Confidence analysis-based hybrid heartbeat detection for ballistocardiogram using template matching and deep learning

TL;DR

This paper introduces a hybrid heartbeat detection method for ballistocardiogram signals that combines template matching and deep learning, using confidence analysis to improve accuracy and robustness across diverse scenarios.

Contribution

The study proposes a novel hybrid detection approach that leverages confidence measures to integrate TM and DL methods, enhancing heartbeat detection performance in practical BCG monitoring.

Findings

Achieved an average absolute interval error of 20.73 ms.

Reduced detection error by 29.28 ms compared to TM alone.

Demonstrated robustness to individual differences and signal quality.

Abstract

Heartbeat interval can be detected from ballistocardiogram (BCG) signals in a non-contact manner. Conventional methods achieved heartbeat detection from different perspectives, where template matching (TM) and deep learning (DL) were based on the similarity of neighboring heartbeat episodes and robust spatio-temporal characteristics, respectively, and thus, performed varied from case to case. Inspired by the above facts, we propose confidence analysis-based hybrid heartbeat detection using both TM and DL, and further explore the advantages of both methods in various scenarios. To be specific, the confidence of the heartbeat detection results was evaluated by the consistency of signal morphology and the variability of the detected heartbeat intervals, which could be formulated by the averaged correlation between each heartbeat episode and the detected template and the normalized standard deviation among detected heartbeat intervals, respectively, where the results with higher confidence were remained. In order to validate the effectiveness of the proposed hybrid method, we conducted experiments using practical clinical BCG dataset with 34 subjects including 924,235 heartbeats. Numerical results showed that the proposed hybrid method achieved an average absolute interval error of 20.73 ms, yielding a reduction of 29.28 ms and 10.13 ms compared to solo TM and DL methods, respectively. Besides, case study showed the robustness of heartbeat detection of TM and DL to individual differences and signal quality, respectively, and in turn, validated that the hybrid method could benefit from the complementary advantages of both methods, which demonstrated the superiority of the proposed hybrid method in practical BCG monitoring scenarios.