Abstract
As the heartbeat detection from ballistocardiogram (BCG) signals using force sensors is interfered by respiratory effort and artifact motion, advanced signal processing algorithms are required to detect the J-peak of each BCG signal so that beat-to-beat interval can be identified. However, existing methods generally rely on rule-based detection of a fixed size, without considering the rhythm features in a large time scale covering multiple BCG signals. Methods. This paper develops a deep learning framework based on ResNet and bidirectional long short-term memory (BiLSTM) to conduct beat-to-beat detection of BCG signals. Unlike the existing methods, the proposed network takes multiscale features of BCG signals as the input and, thus, can enjoy the complementary advantages of both morphological features of one BCG signal and rhythm features of multiple BCG signals. Different time scales of multiscale features for the proposed model are validated and analyzed through experiments. Results. The BCG signals recorded from 21 healthy subjects are conducted to verify the performance of the proposed heartbeat detection scheme using leave-one-out cross-validation. The impact of different time scales on the detection performance and the performance of the proposed model for different sleep postures are examined. Numerical results demonstrate that the proposed multiscale model performs robust to sleep postures and achieves an averaged absolute error (Eabs) and an averaged relative error (Erel) of the heartbeat interval relative to the R-R interval of 9.92 ms and 2.67 ms, respectively, which are superior to those of the state-of-the-art detection protocol. Conclusion. In this work, a multiscale deep-learning model for heartbeat detection using BCG signals is designed. We demonstrate through the experiment that the detection with multiscale features of BCG signals can provide a superior performance to the existing works. Further study will examine the ultimate performance of the multiscale model in practical scenarios, i.e., detection for patients suffering from cardiovascular disorders with night-sleep monitoring.
Highlights
Background e World HealthOrganization (WHO) announced that cardiovascular disease (CVD) causes the highest mortality in the world, where approximately 17.1 million people died of CVD every year
Unlike the existing schemes [20, 22], it is noted from Figure 4(b) that the rhythm feature of the BCG signal (J-peaks and the neighboring peaks across multiple BCGs in a large time scale), in addition to the morphological features within a single BCG duration, can improve the ability of feature extraction, which is a benefit to heartbeat detection. erefore, this paper proposes to take different time-scale segmentations of BCG signals as the input to enjoy the complementary advantages of both finegrained morphological features and rhythm features. is is different from the previous deep learning (DL)-aided studies [20, 22]
For the validation of the proposed heartbeat detection scheme, we conduct an experiment using a total of 440 min BCG signals measured by 21 subjects. 426 min recorded signals are used for heartbeat detection, while the rest are motion artifacts
Summary
Background e World HealthOrganization (WHO) announced that cardiovascular disease (CVD) causes the highest mortality in the world, where approximately 17.1 million people died of CVD every year. Clinical studies have shown that continuous vital sign monitoring (including heart rate and respiratory rate) is of great significance for the early detection of CVD [1,2,3]. As the gold standard of heart rate monitoring, electrocardiogram (ECG)-based technologies have been widely used over the past several decades. In pioneer studies [4, 5], the authors developed a noninvasive BCG acquisition system by using force sensors, where heart rate was computed based on the detection of J-peaks from BCG signals. Since the acquisition of the BCG signal is Journal of Healthcare Engineering contactless with the human body, such a noninvasive heartbeat detection is promising for the application of inhome monitoring. The morphology of BCG may differ between people of different body weights, gender, and healthy status, which brings challenges to the detection
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