In this paper, we studied and validated a simple heartbeat classifier based on ECG feature models selected with the focus on an improved generalization capability. We considered features from the RR series, as well as features computed from the ECG samples and different scales of the wavelet transform, at both available leads. The classification performance and generalization were studied using publicly available databases: the MIT-BIH Arrhythmia, the MIT-BIH Supraventricular Arrhythmia, and the St. Petersburg Institute of Cardiological Technics (INCART) databases. The Association for the Advancement of Medical Instrumentation recommendations for class labeling and results presentation were followed. A floating feature selection algorithm was used to obtain the best performing and generalizing models in the training and validation sets for different search configurations. The best model found comprehends eight features, was trained in a partition of the MIT-BIH Arrhythmia, and was evaluated in a completely disjoint partition of the same database. The results obtained were: global accuracy of 93%; for normal beats, sensitivity (S) 95%, positive predictive value (P(+)) 98%; for supraventricular beats, S 77%, P(+) 39%; and for ventricular beats S 81%, P(+) 87%. In order to test the generalization capability, performance was also evaluated in the INCART, with results comparable to those obtained in the test set. This classifier model has fewer features and performs better than other state-of-the-art methods with results suggesting better generalization capability.

Visible T wave alternans (TWA) in the electrocardiogram (ECG) had been regarded as an infrequent phenomenon during the first 80 years of electrocardiography. Nevertheless, computerized analysis changed this perception. In the last two decades, a variety of techniques for automatic TWA analysis have been proposed. These techniques have allowed researchers to detect nonvisible TWA in a wide variety of clinical and experimental conditions. Such studies have recently shown that TWA is related to cardiac instability and increased arrhythmogenicity. Comparison of TWA analysis methods is a difficult task due to the diversity of approaches. In this paper, we propose a unified framework which holds the existing methods. In the light of this framework, the methodological principles of the published TWA analysis schemes are compared and discussed. This framework may have an important role to develop new approaches to this problem.

Highlights► Spike sorting algorithms are limited in the number of single units they can detect. ► The maximum number of correctly identified neurons stands between 8 and 10. ► Sparse neurons are strongly affected by this limitation. ► Further development of algorithms is needed to address sparse neurons detection.

This study tackles the ECG classification problem by means of a methodology, which is able to enhance classification performance while simultaneously reducing the computational resources, making it specially adequate for its application in the improvement of ambulatory settings. For this purpose, the sequential forward floating search (SFFS) algorithm is applied with a new criterion function index based on linear discriminants. This criterion has been devised specifically to be a quality indicator in ECG arrhythmia classification. Based on this measure, a comprehensive feature set is analyzed with the SFFS algorithm, and the most suitable subset returned is additionally evaluated with a multilayer perceptron (MLP) to assess the robustness of the model. Aiming at obtaining meaningful estimates of the real-world performance and facilitating comparison with similar studies, the present contribution follows the Association for the Advancement of Medical Instrumentation standard EC57:1998 and the same interpatient division scheme used in several previous studies. Results show that by applying the proposed methods, the performance obtained in similar studies under the same constraints can be exceeded, while keeping the requirements suitable for ambulatory monitoring

This consensus guideline was prepared on behalf of the International Society for Holter and Noninvasive Electrocardiology and is cosponsored by the Japanese Circulation Society, the Computers in Cardiology Working Group on e-Cardiology of the European Society of Cardiology, and the European Cardiac Arrhythmia Society. It discusses the electrocardiographic phenomenon of T-wave alternans (TWA) (i.e., a beat-to-beat alternation in the morphology and amplitude of the ST- segment or T-wave). This statement focuses on its physiological basis and measurement technologies and its clinical utility in stratifying risk for life-threatening ventricular arrhythmias. Signal processing techniques including the frequency-domain Spectral Method and the time-domain Modified Moving Average method have demonstrated the utility of TWA in arrhythmia risk stratification in prospective studies in >12,000 patients. The majority of exercise-based studies using both methods have reported high relative risks for cardiovascular mortality and for sudden cardiac death in patients with preserved as well as depressed left ventricular ejection fraction. Studies with ambulatory electrocardiogram-based TWA analysis with Modified Moving Average method have yielded significant predictive capacity. However, negative studies with the Spectral Method have also appeared, including 2 interventional studies in patients with implantable defibrillators. Meta-analyses have been performed to gain insights into this issue. Frontiers of TWA research include use in arrhythmia risk stratification of individuals with preserved ejection fraction, improvements in predictivity with quantitative analysis, and utility in guiding medical as well as device-based therapy. Overall, although TWA appears to be a useful marker of risk for arrhythmic and cardiovascular death, there is as yet no definitive evidence that it can guide therapy.

Widely developed for clinical screening, electrocardiogram (ECG) recordings capture the cardiac electrical activity from the body surface. ECG analysis can therefore be a crucial first step to help diagnose, understand and predict cardiovascular disorders responsible for 30% of deaths worldwide. Computational techniques, and more specifically machine learning techniques and computational modelling are powerful tools for classification, clustering and simulation, and they have recently been applied to address the analysis of medical data, especially ECG data. This review describes the computational methods in use for ECG analysis, with a focus on machine learning and 3D computer simulations, as well as their accuracy, clinical implications and contributions to medical advances. The first section focuses on heartbeat classification and the techniques developed to extract and classify abnormal from regular beats. The second section focuses on patient diagnosis from whole recordings, applied to different diseases. The third section presents real-time diagnosis and applications to wearable devices. The fourth section highlights the recent field of personalized ECG computer simulations and their interpretation. Finally, the discussion section outlines the challenges of ECG analysis and provides a critical assessment of the methods presented. The computational methods reported in this review are a strong asset for medical discoveries and their translation to the clinical world may lead to promising advances.

In this paper, we present a patient-adaptable algorithm for ECG heartbeat classification, based on a previously developed automatic classifier and a clustering algorithm. Both classifier and clustering algorithms include features from the RR interval series and morphology descriptors calculated from the wavelet transform. Integrating the decisions of both classifiers, the presented algorithm can work either automatically or with several degrees of assistance. The algorithm was comprehensively evaluated in several ECG databases for comparison purposes. Even in the fully automatic mode, the algorithm slightly improved the performance figures of the original automatic classifier; just with less than two manually annotated heartbeats (MAHB) per recording, the algorithm obtained a mean improvement for all databases of 6.9% in accuracy A, of 6.5% in global sensitivity S and of 8.9% in global positive predictive value P(+). An assistance of just 12 MAHB per recording resulted in a mean improvement of 13.1% in A, of 13.9% in S, and of 36.1% in P(+). For the assisted mode, the algorithm outperformed other state-of-the-art classifiers with less expert annotation effort. The results presented in this paper represent an improvement in the field of automatic and patient-adaptable heartbeats classification, concluding that the performance of an automatic classifier can be improved with an efficient handling of the expert assistance.

No abstract