Key result
A feature extraction method using ballistocardiogram signals and a support vector machine classifier detected atrial fibrillation with a sensitivity of 0.968, precision of 0.928, and accuracy of 0.945.
Why the study?
AF is the most common arrhythmia, causing fatal diseases and impairing quality of life, making diagnostic detection methods clinically important.
Can a feature extraction method using ballistocardiogram (BCG) signals accurately detect atrial fibrillation?
Can a feature extraction method using ballistocardiogram (BCG) signals accurately detect atrial fibrillation?
Answers are grounded in this paper and cite their sources.
A novel feature extraction method using non-contact ballistocardiogram signals and machine learning can accurately detect atrial fibrillation during sleep.
Atrial fibrillation (AF) is the most frequently occurring form of arrhythmia, which induces multiple fatal diseases and impairs the quality of life in patients; thus, the study of the diagnostic methods for detecting AF is clinically important. Here, we present a feature extraction method for the detection of AF using a ballistocardiogram (BCG), which is based on a physiological signal database collected by a non-contact sensor. The BCG signals, including both with AF and sinus rhythm (SR), were collected from 37 subjects during overnight sleep (approximately 8 h). The signals were split into 2915 1-min segments (AF: 1494, SR: 1421) without overlap and labeled as AF and SR. BCG signals were transformed into BCG energy signals in order to highlight the features of AF and SR BCG signals; and four new data sequences representing different characteristics of the BCG energy signals were generated. The mean value, variance, skewness, and kurtosis of the four data sequences were calculated and 16 features were extracted for each segment. Five machine learning algorithms were used for classification. The results of this study show that the support vector machine performed the best among the five tested classifiers and achieved sensitivity, precision, and accuracy of 0.968, 0.928, and 0.945, respectively. These results indicate that the proposed feature extraction method can be well applied to AF and SR classification and may lay foundations for the development of systems for long-term home cardiac monitoring and AF screening.
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Wen et al. (2019) studied Atrial fibrillation (n=37). Feature extraction method using ballistocardiogram (BCG) and machine learning vs. Sinus rhythm was evaluated on Classification of atrial fibrillation versus sinus rhythm. A feature extraction method using ballistocardiogram signals and a support vector machine classifier detected atrial fibrillation with a sensitivity of 0.968, precision of 0.928, and accuracy of 0.945.
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