Heartbeat Detection and Rate Estimation from Ballistocardiograms using the Gated Recurrent Unit Network

Hai, Dong; Chen, Chao; Yi, Ruhan; Gou, Shuiping; Su, Bo; Jiao, Changzhe; Skubic, Marjorie

doi:10.1109/embc44109.2020.9176726

Cited by 9 publications

(11 citation statements)

References 15 publications

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“…Therefore, this paper proposes to take different time-scale segmentations of BCG signals as the input to enjoy the complementary advantages of both fine-grained morphological features and rhythm features. This is different from the previous DL-aided studies [ 20 , 22 ]. To be specific, we divide the BCG after preprocessing into two different time segments, as shown in Figure 5 .…”

Section: Methodscontrasting

confidence: 98%

“…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 (a relatively small time scale), can improve the ability of feature extraction, which is a benefit to heartbeat detection. Therefore, this paper proposes to take different time-scale segmentations of BCG signals as the input to enjoy the complementary advantages of both fine-grained morphological features and rhythm features.…”

Section: Methodsmentioning

confidence: 99%

“…To elaborate, during the interval interfered by respiratory effort and artifact motion, modeling of BCG is challenging since the shape of each BCG signal over an epoch is nonrobust. Moreover, the J-peak detection is based on a sliding window of a fixed time scale, ranging from 0.5 s to 1.5 s [ 19 , 20 ]. That means, heartbeat detection is infeasible in cases of tachycardia (i.e., heart rate >100 beats/s) or bradycardia (i.e., heart rate <40 beats/s).…”

Section: Introductionmentioning

confidence: 99%

“…In [ 23 ], Jiao et al proposed a BCG detection algorithm based on multi-instance and dictionary learning, where the feature dimensions were firstly reduced by dictionary learning, and semisupervised learning method, i.e., multi-instance learning, was then used for classification. To the latest contribution, Hai et al proposed to use the GRU neural network for BCG detection [ 20 ]. Compared with the conventional rule-based detection [ 13 – 19 ], DL-based methods can address the limitation of a fixed-scale decision.…”

Section: Introductionmentioning

confidence: 99%

“…Intuitively, the detection of the J-peak depends not only on the details of the current BCG signal but also relies on the priori information of temporal rhythm across adjacent intervals in a large time scale. However, such rhythm features over multiple BCG signals were not considered by the existing DL methods [ 20 , 22 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

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ResNet-BiLSTM: A Multiscale Deep Learning Model for Heartbeat Detection Using Ballistocardiogram Signals

Liu

Lyu

et al. 2022

Journal of Healthcare Engineering

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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 E abs and an averaged relative error E rel 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.

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Section: Methodscontrasting

confidence: 98%

Section: Methodsmentioning

confidence: 99%