Research on Heartbeat Detection Method of Ballistocardiogram Based on Bidirectional Long Short-term Memory Network

Abstract

In order to improve the accuracy and generalization ability of extracting successive heartbeat cycle based on ballistocardiogram (BCG), this paper proposed a general method for detecting J peak of BCG signals by using bidirectional long short-term memory network. First, the clustering method is used to establish the sequence feature set of BCG signals in different sleeping positions, and the data set used contains a variety of different forms of BCG signals. Then, according to the Bidirectional LSTM (BiLSTM) many-to-many recognition model, the number of J peaks in the output sequence is counted to achieve real-time heartbeat detection. The results showed that the deviation rate of BCG heart rate detection was 0.27%, and there was no significant difference between BCG and ECG in the detection of heartbeat interval. Compared with other methods, this method has higher robustness and accuracy in detection effect, which provides a new idea for realizing high-precision unconstrained heartbeat detection.

Real-time heart rate estimation using PVDF flexible piezoelectric sensor based on machine learning

“…Since there are some differences in pulse signal patterns in different individuals or in different states of individuals [15], a large number of pulse signals in different states was acquired by proposed pulse signal acquisition system to construct the data set in the experiments. The K-means clustering algorithm was employed to locate the peaks in the collected pulse…”