Multiple Instance Dictionary Learning for Beat-to-Beat Heart Rate Monitoring From Ballistocardiograms

Jiao, Changzhe; Su, Bo-Yu; Lyons, Princess; Zare, Alina; Ho, K. C.; Skubic, Marjorie

doi:10.1109/tbme.2018.2812602

Cited by 28 publications

(9 citation statements)

References 56 publications

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“…In practice, r was set to 45, resulting in 91 sample long features (corresponding to 0.91 s signal, 45 samples before and after the peak). This setting was verified in our previous work [34] and was found to be the typical length of a heartbeat pattern. Figure 8 shows the three lead ECG signals from subject no.…”

Section: Data Preprocessingsupporting

confidence: 77%

Convolutional Autoencoding and Gaussian Mixture Clustering for Unsupervised Beat-to-Beat Heart Rate Estimation of Electrocardiograms from Wearable Sensors

Zhong

Hai

Cheng

et al. 2021

Sensors

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Heart rate is one of the most important diagnostic bases for cardiovascular disease. This paper introduces a deep autoencoding strategy into feature extraction of electrocardiogram (ECG) signals, and proposes a beat-to-beat heart rate estimation method based on convolution autoencoding and Gaussian mixture clustering. The high-level heartbeat features were first extracted in an unsupervised manner by training the convolutional autoencoder network, and then the adaptive Gaussian mixture clustering was applied to detect the heartbeat locations from the extracted features, and calculated the beat-to-beat heart rate. Compared with the existing heartbeat classification/detection methods, the proposed unsupervised feature learning and heartbeat clustering method does not rely on accurate labeling of each heartbeat location, which could save a lot of time and effort in human annotations. Experimental results demonstrate that the proposed method maintains better accuracy and generalization ability compared with the existing ECG heart rate estimation methods and could be a robust long-time heart rate monitoring solution for wearable ECG devices.

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Section: Data Preprocessingsupporting

confidence: 77%

Convolutional Autoencoding and Gaussian Mixture Clustering for Unsupervised Beat-to-Beat Heart Rate Estimation of Electrocardiograms from Wearable Sensors

Zhong

Hai

Cheng

et al. 2021

Sensors

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“…In [ 22 ], Zhang et al employed the convolutional neural network (CNN) combined with the extreme learning machine to detect the J-peak of the BCG signal. 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 ].…”

Section: Introductionmentioning

confidence: 99%

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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“…7 (a)). The BCG signal could also be acquired by pneumatic sensors [ 98 ], optical fibers [ 99 ], hydraulic bed sensors [ 100 ], [ 101 ] ( Fig. 7 (b)) and accelerometers [ 102 ].…”

Section: Unobtrusive Monitoring Technologymentioning

confidence: 99%

A review of wearable and unobtrusive sensing technologies for chronic disease management

Guo

Liu

Peng

et al. 2021

Computers in Biology and Medicine

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Multiple Instance Dictionary Learning for Beat-to-Beat Heart Rate Monitoring From Ballistocardiograms

Cited by 28 publications

References 56 publications

Convolutional Autoencoding and Gaussian Mixture Clustering for Unsupervised Beat-to-Beat Heart Rate Estimation of Electrocardiograms from Wearable Sensors

Convolutional Autoencoding and Gaussian Mixture Clustering for Unsupervised Beat-to-Beat Heart Rate Estimation of Electrocardiograms from Wearable Sensors

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

A review of wearable and unobtrusive sensing technologies for chronic disease management

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