An Unobtrusive System for Heart Rate Monitoring Based on Ballistocardiogram Using Hilbert Transform and Viterbi Decoding

Xie, Qingsong; Li, Yongfu; Wang, Guoxing; Lian, Yong

doi:10.1109/jetcas.2019.2951411

Cited by 17 publications

(12 citation statements)

References 27 publications

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“…The outcome of the Hilbert transform, i.e.,

, is a complex signal containing in its real part the copy of

and in its imaginary part a 90 deg phase shift of

itself. Assuming that the heart-beat activity (

) is hidden and only its modulation can be measured, it is possible to model the recorded signal (

) as follows [ 44 ]:

…”

Section: Discussionmentioning

confidence: 99%

“…In ( 4 ),

denotes the modulating term [ 44 ], while

denotes additive noise. Therefore, according to the effect of ( 3 ) on the input signal, it is possible to extract

as follows:

denoting

and

the real part and the imaginary part of

, respectively.…”

Section: Discussionmentioning

confidence: 99%

“…As regards HR, we considered the signals recorded by IMU. Firstly, we employed the Hilbert Transform to enhance the contribute of the heartbeat on the recorded signals, as already used in similar applications [ 43 , 44 ]. This technique allowed us to obtain the heartbeat envelope relative to the x -axis of the accelerometer (

) and the x -axis of the gyroscope (

).…”

Section: Discussionmentioning

confidence: 99%

“…In ( 4), cos(2π f 0 t) denotes the modulating term [44], while (t) denotes additive noise. Therefore, according to the effect of (3) on the input signal, it is possible to extract h(t) as follows:…”

Section: Cardiac Activity: Data Analysismentioning

confidence: 99%

See 3 more Smart Citations

A Wearable System with Embedded Conductive Textiles and an IMU for Unobtrusive Cardio-Respiratory Monitoring

Tocco

Raiano

Sabbadini

et al. 2021

Sensors

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The continuous and simultaneous monitoring of physiological parameters represents a key aspect in clinical environments, remote monitoring and occupational settings. In this regard, respiratory rate (RR) and heart rate (HR) are correlated with several physiological and pathological conditions of the patients/workers, and with environmental stressors. In this work, we present and validate a wearable device for the continuous monitoring of such parameters. The proposed system embeds four conductive sensors located on the user’s chest which allow retrieving the breathing activity through their deformation induced during cyclic expansion and contraction of the rib cage. For monitoring HR we used an embedded IMU located on the left side of the chest wall. We compared the proposed device in terms of estimating HR and RR against a reference system in three scenarios: sitting, standing and supine. The proposed system reliably estimated both RR and HR, showing low error averaged along subjects in all scenarios. This is the first study focused on the feasibility assessment of a wearable system based on a multi-sensor configuration (i.e., conductive sensors and IMU) for RR and HR monitoring. The promising results encourage the application of this approach in clinical and occupational settings.

show abstract

“…The outcome of the Hilbert transform, i.e.,

, is a complex signal containing in its real part the copy of

and in its imaginary part a 90 deg phase shift of

itself. Assuming that the heart-beat activity (

) is hidden and only its modulation can be measured, it is possible to model the recorded signal (

) as follows [ 44 ]:

…”

Section: Discussionmentioning

confidence: 99%

“…In ( 4 ),

denotes the modulating term [ 44 ], while

denotes additive noise. Therefore, according to the effect of ( 3 ) on the input signal, it is possible to extract

as follows:

denoting

and

the real part and the imaginary part of

, respectively.…”

Section: Discussionmentioning

confidence: 99%

) and the x -axis of the gyroscope (

).…”

Section: Discussionmentioning

confidence: 99%

Section: Cardiac Activity: Data Analysismentioning

confidence: 99%

See 2 more Smart Citations

A Wearable System with Embedded Conductive Textiles and an IMU for Unobtrusive Cardio-Respiratory Monitoring

Tocco

Raiano

Sabbadini

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…For heartbeat detection, most of the conventional schemes of heartbeat detection are based on the criterion of template matching. To be specific, the authors in [ 13 ] proposed to extract the envelope of the BCG signal with Hilbert transform and then calculated the averaged heart rate in the frequency domain using the fast Fourier transform. For beat-to-beat detection, the authors in [ 14 , 15 ] employed discrete wavelet transform and filter banks to extract BCG signals from the mixed vital signs, where the heartbeat interval was obtained by identifying the J-peak of each BCG signal.…”

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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A robust QRS detection and accurate R-peak identification algorithm for wearable ECG sensors

Zhao

Wang

et al. 2021

Sci. China Inf. Sci.

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An Unobtrusive System for Heart Rate Monitoring Based on Ballistocardiogram Using Hilbert Transform and Viterbi Decoding

Cited by 17 publications

References 27 publications

A Wearable System with Embedded Conductive Textiles and an IMU for Unobtrusive Cardio-Respiratory Monitoring

A Wearable System with Embedded Conductive Textiles and an IMU for Unobtrusive Cardio-Respiratory Monitoring

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

A robust QRS detection and accurate R-peak identification algorithm for wearable ECG sensors

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