Key result
Novel ballistocardiogram feature fusion detects atrial fibrillation with ~96% accuracy.
Why the study?
Atrial fibrillation is the most common clinically significant arrhythmia, making improved detection performance using ballistocardiogram signals crucial.
Does the CTW-RQA feature extraction method improve the accuracy of atrial fibrillation detection using ballistocardiogram signals compared to classical features?
Cross-Sectional (n=59)
Does the CTW-RQA feature extraction method improve the accuracy of atrial fibrillation detection using ballistocardiogram signals compared to classical features?
A novel CTW-RQA feature extraction method for ballistocardiogram signals effectively supplements existing features, achieving high accuracy for unobtrusive atrial fibrillation detection.
May support unobtrusive AF detection via ballistocardiogram; leaves open prospective validation before clinical adoption.
Atrial fibrillation (AF) is the most common clinically significant arrhythmia; therefore, AF detection is crucial. Here, we propose a novel feature extraction method to improve AF detection performance using a ballistocardiogram (BCG), which is a weak vibration signal on the body surface transmitted by the cardiogenic force. In this paper, continuous time windows (CTWs) are added to each BCG segment and recurrence quantification analysis (RQA) features are extracted from each time window. Then, the number of CTWs is discussed and the combined features from multiple time windows are ranked, which finally constitute the CTW-RQA features. As validation, the CTW-RQA features are extracted from 4000 BCG segments of 59 subjects, which are compared with classical time and time-frequency features and up-to-date energy features. The accuracy of the proposed feature is superior, and three types of features are fused to obtain the highest accuracy of 95.63%. To evaluate the importance of the proposed feature, the fusion features are ranked using a chi-square test. CTW-RQA features account for 60% of the first 10 fusion features and 65% of the first 17 fusion features. It follows that the proposed CTW-RQA features effectively supplement the existing BCG features for AF detection.
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Cheng et al. (2022) conducted a cross-sectional in Atrial Fibrillation (n=59). Continuous time window recurrence quantification analysis (CTW-RQA) fusion features vs. Classical time, time-frequency, and energy features was evaluated on Atrial fibrillation detection accuracy (95.63% accuracy). The fusion of continuous time window recurrence quantification analysis (CTW-RQA) features with existing ballistocardiogram features achieved an atrial fibrillation detection accuracy of 95.63%.
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