High-Accuracy Heart Rate Variability Monitoring Using Doppler Radar Based on Gaussian Pulse Train Modeling and FTPR Algorithm

Nosrati, Mehrdad; Tavassolian, Negar

doi:10.1109/tmtt.2017.2721407

Cited by 99 publications

(65 citation statements)

References 39 publications

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“…Also, in the previous research [17], even though the time window length is short, DCT has been shown to achieve the high HR estimation accuracy, compared with FFT. In addition to these HR estimation methods, M. Nosrati et al have introduced the FTPR (Frequency Time Phase Regression) algorithm for the accurate HR estimation [18], however, this method requires long time window, i.e., 10 s.…”

Section: Related Workmentioning

confidence: 99%

“…Thus, the use of a Doppler sensor has been investigated in the field of not only the heartbeat detection [5]- [28], but also the respiration detection [29]- [31] and the activity recognition [32]- [34]. In the Doppler sensorbased heartbeat detection, (i) the HR [5]- [17] and the RRI (R-R Interval) [18]- [27] are typically estimated. In general, the HR is estimated mainly based on the frequency analysis such as FFT (Fast Fourier Transform) and MSUIC (Multiple Signal Classification).…”

Section: Introductionmentioning

confidence: 99%

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Non-Contact Heartbeat Detection by Heartbeat Signal Reconstruction Based on Spectrogram Analysis With Convolutional LSTM

Yamamoto

Ohtsuki

2020

IEEE Access

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Heartbeat detection is one of key techniques to monitor our health condition in daily life, and demands for this technique have increased year and year. Thanks to the non-contact and non-invasive features, various Doppler sensor-based detection methods have been investigated so far. However, the heartbeat detection accuracy of the conventional methods could get degraded due to the low SNR (Signal-to-Noise Ratio) of heartbeat components. Thus, even after some signal processing, non-heartbeat components still remain over such processed signal, which could degrade the heartbeat detection accuracy. In particular for the subjects with low HR (Heart Rate), the estimated HR tends to be higher than the ground truth HR due to such non-heartbeat components, though the conventional methods have mainly focused on the heartbeat detection against the subjects with the normal HR higher than 50 bpm (Beats Per Minute). In this paper, to accurately detect heartbeat even with low HR via a Doppler sensor, we propose a heartbeat detection method based on heartbeat signal reconstruction with convolutional LSTM (Bidirectional-Long Short-Term Memory). In the proposed method, to reconstruct a heartbeat signal based on the periodicity of heartbeat and the spectrum distribution peculiar to heartbeat, successive spectrograms that might be due to heartbeat is used as an input to convolutional LSTM. In addition, for better reconstruction of a heartbeat signal, the previously estimated RRI (R-R Interval) is also used as a feature in the proposed deep learning model with convolutional LSTM. Through the experiments, we confirmed that our proposed method accurately detected heartbeat against 17 subjects including the ones with the HR lower than 50 bpm.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Non-Contact Heartbeat Detection by Heartbeat Signal Reconstruction Based on Spectrogram Analysis With Convolutional LSTM

Yamamoto

Ohtsuki

2020

IEEE Access

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“…The most important vital signs utilized in the related works summarized above are HR, FD, SpO2, and ECG [6][7][8][9][10][11][12][13][14]21]. HR and FD are the most significant signals considered in this work.…”

Section: Related Workmentioning

confidence: 99%

An Autonomous Wireless Health Monitoring System Based on Heartbeat and Accelerometer Sensors

Fakhrulddin

Gharghan

2019

JSAN

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Falls are a main cause of injury for patients with certain diseases. Patients who wear health monitoring systems can go about daily activities without limitations, thereby enhancing their quality of life. In this paper, patient falls and heart rate were accurately detected and measured using two proposed algorithms. The first algorithm, abnormal heart rate detection (AHRD), improves patient heart rate measurement accuracy and distinguishes between normal and abnormal heart rate functions. The second algorithm, TB-AIC, combines an acceleration threshold and monitoring of patient activity/inactivity functions to accurately detect patient falls. The two algorithms were practically implemented in a proposed autonomous wireless health monitoring system (AWHMS). The AWHMS was implemented based on a GSM module, GPS, microcontroller, heartbeat and accelerometer sensors, and a smartphone. The measurement accuracy of the recorded heart rate was evaluated based on the mean absolute error, Bland–Altman plots, and correlation coefficients. Fourteen types of patient activities were considered (seven types of falling and seven types of daily activities) to determine the fall detection accuracy. The results indicate that the proposed AWHMS succeeded in monitoring the patient’s vital signs, with heart rate measurement and fall detection accuracies of 98.75% and 99.11%, respectively. In addition, the sensitivity and specificity of the fall detection algorithm (both 99.12%) were explored.

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“…For an Indented Radar system, Equation (15) should be modified as it would be subject to Amplitude and DC imbalance. By simulating different cases, it is observed that in DACM algorithm the effect of DC offset is more crucial than amplitude imbalance and other factors, as addressed in [24] and [9]. Thus, considering the DC offset imbalance, Equation (15) gets modified to Equation (19).…”

Section: Dacmmentioning

confidence: 99%

Novel Approach for Vibration Detection Using Indented Radar

Gigie¹,

Rani²,

Sinharay³

et al. 2018

PIER C

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Non-contact vibration detection using microwave radar is becoming a popular research area. However, vibration sensing using Doppler radar based measurements suffers from the problem of 'Null point'. In order to mitigate this, traditional designs incorporate phase measurements using Quadrature (I/Q) radar. Such Quadrature radars are not cost effective for large scale indoor deployment scenarios. In this paper, we propose a new configuration of 'Indented Radar'; a system of two single-channel radars offset in space by a path length, which is equivalent to 90 degree phase shift. However, such a system of two independent channels is prone to different imbalances such as amplitude, phase and DC. This work closely examines the imbalance effect on the two-radar system and reports a novel approach that can be used to tackle such imbalance in a two-radar configuration. Our approach yields superior results over other commonly used I/Q algorithms, while measuring vibrational frequencies. Thus, our work can find immense application in both vital sign detection and structural vibration detection use-cases where affordable solution is sought.

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High-Accuracy Heart Rate Variability Monitoring Using Doppler Radar Based on Gaussian Pulse Train Modeling and FTPR Algorithm

Cited by 99 publications

References 39 publications

Non-Contact Heartbeat Detection by Heartbeat Signal Reconstruction Based on Spectrogram Analysis With Convolutional LSTM

Non-Contact Heartbeat Detection by Heartbeat Signal Reconstruction Based on Spectrogram Analysis With Convolutional LSTM

An Autonomous Wireless Health Monitoring System Based on Heartbeat and Accelerometer Sensors

Novel Approach for Vibration Detection Using Indented Radar

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