A Least Square Support Vector Machine classifier reliably detected atrial fibrillation in short smartphone ECG recordings, achieving a Global F1 score of 0.83 (Physionet) and 0.98 (MIT-BH).
Does a novel machine learning algorithm accurately detect atrial fibrillation and other arrhythmias in smartphone ECG recordings?
A novel machine learning algorithm using a Least Square Support Vector Machine classifier can reliably detect atrial fibrillation and other arrhythmias from short smartphone ECG recordings.
Effect estimate: Global F1 = 0.83 (Physionet) and 0.98 (MIT-BH)
Atrial fibrillation (AF) is the most common cardiac disease and is associated with other cardiac complications. Few attempts have been made for discriminating AF from other arrhythmias and noise. The aim of this study is to present a novel approach for such a classification in short ECG recordings acquired using a smartphone device. The implemented algorithm was tested on the Physionet Computing in Cardiology Challenge 2017 Database and, for the purpose of comparison, on the MIT-BH AF database. After feature extraction, the stepwise linear discriminant analysis for feature selection was used. The Least Square Support Vector Machine classifier was trained and cross-validated on the available dataset of the Challenge 2017. The best performance was obtained with a total of 30 features. The algorithm produced the following performance: F1 Normal rhythm = 0.92; F1 AF rhythm: 0.82; F1 Other rhythm = 0.75; Global F1 = 0.83, obtaining the third best result in the follow-up phase of the Physionet Challenge. On the MIT-BH ADF database the algorithm gave the following performance: F1 Normal rhythm = 0.98; F1 AF rhythm: 0.99; Global F1 = 0.98. Since the algorithm reliably detect AF and other rhythms in smartphone ECG recordings, it could be applied for personal health monitoring systems.
Billeci et al. (Sun,) conducted a other in Atrial fibrillation and other arrhythmias. Least Square Support Vector Machine classifier algorithm was evaluated on Algorithm performance (F1 score) for detecting AF, normal rhythm, and other rhythms (Global F1 = 0.83 (Physionet) and 0.98 (MIT-BH)). A Least Square Support Vector Machine classifier reliably detected atrial fibrillation in short smartphone ECG recordings, achieving a Global F1 score of 0.83 (Physionet) and 0.98 (MIT-BH).