Detection of Atrial Fibrillation Episodes in Long-Term Heart Rhythm Signals Using a Support Vector Machine

Abstract

Atrial fibrillation (AF) is a serious heart arrhythmia leading to a significant increase of the risk for occurrence of ischemic stroke. Clinically, the AF episode is recognized in an electrocardiogram. However, detection of asymptomatic AF, which requires a long-term monitoring, is more efficient when based on irregularity of beat-to-beat intervals estimated by the heart rate (HR) features. Automated classification of heartbeats into AF and non-AF by means of the Lagrangian Support Vector Machine has been proposed. The classifier input vector consisted of sixteen features, including four coefficients very sensitive to beat-to-beat heart changes, taken from the fetal heart rate analysis in perinatal medicine. Effectiveness of the proposed classifier has been verified on the MIT-BIH Atrial Fibrillation Database. Designing of the LSVM classifier using very large number of feature vectors requires extreme computational efforts. Therefore, an original approach has been proposed to determine a training set of the smallest possible size that still would guarantee a high quality of AF detection. It enables to obtain satisfactory results using only 1.39% of all heartbeats as the training data. Post-processing stage based on aggregation of classified heartbeats into AF episodes has been applied to provide more reliable information on patient risk. Results obtained during the testing phase showed the sensitivity of 98.94%, positive predictive value of 98.39%, and classification accuracy of 98.86%.

Phonocardiogram Signal Processing for Automatic Diagnosis of Congenital Heart Disorders through Fusion of Temporal and Cepstral Features

“…The SVM classifier is a widely applied method of classification for biomedical signals [ 62 , 63 , 64 , 65 ] due to its excellent generalization capability. It obtains the optimal separating hyperplane for class separation by converting input features to higher dimensions through some nonlinear mapping [ 66 ]. The distance between patterns and the hyperplane is maximized using a maximum margin principle to get the best separation.…”

Complementary Photoplethysmogram Synthesis From Electrocardiogram Using Generative Adversarial Network

“…variability of RR intervals) and/or morphological (i.e. absence or irregularity of P-wave) properties of the ECG signal [66][67][68][69]. It has been recently shown that atrial fibrillation episodes can be detected using features derived from beat-to-beat interval based on PPG signal as an alternative to existing ECG based solutions, where various features (such as the normalized root mean square of successive differences (RMSSD), sample entropy, etc.)…”

Learning Explainable Time-Morphology Patterns for Automatic Arrhythmia Classification from Short Single-Lead ECGs

“…To date, various types of morphological or statistical features manually extracted from ECG signals in the time domain, frequency domain, or nonlinear and transformed domain have generally been used in conventional machine learning classifiers for arrhythmia detection and classification [ 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 ]. Although some of these have shown acceptable performance in several studies, hand-crafted features are difficult to generalize in other situations.…”