Spectrogram-Based Non-Contact RRI Estimation by Accurate Peak Detection Algorithm

Yamamoto, Kohei; Toyoda, Kentaroh; Ohtsuki, Tomoaki

doi:10.1109/access.2018.2875737

Cited by 37 publications

(40 citation statements)

References 21 publications

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“…From this figure, it can be seen that the spectrum due to each heartbeat appears in the frequency band higher than 2 Hz. Specifically, in our preliminary research [23], we have confirmed that the frequency components due to the heartbeat are mainly distributed from 8.0 Hz to 30 Hz. To associate features of the heartbeat signal with those of the ECG signal, the heartbeat components in such a high frequency band are essential.…”

Section: A Heartbeat Signal By Doppler Sensorsupporting

confidence: 66%

“…Some conventional methods estimate the HR based on frequency analysis by (i) Fast Fourier Transform (FFT) [10][11], (ii) Wavelet Transform (WT) [12][13], (iii) Multiple Signal Classification (MUSIC) [14] [15], and DCT [16]. In contrast, by detecting a heartbeat signal over the signal obtained through the extended signal processing, the conventional methods [17]- [23] estimate the BBI. Experimental results have shown that these conventional methods have achieved high HR and BBI estimation accuracies.…”

Section: Related Workmentioning

confidence: 99%

“…As one of the non-contact heartbeat detection methods, a Doppler sensor-based method has been studied intensively [10]- [23]. A Doppler sensor is a device that transmits microwaves toward a target and receives the microwaves reflected by the target.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, it is possible to measure the velocity and direction of the target's motion by analyzing the received signal. Based on this principle, the use of a Doppler sensor has been investigated in various fields such as heartbeat detection [10]- [23], respiration detection [24]- [26], and activity recognition [27]- [29]. However, the Signal-to-Noise Ratio (SNR) of heartbeat components is low, compared to those of respiration and slight body movements.…”

Section: Introductionmentioning

confidence: 99%

“…However, the Signal-to-Noise Ratio (SNR) of heartbeat components is low, compared to those of respiration and slight body movements. Hence, to accurately detect heartbeat, various Doppler sensor-based heartbeat detection methods have been proposed [10]- [23]. These conventional methods aim to estimate the beat-to-beat interval (BBI) and the heart rate (HR) and have been experimentally shown to achieve a high heartbeat detection accuracy.…”

Section: Introductionmentioning

confidence: 99%

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ECG Signal Reconstruction via Doppler Sensor by Hybrid Deep Learning Model With CNN and LSTM

2020

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An Electrocardiogram (ECG) is a typical method used to detect heartbeat, and an ECG signal analysis enables the detection of some heart diseases. However, the ECG-based heartbeat detection requires device attachment, which is not preferred for daily use. A Doppler sensor could be a device used to enable the non-contact heartbeat detection. In this paper, we propose a Doppler sensor-based ECG signal reconstruction method by a hybrid deep learning model with Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). An ECG signal can be reconstructed by relating features of a heartbeat signal obtained by a Doppler sensor to those of the ECG signal. Thus, we construct the deep learning model that extracts the spatial and temporal features from the heartbeat signal by CNN and LSTM. Based on the extracted features, the ECG signal is reconstructed. We conducted experiments to observe heartbeat against 9 healthy subjects without heart disease. The experimental results showed that our method performed ECG signal reconstruction with a correlation coefficient of 0.86 between the reconstructed and actual ECG signals, even without attaching devices. The results indicate that it is possible to remotely reconstruct an ECG signal from a heartbeat signal via a Doppler sensor.

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Section: A Heartbeat Signal By Doppler Sensorsupporting

confidence: 66%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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ECG Signal Reconstruction via Doppler Sensor by Hybrid Deep Learning Model With CNN and LSTM

2020

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Analysis of Visualized Bioindicators Related to Activities of Daily Living

Matsui

Onishi

Misaki

et al. 2021

Lecture Notes in Networks and Systems

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Contactless analysis of heart rate variability during cold pressor test using radar interferometry and bidirectional LSTM networks

Shi

Steigleder

Schellenberger

et al. 2021

Sci Rep

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Contactless measurement of heart rate variability (HRV), which reflects changes of the autonomic nervous system (ANS) and provides crucial information on the health status of a person, would provide great benefits for both patients and doctors during prevention and aftercare. However, gold standard devices to record the HRV, such as the electrocardiograph, have the common disadvantage that they need permanent skin contact with the patient. Being connected to a monitoring device by cable reduces the mobility, comfort, and compliance by patients. Here, we present a contactless approach using a 24 GHz Six-Port-based radar system and an LSTM network for radar heart sound segmentation. The best scores are obtained using a two-layer bidirectional LSTM architecture. To verify the performance of the proposed system not only in a static measurement scenario but also during a dynamic change of HRV parameters, a stimulation of the ANS through a cold pressor test is integrated in the study design. A total of 638 minutes of data is gathered from 25 test subjects and is analysed extensively. High F-scores of over 95% are achieved for heartbeat detection. HRV indices such as HF norm are extracted with relative errors around 5%. Our proposed approach is capable to perform contactless and convenient HRV monitoring and is therefore suitable for long-term recordings in clinical environments and home-care scenarios.

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Spectrogram-Based Non-Contact RRI Estimation by Accurate Peak Detection Algorithm

Cited by 37 publications

References 21 publications

ECG Signal Reconstruction via Doppler Sensor by Hybrid Deep Learning Model With CNN and LSTM

ECG Signal Reconstruction via Doppler Sensor by Hybrid Deep Learning Model With CNN and LSTM

Analysis of Visualized Bioindicators Related to Activities of Daily Living

Contactless analysis of heart rate variability during cold pressor test using radar interferometry and bidirectional LSTM networks

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