window of interest was excluded. If patients had multiple ECGs in the preceding seven days, the most recent ECG, for which physicians decided whether to provide diagnostic CAG, was selected. Figure For the comparison of data distribution among the non-ObCAD, ObCAD, and AMI groups, 31 electrocardiographic patterns and eight quantitative ECG measurements were extracted from the ECG data and summarized for each group . The eigThe CAG reports were extracted from the electronic medical system. The dataset was divided into acute myocardial infarction and suspected angina pectoris to compare the performances between the DL models of ObCAD and AMI. After excluding the patients finally diagnosed with AMI, ObCAD was defined as the stenosis ≥ 50% luminal narrowing of any major vessel in QCA, and non-ObCAD as < 50%; it was defined to identify patients whose QCA showed significant stenosis more than 50% and those who could have benefited from further non-invasive diagnostic tests and CAG –17.1D ResNet was suggested as a useful architecture for classifying ECG –20. The A resting ECG consists of 12 vectors with 5,000 dimensions per sample, which is a very large input dimension compared to the number of training samples. Therefore, raw ECG signal is not suitable for use in traditional ML classifiers such as LR and RF. We used a fast Fourier transform (FFT) to extract the 10–100 Hz range from each lead in 10 Hz intervals to transform it into a dimensionality suitable for use with traditional ML classifiers. This was finally transformed into a vector with a total of 120 dimensions for the 12 leads. Then, ECG transformed by FFT was used as input for LR and RF. Using grid search, we set the hyperparameters of the RF model: number of estimators, minimum number of samples required to split, and minimum number of samples required to be at a leaf node to 100, 2, and 2, respectively. Bi-LSTM used L2 kernel regularizer, dropout 0.2, and ReLU activation function. In this study, we did not use the Transformer Encoder immediately before the Fully Connected (FC) Layer as in the study for ECG Arrhythmia Classification to identify the ECG pattern of ObCAD, but adopted the method of analyzing the time series with LSTM by determining and weighting the importance through self-attention to the extracted convolutional neural network (CNN) features. The ECG signal is compressed to a size of  through 1D CNN, and the same size  data is extracted through MultiHeadAttention. Finally, it was classified by sequentially passing the LSTM layer, FC layer, and sigmoid function.The eight physical leads  were used in the 12-lead ECG because the four augmented leads  were produced by a linear function of leads I and II. The horizontal long axis (10 s at 500 Hz) was denoted as the temporal axis to extract the morphological and temporal features, while the short axis  represented the spatial axis to use layers from all the leads.The ECG DL models were cross-validated using stratified 5-fold to estimate the average predictive performance. The validation set was randomly selected from the train set and the dataset was divided into train, validation, and test sets with an 3:1:1 ratio. The training dataset was used to train the DL model and optimize the hyperparameters with a validation dataset. The test dataset containing the remaining patients not used in the training or validation was used to evaluate the performance of the ECG-based DL algorithm. A diagnostic threshold was selected using the area under the curve (AUC) of the receiving operating characteristic (ROC) curve for the validation set. The threshold was then applied to the test dataset to calculate the precision, recall, accuracy, and F1 score.The hyperparameters were compared among the following options: ResNet with the residual blocks , kernel size , batch size , initial learning rate , optimization algorithms , and dropout rate . The best hyperparameters achieving the highest F1-score in the validation set were 4, 16, 8, 0.001, and 0.3, respectively, for residual blocks, kernel size, batch size, initial learning rate, and dropout rate with optimization algorithms of the Adam optimizer.Subgroup analysis was performed to get insight into whether the performance depended on the ECG diagnostic abnormalities in the interpretation which were provided by the GE-Marquette ECG machine.After excluding the CAG unmatched with ECG and duplicated reports, 14,080 CAG reports were included with matched 41,355 ECG data  in the review, which performed as well as six models for detecting AMI . HoweverIn this study, the performance of the DL model for screening ObCAD was modest compared to that of the model for diagnosing AMI. The ECG findings of AMI were more remarkable because AMI causes more irreversible tissue damage to the myocardium than ObCAD with stable chest pain. In contrast, the ECG perturbations tend to be subtle and have difficulty classifying ObCAD . AlthougCurrent practice depends on the clinician’s interpretation on ECG when the patient with stable chest pain and suspected ObCAD is evaluated. Therefore, it is subjective according to the knowledge and experience of the clinician and requires time and effort in the field. It also could not be quantified and integrated into any quantitative estimation of risk stratification. In contrast, the DL-based ECG model could contribute to automated ECG interpretation and risk quantification. Furthermore, while ECG may be a poor predictor from the perspective of the human eye and computer-assisted ECG features provided by GE machine, ECG characteristics have been demonstrated to be related to the prediction of ObCAD , 34–36. In this study, the proposed model adopting 1D ResNet was superior to other ML and DL models. It may be because LR and RF cannot reflect time series features when learning ECG signals. In particular, although RF improves generalization performance by preventing overfitting of decision trees through ensembles, we experimentally confirmed that it still tends to overfit in high-dimensional data . To prevThe main strength of this study was the inclusion of a contemporary population with suspected ObCAD and who received CAG. Therefore, there is little risk of misclassification because the classification was based on the CAG reports. Moreover, the number of the subjects was higher than those of previous experiments, which offers more variation in the ECG data and is close to the real world. Furthermore, the DL-based model could help more people receive an earlier diagnosis and treatment compared to traditional ECG interpretation and help to reduce unnecessary diagnostic tests in the current practice. Compared to traditional ECG interpretations, the DL-based model could lead to the earlier diagnosis of 58% more people with CAG and the earlier treatment of patients with stable chest pain and suspected ObCAD: 646 (15%) of the traditional interpretation vs. 3,050 (73%) of the DL model in 4,178 patients with QCA stenosis. Furthermore, 62% of the 4,293 patients with a low probability of ObCAD by the DL model could be excluded from unnecessary non-invasive diagnostic tests and CAG.This study had some limitations. This was a retrospective study based on subjects who were not all comers with chest pain but had received CAG. Therefore, the enrolled subjects were the selected patients considered high-risk by a physician, which may limit generalization. Second, these results need to be transformed into applications in the future, such as being implemented in a prospective study, to confirm the performance. Third, although the DL algorithm showed good performance in automatically detecting AMI, it still showed fair accuracy and sensitivity in screening for ObCAD. Therefore, the results of this study should be interpreted with caution in clinical practice, and the DL-based ECG model should be innovatively improved for practical use in the future. Fourth, the characteristics of symptoms, which were critical in clinical practice, could not be covered by a better ECG interpretation. Finally, it is important to explore further the ECG components that contribute to the classification. Recently, efforts have been made to develop new technologies that could make machine-learning models interpretable or explainable.This study examined the possibility of adopting a DL algorithm to ECG for screening ObCAD and compared the performance of the ObCAD model with that of the AMI model. Although the model showed good to excellent performance in detecting AMI, it demonstrated only modest performance in suggesting the probability of ObCAD and required further enhancement. Nevertheless, information from ECG extracted by the DL algorithm may serve as an adjunct to an initial assessment by clinicians in addition to the pre-test probability. With further refinement and evaluation, ECG coupled with the DL algorithm may provide potential front-line screening support to assist clinicians in the resource-intensive diagnostic pathways.


Wilde was highly preceptive especially in his views on the recording of medical data and outcomes in clinical practice. We argue that Wilde was probably unmatched in the variety of his talents but was also perplexing in the various actions he took during his life and that indeed Wilde was an enigma.William Wilde, father of Oscar Wilde, made a significant contribution to ophthalmology and otology. Qualified as a surgeon. educated in statistics and showing sympathy for the Irish population, Wilde was appointed a Commissioner for the 1851 Census, which covered the time of the Irish Famine (1845–1852). Wilde, steeped in Irish mythology, used his knowledge to develop a close rapport with the Irish peasantry. However, his life was a paradox; he supported the British Government's approach to the Famine and at the same time he showed humanity to the Irish peasantry. In his personal life he was implicated in an abortive libel case involving a young female patient who had accused him of rape. Wilde lived as though he had two separate lives By today's standards Wilde's education was somewhat unconventional. He did not attend university though he understood the importance of such institutions. As a precocious 23-year-old he criticized Trinity College Dublin for not investing in studies including natural sciences, philology, Semitic languages, and Oriental studies. Wilde's encyclopedic knowledge of subjects ranging from medicine to natural sciences, art, archeology, Celtic folklore, literature, and statistics singles him out as a man with an avaricious appetite for knowledge enhanced by travel and interactions with the intellectuals of his time.Many of William Wilde's achievements have been over-shadowed by the success and notoriety of his son, Oscar (1854–1900). However, although William Wilde was an innovator in the field of medicine and surgery, historians hesitate to rank him as a true academic physician scientist.In this paper, we seek to evaluate the influence of his background and education on his medical, sociopolitical, and artistic achievements. We argue that, while his background and intellectual gifts helped make him ahead of his time, it also resulted in a complex conflicted personality creating an enigma. We also suggest that, despite major upheavals in his life, Wilde was a sympathetic doctor and a kind, committed humanitarian. In their journeys, the Wildes would have encountered the underlying agrarian agitation in these deprived, dispossessed people which frequently exploded into rebellion by organized militant groups including the “Ribbonmen”, a 19th-century movement of poor Catholics in Ireland. These encounters and rural experiences armed William with valuable insights, setting him apart from his urban middle-class Dublin colleagues.William Wilde grew up in rural County Roscommon where he frequently accompanied his father Dr Thomas Wilde (1760–1838) on his daily rounds. Apart from influencing his choice of career, these childhood experiences offered William the opportunity to become acquainted with the rural poor, including their ideologies, diet and living conditions. He befriended many and enjoyed listening to their tales of witches, ghosts, saints, and fairies, resulting in a life-long interest in Celtic folklore. This elderly Augustinian cleric, a fluent Irish speaker, lived on the lands owned by his mother's family. At that time Father Patrick had in his possession priceless antiquities which are now to be seen in Dublin's National Museum. Wilde received his early formal education at the Banagher Royal School, in County Offaly, which had been given its royal charter by Charles I. The school was finally established in 1806 and in Wilde's time the Master was the Reverend Dr Alan Bell (1789–1839) a graduate of Trinity College Dublin and Glasgow University. There were 30 to 35 pupil's half of whom were boarders and half day boys. The pupils were the sons of clergymen of the Established Church, of army officers, doctors and other children of the wealthier middle classes of Connaught. The curriculum included Latin and Greek, French, Hebrew and English, algebra, Euclid (geometry), arithmetic ), geography, and history. Later Wilde attended at the Elphin Diocesan School, Co Roscommon, which had been founded in 1685 by Doctor John Hodson (1604–1686) Anglican Bishop of Elphin. When Wilde attended the school, the Master was the Reverend William Smith (1801–1841), who was the curate of Elphin and the Vicar General of the Diocese. The curriculum at the Elphin school also included Euclid, algebra, and arithmetic as well as Latin and Greek along with English and bookkeeping, with the pupils aiming for entrance to Trinity College Dublin. Interestingly, at the Elphin school in 1826 there were 19 pupils in attendance of whom 10 were of the Established Church and nine were Roman Catholics and of the total number of 19, 11 were educated for free. It is therefore clear that William Wilde had an all-round education, learning Irish and about the antiquities from Father Patrick and the classics and mathematics along with history and English from the two schools, which must have laid a sound basis for his future scholarship.William Wilde's earliest interests in education, archaeology, and natural sciences were provided by private tutorials, given to him by a retired monk, the Lord Abbott Cong, Father Patrick Prendergast (1741–1829). In 1837 Wilde was licensed to practice surgery by the Royal College of Surgeons in Ireland and shortly afterwards travelled abroad, chaperoning a wealthy patient of Dr Graves on a Mediterranean cruise. After leaving the patient Wilde spent several months in Egypt which had a defining influence on his life. His interests in botany, zoology and, more significantly, archaeology were stimulated, and he recorded his European travels and explorations in a two-volume book. Wilde's curiosity in ophthalmology was aroused when in Alexandria and Cairo he encountered the effects of trachoma, a Chlamydial bacterial eye infection that led to partial or complete blindness.At the age of 17 years, Wilde enrolled as a medical student at Dublin's Park Street Medical School and Dr Steevens’ Hospital where he was apprenticed to the surgeon Abraham Colles (1773–1843) and was taught by the physicians Robert Graves (1793–1853) and William Stokes (1804–1877). By all accounts he was an exceptional student, submitting a prize-winning paper on spina bifida while on his obstetric rotation at the Rotunda Hospital. and had treated the poet John Keats (1795–1821) during Keats’ illness in Rome. Through Clark, Wilde was introduced to William Farr (1807–1883), epidemiologist and one of the founders of medical statistics.Following his travels to Egypt, to gain further experience in diseases of the eye, Wilde went to London in 1840, spending six months at Moorfield's Eye Hospital. With the help of letters of introduction from the author Maria Edgeworth (1768–1849), he quickly accumulated a group of influential friends including Sir James Clark, (1788–1870), Queen Victoria's physician. Clark, like Wilde, had written a book on his European travels He made full use of his time in Vienna and recorded his experiences in a book, Austria: Its Literary, Scientific and Medical Institutions, in which, using the new statistical skills he had acquired, Wilde analyzed the Austrian Government's medical, social, and educational polices. Emer O'Sullivan writes in The Fall of the House of Wilde (2016) that Wilde was “an intellectual voicing his opinion in Austria's Civil Rights as well as charging the Church and Monarchy with interference in scientific inquiry.” In Vienna, Wilde demonstrated his qualities of clinical observation when he noted the extremely high rate of puerperal sepsis in the obstetric wards. Wilde believed that the Austrian doctors did not consider puerperal fever infectious, writing “I myself have seen a newly-delivered woman placed in a bed scarcely yet cold, in which a death from puerperal fever had taken place two hours before!” He was especially critical of the Viennese doctors, castigating them for not taking such elementary precautions, including cleansing, fumigating, whitewashing, and closing infected wards, when managing patients where there was puerperal sepsis.Intent on furthering his knowledge of ophthalmology and to gain experience in diseases of the ear, in 1840 Wilde travelled to Vienna where he enrolled as an observer at the Allgemeines Krankenhaus. Semmelweis introduced hand washing with chlorinated lime water dramatically reducing the incidence of puerperal sepsis. These methods were similar to those that Wilde's made seven years earlier when as a medical student he used comparable protocols following the death, from cholera, of a patient he had nursed in Galway. Both Wilde and Semmelweis's observations were made 20 years before Joseph Lister's (1827–1912) seminal publication on antiseptics in surgery in 1867. In 1840–1841 Wilde travelled further in Europe, visiting surgical centres and meeting with intellectuals and royalty, showing his ability to be equally at ease with European aristocrats and Irish peasants. In Berlin, he studied with the pioneering plastic surgeon Johann Friedrich Dieffenbach (1792–1847). Later in 1841 he returned to Dublin where he established his first clinic at St Mark's Hospital, dedicated to the treatment of diseases of both the eye and the ear, and largely for poor patients. The hospital had 20 public and three private inpatient beds and became so successful that in 1850 he purchased the recently vacated Park Street Medical School, where he had been a medical student, and transferred St Mark's to that site. This hospital became the premier eye infirmary and one of the only hospitals in the British Isles specializing in diseases of the ear. Wilde attracted patients to the clinic from all over Ireland, along with students from Europe and North America.Some years later, in 1847, Ignaz Semmelweis (1818–1865), who became a life-long friend of Wilde and who also worked at Allgemeines Krankenhaus, took this further. Semmelweis noticed there was an association between contaminated tissue, transported from the autopsy room to the labor ward and borne on the hands of medical students and doctors, and puerperal sepsis.Practical Observations on Aural Surgery, he wrote.statistical tables and calculations are really valuable only when we can rely upon the original investigations from which they were deduced; if the materials have been loosely collected, or for any special purpose, or support any preconceived theory, such circumstances naturally influence the value to be set upon all subsequent arrangements, no matter how ingenious.This observation remains relevant today, as data input into an analysis has to be accurate or else it has no value and preconceived ideas of the outcome leads to bias.One of Wilde's significant medical contributions was the introduction of meticulous documentation involving the demographics, diagnosis, treatment of patients and, most important, outcomes. In his publications Wilde continued to emphasize the necessity for documentation as well as long-term follow up. In Dublin Journal of Medical Science, which enhanced both Dublin's medical reputation and promoted Wilde's career. In the journal, Wilde reported on progress in ophthalmology, citing articles published in the previous 12 months from both home and around the world, based on their scientific qualities.In 1846, Wilde was appointed editor of the Wilde's Worlds (2016), the historian James McGeachie states that the medicine practiced by Wilde and his colleagues was not by today's standards truly scientific. However, the 1830s were the turning point in the importance of the microscope in studying organs and tissues of the body. Microscopy was being developed particularly in Europe and many students from Britain travelled to Europe, notably to Paris, to acquire expertise in this new technology. In Edinburgh in the 1830′s Allen Thompson (1809–1884) was probably the first person to introduce microscopy to medical student teaching and John Goodsir (1814–1867) conservator of the Museum of the Royal College of Surgeons of Edinburgh and later Professor of Anatomy at Edinburgh University, developed his theory about the nature and structure of cellular life and organization based on microscopic evaluation of tissues. Later, this new science of microscopy became widely available for the study of gross pathological features following the seminal work of Rudolf Virchow (1821–1902) in 1858. Hence, the scientific revolution in medicine was on its way and Wilde recognized the importance of understanding anatomy and pathology, particularly of the organs of hearing. He collaborated with the London otologist Joseph Toynbee (1815–1866), who had dedicated his life to the study of the anatomy and pathology of the ear, and it is apparent that Toynbee's talents as a scientist complemented the clinical expertise of Wilde. Wilde's understanding of the role of a physician are to be found in his book Practical, Observations on Aural Surgery in which he wrote “The practitioner of aural surgery ought to be a well-educated surgeon or physician who applies the well-recognized principles of medicine to the organ of hearing.” Wilde paid great attention to the classification and tabulation of diseases of the eye and ear, as well as reporting the outcomes of these conditions. Fifty years later Ernest Armory Codman (1869–1940), a surgeon in Boston, Massachusetts, USA, devoted much of his life to advocating the accountability of hospitals and physicians ‘The End Result Idea’ (1918). Controversial at that time, the concepts of these two pioneers have become the standard practice for hospitals and physicians and surgeons today. Wilde also made substantial contributions to advances in surgical practice by inventing instruments for use in otology and describing a post-auricular incision for draining periosteal mastoid abscesses, Wilde's Incision. Further evidence of his progressive scientific views was his early adaption of chloroform anaesthesia and the use of atropine eyedrops in the management of inflammatory diseases of the eye. In 1850 he championed the empirical use of cod liver oil, a dietary supplement rich in vitamins A and D, in the management of xerophthalmia, a condition characterized by progressive dryness of the eye, which can lead to blindness and is now known to be due to vitamin A deficiency.In Wilde's scientific approach to the practice of surgery could however be seen to be at odds with his strongly held beliefs in mythology and folklore. It would appear though that he was at ease with these disparate topics, dealing with these conflicting positions entirely separately. Wilde's medical qualifications, statistical knowledge, familiarity with the Irish language and folklore along with publications on social issues all made him an ideal choice. As a young doctor, Wilde also welcomed the added income. He was first appointed to the Commission as a medical advisor and compiler of the causes of death from the 1841 Census. The Commissioner at that time, Sir Thomas Lorcan (1801–1879), was impressed by Wilde's work and appointed him Assistant Commissioner for the 1851 Census, a position he held for the rest of his life.The historian Peter Froggatt asserted that the appointment of Wilde to the Irish Census Board was inspirational. In the report, Wilde also recorded the incidence of blindness on 31 March 1851 in both adults and children as one person in 864, later using this information to draw the attention of the authorities to provide more facilities for the care of blind patients. Also, at Wilde's insistence the 1851 Census included the inhabitants of both the workhouses and hospitals who had been ignored in previous censuses.Wilde approached the Herculean task of conducting the 1851 Census, which covered the years of the Great Famine, with his usual vigor, presenting a detailed completed report in 1856. This is the only official audit of the Great Famine carried out by the British State and Froggatt wrote that it was one of the greatest national Censuses ever produced, including details of the incidence of impaired hearing and sight as well as mental disabilities, which was unique at the time.Dublin Quarterly Journal, he circulated questionnaires to local doctors to improve the accuracy of the data.However, despite Wilde's best organization and analytical endeavors, the Census Reports were flawed. They relied primarily on the honesty and memory of an exhausted and sometimes hostile population stretching back over 10 years during which time many families had died or emigrated. Wilde appears to have been aware of these shortcomings, now known as bias, but was not deterred by them. He used his rural background knowledge to interpret and enhance the value of the information he collected. Also, as editor of the  Later though in 2002, the economic historians Joel Mokyr and Cormac O’Grada applied mathematical modeling to Wilde's data and concluded that the Census reflected the conditions of that time. In Power and Popular Culture (2010), Peter Gray discussed the accuracy of Wilde's figures and noted that, before his publication of the tables of deaths in the General Census Report in 1856, there was wide variation in the mortality reported by the press. The Times of London, for example, estimated that there were about 20,000 deaths from the famine, while the nationalist Irish Freeman's Journal put the figure at 4.75 million. Wilde in the final Census reported a decline in the general population of 2.5 million with deaths accounting for one million, and emigration accounting for much of the remainder. These estimates were accepted later by both The Times and the Freeman's Journal. The latter publication admired Wilde's work but was highly critical of his failure to lay blame for the catastrophe on the British Government. Indeed, at that time the liberal Irish press, notably The Nation, championed the writings of the radical John Mitchel (1815–1875) who wrote “The Almighty, indeed, sent the potato blight, but the English created the Famine”. Wilde though in his Census Report, had defended the policies of both the Dublin and the London Governments and, in The Food of the Irish published in 1854, stated that the Famine had cleared the way for the modernization of Irish society and its diet, especially the potato that supported it. Wilde concluded that the substantial reduction in the population produced by the famine led to improvement in agriculture, living conditions and literacy of the Irish poor. He strongly criticized the dependency of the Irish peasantry on the potato because when it was plentiful it made them lazy and nonproductive, which fuelled agitation. Wilde asked, “Have we lost a man too many? The best friends of Ireland and her people would say not,” revealing a remarkable lack of empathy toward the Irish peasantry. This paradox in Wilde's personality is perpetuated throughout his life.However, William MacArthur, the historian, in 1956 commented that many historians placed little credence on the usefulness of Wilde's tables.In the 1850s and early 1860s, William Wilde was at the zenith of his fame. He was an internationally recognized doctor and had written successful books on surgery, antiquities, and folklore. Wilde was also the author of a National Census Report unequalled internationally, and he was awarded a knighthood of the Order of St Patrick for this work in 1864. Wilde himself concludes that all this work had been done at a risk to his life. and Froggatt posits that it was the beginning of his mental and physical decline.In March 1857 he undertook the massive project of cataloguing, describing and illustrating some 10,000 antiquities in the museum of the Royal Irish Academy. He successfully completed this task in four months, which a committee of experts had failed to accomplish in four years. The publication of this catalogue for which he received the Academy's highest award, the Cunningham Medal, marked the end of the greatest productive period of Wilde's life.Despite this success Wilde displayed a somewhat reckless side to his character. He had several illegitimate children, and it was common knowledge that he was a philanderer. Though The Lancet in London, Saunders's Newsletter in Dublin, and most of the medical fraternity, supported him, after the court case Wilson notes that Wilde lost his enthusiasm for surgery and that his clinical practice suffered irrevocably following the trial. Following the court case Wilde departed Dublin for Moytura near the village of Cong in County Mayo, leaving his practice in the care of Henry Wilson who was widely thought to be Wilde's illegitimate son.Furthermore, in the mid-1864 his career suffered a severe blow which could be attributed to irresponsible behaviour. He had developed an unprofessional relationship with a young female patient named Mary Travers who became so demanding that Wilde tried to end the relationship. Determined to ruin Wilde, Mary Travers circulated leaflets suggesting he had raped her while she was under the influence of chloroform. Lady Jane Wilde (1821–1986), William's wife and writer, known as Speranza, was incensed by these accusations and wrote to Mary's father protesting at these allegations. Mary, though, read the letter and started proceedings to sue Lady Jane for libel, thereby implicating William Wilde. The court found in favour of Mary Travers and ruled that Wilde was not without fault, but only awarded damages of one farthing. However, Wilde was left with legal fees of £2000 on behalf of Lady Jane.The Parents of Oscar Wilde (1967) Terence de Vere White wrote that even before the trial Wilde “had taken to escaping to Cong whenever he could”, indicating that his medical interests were already waning. Richard Ellman, Oscar Wilde's biographer, suggests that Wilde showed his indifference to the outcome of the lawsuit “by writing his most cheerful book Lough Corrib (1867).”In  and then three years later his two illegitimate daughters died in a fire. Wilde had been suffering from asthma and bronchitis for many years and his general health started to deteriorate significantly in late 1875, although Froggatt suggested that his decline dated back to the mid-1850s. Wilde died on 19 April 1876 aged 61 years.Unfortunately, Wilde suffered further anguish when in 1867 his daughter Isola, died in her tenth year, most likely from meningoencephalitis, a fatal brain complication of typhus, This characteristic was highlighted in an obituary in the Journal of the Archeological Society in October 1876 which reportedDoctor mor  would certainly restore the blessed light of heaven to blind-struck eyes.By the peasantry he was particularly loved and trusted, for he had brought back joy and hope to many households. How gratefully they remembered his professional skill, always so generously given, and how, in the remote country districts, he would often cross moor and mountain at the summons of some poor sufferer, who believed with simple faith that the The literature is replete with reports of Wilde the humanitarian, an example of which is when as a medical student he travelled to Connaught and nursed a terminally ill man suffering from cholera, whom he personally arranged to be buried.Quarterly Journal, he highlighted the appallingly high mortality among dispensary doctors, who were contracted to give medical care to the poor, by publishing an article by the surgeon James Cusack (1778–1861) and the physician William Stokes. The article used figures derived from Wilde's Tables of Deaths in the 1841 Census and these data were presented to a Select Committee of the House of Commons. Wilde also published a letter by Robert Graves, which severely criticized the Board of Health for its treatment of dispensary doctors who at that time were dying at an alarming rate. Wilde, Cusack, and Stokes established a medical relief committee which distributed money to the families of those doctors who had experienced hardship. Moreover, he demonstrated his generosity by opening St Mark's Hospital largely for poor patients and in 1862 he bequeathed his hospital to the city, for the use of the poor of Ireland, probably the greatest example of his philanthropy.Wilde also showed real concern for the health of his medical colleagues, especially those working in the Poor Law Service. As editor of the William Robert Wilde's life included many major achievements, some of which were influenced by his background and early education. His considerable accomplishments though could not have occurred without an insatiable appetite for knowledge in many diverse spheres of life, combined with an enthusiasm and determination to make advances in the fields of otology and ophthalmology. His insistence on the accurate documentation of clinical data, the use of statistical methods, and the early introduction of the concept of evidence-based medicine, shows him to have been much ahead of his time. Some of Wilde's observations were prescient, including the infectious nature of puerperal fever and the treatment of xerophthalmia. These contributions to medicine rank him as an original physician scientist.Wilde also demonstrated a keen sense of scholarship from his earliest days, not only in medicine but also in such divergent disciplines as archaeology and mythology. His accomplishments as a Census Commissioner and his classification of the antiquities in the Royal Irish Academy show the breadth of his interests and intellect. His reliance on common sense and proven practices in running his hospital contrasts with his beliefs in mythological events such as the Battle of Moytura in 3303 BC. These apparent paradoxes may be explained by understanding that Wilde's primary vocation was as a scientific physician while his genuine interest in Celticism and folklore was romantic and can best be described as an avocation.By contrast, it has to be acknowledged however that Wilde's life was full of conflicts and contradictions. On the one hand in his professional career, he was a strong advocate of evidence-based medicine, on the other, in his writing on mythology, he recommended folk remedies.The Irish Crisis 1848’ which attributed the catastrophic effects of the famine to Divine intervention. However, McGeachie notes that while writing the 1851 Census report,Wilde's authorial voice moved from being a government-appointed exponent of one the key legacies in Ireland of the Enlightenment State – the tabulating of public health – to that of the narrator of a tragedy, the massive human, social and cultural catastrophe of the famine.However, the fact that he largely absolved the British Government of blame is difficult to understand, coming from a man with firsthand experience of British oppression of the Irish peasants in his youth. Possible explanations for this dichotomy are that he was acting as an insensitive civil servant, unwilling to bite the hand that fed him, an explanation that would suggest a certain weakness of character. This fault may also have accounted for the fact that he did not defend himself in the Mary Travers case, and also that later he avoided discussion with his family about his precarious financial status, which condemned his widow to a life of genteel poverty. Another possible explanation was that although he genuinely believed what he wrote, he modified his beliefs over the passage of time. In the last speech of his life, delivered in 1874 to the Anthropological Section of the Royal Academy in Belfast, Wilde stated bluntly that Ireland was not defeated by Henry II but by the Great Famine which was made worse by British Government policy.It would also appear that his reports on the Great Famine were surrounded by an internal conflict. For example, in the 1851 Census Wilde defended the Whig Government's management of the disaster, even lending support to Charles Trevelyan's (1807–1886) ‘Wilde's conventional public lifestyle contrasts with his private behaviour such as demonstrated by his illicit affairs. To explain his complexities and eccentric conduct, it has been suggested that Wilde had a form of Asperger's Syndrome, a neurodevelopmental disorder characterized by difficulties in social interaction and nonverbal communication, believed to be common in Irish artists of his time. although the evidence might suggest otherwise. It would appear that his interest in surgery diminished, possibly as early as 1862 when he handed over the management of St Mark's to a board of trustees and his son, Henry Wilson, alleged to be Oscar's half-brother, took over a large part of Wilde's ophthalmological practice. Froggatt argued coherently though that Wilde's decline probably began even earlier and could be traced to the physical and psychological exhaustion he suffered after completing the classification of antiquities for the Royal Irish Society in 1857. It is conceivable that after more than 20 years of stressful medical practice and running a hospital Wilde suffered from a condition now known as “medical burn out.” Nonetheless, Wilde continued with his non-medical interests, even writing his Lough Corrib. Its Shores and Islands, thought by many to be his finest work, suggesting that burn out only affected his medical practice.Wilson states that the loss of his court case in 1864 was a severe blow from which Wilde's medical career never recoveredWhat is also fascinating is that, in addition to his liberal humanitarian qualities, Wilde was an outspoken advocate for racial equality and supported the principles of crossbreeding. This was at a time when many Europeans were promoting racial purity and white superiority.In conclusion, we would argue that William Wilde was a thoughtful, kind, humane doctor although his interpretation of the effects of the Irish Famine being of benefit to the Irish people would not sit comfortably with modern opinions. He was a true medical scientist who could empathize with both the medical elite in Dublin's Merrion Square and with the peasants in Moytura.Let us then remember William Wilde: certainly not just as the father of Oscar, but as a great humanitarian, a great doctor, a unique statistician, an investigator in many fields, and also as one who loved to fish in Connemara, to study his country's archaeology, but who loved perhaps best of all the company of his friends.In reality Wilde was an enigma. He promoted his wide-ranging ideas in articles and books, primarily to make advances in his specialty. He understood statistics and the need for outcome data to be recorded in order to improve the management of patients. He delighted in the study of architecture as well as cataloguing the antiquities. However, he lived an alternative life embedded in mysticism and romanticism and was reckless with his social life and finances. We would suggest however, that without such innovators and Enlightenment figures as Wilde, who are in turn extremely individualistic, single-mindedly pursuing their ideals, the world would be a far poorer place.Perhaps his life is best summed up by LB Somerville-Large (1901–1966) who wrote.