Обзор методов автоматической диагностики сердечной аритмии для принятия решений о необходимости проведения дефибрилляции

Ventricular fibrillation is considered the most common cause of sudden cardiac arrest. The fibrillation, and ventricular tachycardia often preceding it, are cardiac rhythms that may respond to emergency electroshock therapy and return to normal sinus rhythm when diagnosed early after cardiac arrest with the restoration of adequate cardiac pumping function. However, manually checking ECG signals on the existence of a pattern of such arrhythmias is a risky and time-consuming task in stressful situations and practically impossible in the absence of a qualified medical specialist. Therefore, systems of the computer classification of arrhythmias with the function of making a decision on the necessity of electric cardioversion with the parameters of a high-voltage pulse calculated adaptively for each patient are widely used for the automatic diagnosis of such conditions. This paper discusses methods of analyzing the electrocardiographic signal taken from the electrodes of an external automatic or semi-automatic defibrillator in order to make a decision on the necessity for defibrillation, which are applicable in the embedded software of automatic and semiautomatic external defibrillators. The paper includes an overview of applicable filtering techniques as well as subsequent algorithms for extracting, classifying and compressing features for the ECG signal.  Lipchak D. A., Chupov A. A. Methods of Signal Analysis for Automatic Diagnosis of Shockable Cardiac Arrhythmias: A Review. Ural Radio Engineering Journal. 2021;5(4):380–409. (In Russ.) DOI: 10.15826/ urej.2021.5.4.004. Фибрилляция желудочков сердца считается наиболее часто встречающейся причиной внезапной остановки сердца. Такая фибрилляция и часто предшествующая ей желудочковая тахикардия – это ритмы сердца, которые могут реагировать на экстренную электрошоковую терапию и вернуться к нормальному синусовому ритму при ранней диагностике после остановки сердца с восстановлением адекватной насосной функции сердца. Однако ручная проверка сигналов ЭКГ на наличие паттерна такой аритмии является сложной аналитической задачей, требующей немедленного принятия решения в стрессовой ситуации, практически невыполнимой в отсутствие квалифицированного медицинского специалиста. Поэтому для автоматической диагностики острых состояний широкое применение получили системы компьютерной классификации аритмий с функцией принятия решения о необходимости проведения электрокардиотерапии с параметрами высоковольтного импульса, вычисленного адаптивно для каждого пациента. В данной работе рассмотрены методы анализа электрокардиографического сигнала, снимаемого с электродов наружного автоматического или полуавтоматического дефибриллятора, с целью принятия решения о необходимости оказания дефибрилляции, применимые во встроенном программном обеспечении автоматических и полуавтоматических внешних дефибрилляторов. Работа включает обзор применимых методов фильтрации, а также последующих алгоритмов извлечения, классификации и сжатия характерных признаков для сигнала ЭКГ.  Липчак Д. А., Чупов А. А. Обзор методов автоматической диагностики сердечной аритмии для принятия решений о необходимости проведения дефибрилляции. Ural Radio Engineering Journal. 2021;5(4):380–409. DOI: 10.15826/urej.2021.5.4.004.

Mixed convolutional and long short-term memory network for the detection of lethal ventricular arrhythmia

Early defibrillation by an automated external defibrillator (AED) is key for the survival of out-of-hospital cardiac arrest (OHCA) patients. ECG feature extraction and machine learning have been successfully used to detect ventricular fibrillation (VF) in AED shock decision algorithms. Recently, deep learning architectures based on 1D Convolutional Neural Networks (CNN) have been proposed for this task. This study introduces a deep learning architecture based on 1D-CNN layers and a Long Short-Term Memory (LSTM) network for the detection of VF. Two datasets were used, one from public repositories of Holter recordings captured at the onset of the arrhythmia, and a second from OHCA patients obtained minutes after the onset of the arrest. Data was partitioned patient-wise into training (80%) to design the classifiers, and test (20%) to report the results. The proposed architecture was compared to 1D-CNN only deep learners, and to a classical approach based on VF-detection features and a support vector machine (SVM) classifier. The algorithms were evaluated in terms of balanced accuracy (BAC), the unweighted mean of the sensitivity (Se) and specificity (Sp). The BAC, Se, and Sp of the architecture for 4-s ECG segments was 99.3%, 99.7%, and 98.9% for the public data, and 98.0%, 99.2%, and 96.7% for OHCA data. The proposed architecture outperformed all other classifiers by at least 0.3-points in BAC in the public data, and by 2.2-points in the OHCA data. The architecture met the 95% Sp and 90% Se requirements of the American Heart Association in both datasets for segment lengths as short as 3-s. This is, to the best of our knowledge, the most accurate VF detection algorithm to date, especially on OHCA data, and it would enable an accurate shock no shock diagnosis in a very short time.This study was supported by the Ministerio de Economía, Industria y Competitividad, Gobierno de España (ES) (TEC-2015-64678-R) to UI and EA and by Euskal Herriko Unibertsitatea (ES) (GIU17/031) to UI and EA. The funders, Tecnalia Research and Innovation and Banco Bilbao Vizcaya Argentaria (BBVA), provided support in the form of salaries for authors AP, AA, FAA, CF, EG, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the author contributions section

Methods of Signal Analysis for Automatic Diagnosis of Shockable Cardiac Arrhythmias: A Review

Ventricular fibrillation is considered the most common cause of sudden cardiac arrest. Ventricular fibrillation, and ventricular tachycardia often preceding it, are cardiac rhythms that can respond to emergency electroshock therapy and return to normal sinus rhythm when diagnosed early after cardiac arrest with the restoration of adequate cardiac pumping function. However, manually checking ECG signals for the presence of a pattern of such arrhythmias is a risky and time- consuming task in stressful situations and practically impossible in the absence of a qualified medical specialist. Therefore, for the automatic diagnosis of such conditions, systems for the computer classification of arrhythmias to decide on the need for electric cardioversion with the parameters of a high-voltage pulse, calculated adaptively for each patient, are widely used. This paper discusses methods for analyzing the electrocardiographic signal taken from external automatic or semi-automatic defibrillator electrodes to decide the need for defibrillation, which is applicable in the embedded software of automatic, semi-automatic external defibrillators. The paper includes an overview of applicable filtering techniques and subsequent algorithms for extracting, classifying, and compressing features for the ECG signal. Both advantages and disadvantages are discussed for the studied algorithms. © 2022 IEEE.Russian Foundation for Basic Research, РФФИ, (20-37-90037)The reported study is funded by RFBR according to research project No. 20-37-90037

Methods of Signal Analysis for Automatic Diagnosis of Shockable Cardiac Arrhythmias: A Review

Поступила: 19.12.2021. Принята в печать: 18.01.2022.Received: 19.12.2021. Accepted: 18.01.2022.Фибрилляция желудочков сердца считается наиболее часто встречающейся причиной внезапной остановки сердца. Такая фибрилляция и часто предшествующая ей желудочковая тахикардия – это ритмы сердца, которые могут реагировать на экстренную электрошоковую терапию и вернуться к нормальному синусовому ритму при ранней диагностике после остановки сердца с восстановлением адекватной насосной функции сердца. Однако ручная проверка сигналов ЭКГ на наличие паттерна такой аритмии является сложной аналитической задачей, требующей немедленного принятия решения в стрессовой ситуации, практически невыполнимой в отсутствие квалифицированного медицинского специалиста. Поэтому для автоматической диагностики острых состояний широкое применение получили системы компьютерной классификации аритмий с функцией принятия решения о необходимости проведения электрокардиотерапии с параметрами высоковольтного импульса, вычисленного адаптивно для каждого пациента. В данной работе рассмотрены методы анализа электрокардиографического сигнала, снимаемого с электродов наружного автоматического или полуавтоматического дефибриллятора, с целью принятия решения о необходимости оказания дефибрилляции, применимые во встроенном программном обеспечении автоматических и полуавтоматических внешних дефибрилляторов. Работа включает обзор применимых методов фильтрации, а также последующих алгоритмов извлечения, классификации и сжатия характерных признаков для сигнала ЭКГ.Ventricular fibrillation is considered the most common cause of sudden cardiac arrest. The fibrillation, and ventricular tachycardia often preceding it, are cardiac rhythms that may respond to emergency electroshock therapy and return to normal sinus rhythm when diagnosed early after cardiac arrest with the restoration of adequate cardiac pumping function. However, manually checking ECG signals on the existence of a pattern of such arrhythmias is a risky and time-consuming task in stressful situations and practically impossible in the absence of a qualified medical specialist. Therefore, systems of the computer classification of arrhythmias with the function of making a decision on the necessity of electric cardioversion with the parameters of a high-voltage pulse calculated adaptively for each patient are widely used for the automatic diagnosis of such conditions. This paper discusses methods of analyzing the electrocardiographic signal taken from the electrodes of an external automatic or semi-automatic defibrillator in order to make a decision on the necessity for defibrillation, which are applicable in the embedded software of automatic and semiautomatic external defibrillators. The paper includes an overview of applicable filtering techniques as well as subsequent algorithms for extracting, classifying and compressing features for the ECG signal

Ventricular Fibrillation and Tachycardia Detection Using Features Derived from Topological Data Analysis

A rapid and accurate detection of ventricular arrhythmias is essential to take appropriate therapeutic actions when cardiac arrhythmias occur. Furthermore, the accurate discrimination between arrhythmias is also important, provided that the required shocking therapy would not be the same. In this work, the main novelty is the use of the mathematical method known as Topological Data Analysis (TDA) to generate new types of features which can contribute to the improvement of the detection and classification performance of cardiac arrhythmias such as Ventricular Fibrillation (VF) and Ventricular Tachycardia (VT). The electrocardiographic (ECG) signals used for this evaluation were obtained from the standard MIT-BIH and AHA databases. Two input data to the classify are evaluated: TDA features, and Persistence Diagram Image (PDI). Using the reduced TDA-obtained features, a high average accuracy near 99% was observed when discriminating four types of rhythms (98.68% to VF; 99.05% to VT; 98.76% to normal sinus; and 99.09% to Other rhythms) with specificity values higher than 97.16% in all cases. In addition, a higher accuracy of 99.51% was obtained when discriminating between shockable (VT/VF) and non-shockable rhythms (99.03% sensitivity and 99.67% specificity). These results show that the use of TDA-derived geometric features, combined in this case this the k-Nearest Neighbor (kNN) classifier, raises the classification performance above results in previous works. Considering that these results have been achieved without preselection of ECG episodes, it can be concluded that these features may be successfully introduced in Automated External Defibrillation (AED) and Implantable Cardioverter Defibrillation (ICD) therapie

Detection of shockable heart rhythms with convolutional neural networks : Based on ECG spectrograms

Purpose Automated feature extraction combined with deep learning has had and continues to have a strong impact on the improvement and implementation of pattern recognition driven by machine learning. Systems without prior expertise about a problem but with the ability to iteratively learn strategies to solve problems, tend to outperform concepts of manual feature engineering in vari-ous fields. In ECG data analysis as well as in other medical domains, models based on manual feature extraction are tedious to develop, require scientific expertise, and are oftentimes not easily adaptive to variations of the problem to be solved. This work aims to examine automated feature extraction and classification of ECG data, specifically of shockable heart rhythms, with convolu-tional neural networks and residual neural networks. The precise and rapid determination of shockable cardiac conditions is a decisive step to improve the chances of survival for patients having a sudden cardiac arrest. Conventional, commercially available automated external defib-rillators (AEDs) deploy algorithms based on manual feature extraction. Approximately 1 out of 10 shockable conditions is not recognized by the AED. Consequently, strategies for improvement need to be explored. Methods 125 ECG recordings from four annotated cardiac arrhythmia databases (American Heart Association Database, Creighton University Tachyarrhythmia Database, MIT-BIH Arrhythmia Da-tabase, MIT-BIH Malignant Ventricular Arrhythmia Database) with a duration of 30 mins or 8 mins (Creighton University Tachyarrhythmia Database) per recording were processed. Shockable con-ditions are identified as ventricular tachycardia, ventricular fibrillation, and ventricular flutter. The 1 channel ECG recordings (modified limb lead II) were normalized to 250 Hz sampling frequency, high-pass filtered (1 Hz cutoff and 0.85 filter steepness), second order Butterworth low-pass fil-tered (30 Hz cutoff), and notch filtered at 50 Hz. Consistent wavelet transformation with 5 octaves, 20 voices per octave, and a time bandwidth product parameter of 50 was applied to generate greyscale spectrogram representations of the ECG data (pixel value range from 0 to 255). The recordings were segmented into 3 s segments. Data augmentation around the borders of shock-able episodes and along shockable episodes was carried out to create balanced datasets con-sisting of 60340 samples. 45% of samples in the balanced dataset contain shockable rhythms with more than 60% temporal prevalence within each sample. Conventional convolutional neural networks and residual neural networks with varying architectures and hyperparameter settings were trained and evaluated on balanced datasets (train/val/test: 70/15/15). The approach focused on examining a broader range of parameter settings and model architectures rather than optimiz-ing a specific configuration. The best performing model was evaluated in a 5-fold cross-validation. Exemplarily, a leave-one-subject-out cross-validation was deployed with 3 randomly chosen re-cordings, with the constraints that each subject must come from a different database and contain a different shockable condition. Results and Conclusion The best performing model was a residual neural network with 96 residual blocks. The 5-fold cross-validation results on average in an accuracy of 0.987, a sensitivity of 0.992 on shock-able rhythms, and a specificity of 0.984 for non-shockable rhythms on the test sets. The ROC AUC score is 0.998 on average. The 3-fold leave-one-subject-out cross-validation reaches on average an accuracy of 0.984, a sensitivity of 0.984, and a specificity of 0.980. The ROC AUC score reaches 0.997 on average. The analysis of misclassified segments reveals that the classi-fier performs less accurately on border segments containing a shockable and at least one non-shockable rhythm. While the test set contains 4.73% border segments, the set of misclassified samples includes 11.29% border segments. The label distributions of the test set and the set of misclassified samples show that segments annotated as “not defined” (ND) and “ventricular fibril-lation or flutter” (VF-VFL) are significantly more prevalent in the set of misclassified samples. Histogram analysis, referring to the mean pixel intensity of the spectrograms, indicates that the classifier works less accurately on spectrograms with mean pixel values below 2 (practically flat-line signals or signals with very small amplitude). The results indicate that it is possible to improve the analysis of ECG data by deploying automated feature detection combined with artificial neural networks. The methods presented in this work are not restricted to the detection of shockable cardiac arrhythmias, they likewise em-phasize the potential of machine learning in the domain of biosignal analysis and correlated med-ical data. In the next step, the approach needs to be verified on a broader database. The tech-nology can even help create more comprehensive databases of clinical ECG data by supporting automated annotation

Machine learning techniques for arrhythmic risk stratification: a review of the literature

Ventricular arrhythmias (VAs) and sudden cardiac death (SCD) are significant adverse events that affect the morbidity and mortality of both the general population and patients with predisposing cardiovascular risk factors. Currently, conventional disease-specific scores are used for risk stratification purposes. However, these risk scores have several limitations, including variations among validation cohorts, the inclusion of a limited number of predictors while omitting important variables, as well as hidden relationships between predictors. Machine learning (ML) techniques are based on algorithms that describe intervariable relationships. Recent studies have implemented ML techniques to construct models for the prediction of fatal VAs. However, the application of ML study findings is limited by the absence of established frameworks for its implementation, in addition to clinicians’ unfamiliarity with ML techniques. This review, therefore, aims to provide an accessible and easy-to-understand summary of the existing evidence about the use of ML techniques in the prediction of VAs. Our findings suggest that ML algorithms improve arrhythmic prediction performance in different clinical settings. However, it should be emphasized that prospective studies comparing ML algorithms to conventional risk models are needed while a regulatory framework is required prior to their implementation in clinical practice

ECG waveform dataset for predicting defibrillation outcome in out-of-hospital cardiac arrested patients

The provided database of 260 ECG signals was collected from patients with out-of-hospital cardiac arrest while treated by the emergency medical services. Each ECG signal contains a 9 second waveform showing ventricular fibrillation, followed by 1 min of post-shock waveform. Patients’ ECGs are made available in multiple formats. All ECGs recorded during the prehospital treatment are provided in PFD files, after being anonymized, printed in paper, and scanned. For each ECG, the dataset also includes the whole digitized waveform (9 s pre- and 1 min post-shock each) and numerous features in temporal and frequency domain extracted from the 9 s episode immediately prior to the first defibrillation shock. Based on the shock outcome, each ECG file has been annotated by three expert cardiologists, - using majority decision -, as successful (56 cases), unsuccessful (195 cases), or indeterminable (9 cases). The code for preprocessing, for feature extraction, and for limiting the investigation to different temporal intervals before the shock is also provided. These data could be reused to design algorithms to predict shock outcome based on ventricular fibrillation analysis, with the goal to optimize the defibrillation strategy (immediate defibrillation versus cardiopulmonary resuscitation and/or drug administration) for enhancing resuscitation. © 202

Comprehensive electrocardiographic diagnosis based on deep learning

Cardiovascular disease (CVD) is the leading cause of death worldwide, and coronary artery disease (CAD) is a major contributor. Early-stage CAD can progress if undiagnosed and left untreated, leading to myocardial infarction (MI) that may induce irreversible heart muscle damage, resulting in heart chamber remodeling and eventual congestive heart failure (CHF). Electrocardiography (ECG) signals can be useful to detect established MI, and may also be helpful for early diagnosis of CAD. For the latter especially, the ECG perturbations can be subtle and potentially misclassified on manual interpretation and/or when analyzed by traditional algorithms found in ECG instrumentation. For automated diagnostic systems (ADS), deep learning techniques are favored over conventional machine learning techniques, due to the automatic feature extraction and selection processes involved. This paper highlights various deep learning algorithms exploited for the classification of ECG signals into CAD, MI, and CHF conditions. The Convolutional Neural Network (CNN), followed by combined CNN and Long Short-Term Memory (LSTM) models, appear to be the most useful architectures for classification. A 16-layer LSTM model was developed in our study and validated using 10-fold cross validation. A classification accuracy of 98.5% was achieved. Our proposed model has the potential to be a useful diagnostic tool in hospitals for the classification of abnormal ECG signals