Spectrogram-based Non-contact RRI Estimation by Accurate Peak Detection Algorithm

Kohei Yamamoto, Kentaro Toyoda, Tomoaki Ohtsuki

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Demands for vital sign monitoring are increasing in the field of health care. In particular, the RRI (R-R Interval) estimation has been studied extensively, since the RRI variation is highly related with the stress of a subject. Various Doppler sensor based-heartbeat detection methods have been proposed so far, thanks to non-contact and non-invasive features of a Doppler sensor. In our previous research, we have proposed a Doppler sensor-based RRI estimation method by a spectrogram. In this method, the spectra due to heartbeats are integrated on a spectrogram, and then the RRI is estimated by detecting the peaks of the integrated spectrum. However, the undesired peaks sometimes appear even in the situation where a subject sits still. In this paper, as the extended version of our previous method, we propose a Doppler sensor- based RRI estimation method leveraging the accurate peak detection. In the proposed method, to prevent the incorrect peak detection, the peaks due to heartbeats are detected using some peaks before and after the investigated peak. Through the experiments on 10 subjects in the cases where a subject was sitting still, typing, and speaking, we confirmed that the proposed method improved our previous and state-of-the-art ones by the RMSE (Root Mean Squared Error) of the RRI. Furthermore, based on the estimated RRI, we calculated the stress index LF/HF (Low-Frequency/High-Frequency), which is one of the useful indices to evaluate the stress of a subject. As a result, our proposed method outperformed the other ones by the RE (Relative Error) of the LF/HF.

Original languageEnglish
JournalIEEE Access
DOIs
Publication statusAccepted/In press - 2018 Jan 1

Fingerprint

Sensors
Health care
Monitoring
Experiments

Keywords

  • Doppler effect
  • Doppler sensor
  • Estimation
  • Health care
  • Heart beat
  • Heartbeat
  • Microwave theory and techniques
  • Microwaves
  • Rail to rail inputs
  • RRI (R-R Interval)
  • Spectrogram
  • Stress

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

Cite this

Spectrogram-based Non-contact RRI Estimation by Accurate Peak Detection Algorithm. / Yamamoto, Kohei; Toyoda, Kentaro; Ohtsuki, Tomoaki.

In: IEEE Access, 01.01.2018.

Research output: Contribution to journalArticle

@article{725bbd71c24b42bca92f467aa925b5df,
title = "Spectrogram-based Non-contact RRI Estimation by Accurate Peak Detection Algorithm",
abstract = "Demands for vital sign monitoring are increasing in the field of health care. In particular, the RRI (R-R Interval) estimation has been studied extensively, since the RRI variation is highly related with the stress of a subject. Various Doppler sensor based-heartbeat detection methods have been proposed so far, thanks to non-contact and non-invasive features of a Doppler sensor. In our previous research, we have proposed a Doppler sensor-based RRI estimation method by a spectrogram. In this method, the spectra due to heartbeats are integrated on a spectrogram, and then the RRI is estimated by detecting the peaks of the integrated spectrum. However, the undesired peaks sometimes appear even in the situation where a subject sits still. In this paper, as the extended version of our previous method, we propose a Doppler sensor- based RRI estimation method leveraging the accurate peak detection. In the proposed method, to prevent the incorrect peak detection, the peaks due to heartbeats are detected using some peaks before and after the investigated peak. Through the experiments on 10 subjects in the cases where a subject was sitting still, typing, and speaking, we confirmed that the proposed method improved our previous and state-of-the-art ones by the RMSE (Root Mean Squared Error) of the RRI. Furthermore, based on the estimated RRI, we calculated the stress index LF/HF (Low-Frequency/High-Frequency), which is one of the useful indices to evaluate the stress of a subject. As a result, our proposed method outperformed the other ones by the RE (Relative Error) of the LF/HF.",
keywords = "Doppler effect, Doppler sensor, Estimation, Health care, Heart beat, Heartbeat, Microwave theory and techniques, Microwaves, Rail to rail inputs, RRI (R-R Interval), Spectrogram, Stress",
author = "Kohei Yamamoto and Kentaro Toyoda and Tomoaki Ohtsuki",
year = "2018",
month = "1",
day = "1",
doi = "10.1109/ACCESS.2018.2875737",
language = "English",
journal = "IEEE Access",
issn = "2169-3536",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - JOUR

T1 - Spectrogram-based Non-contact RRI Estimation by Accurate Peak Detection Algorithm

AU - Yamamoto, Kohei

AU - Toyoda, Kentaro

AU - Ohtsuki, Tomoaki

PY - 2018/1/1

Y1 - 2018/1/1

N2 - Demands for vital sign monitoring are increasing in the field of health care. In particular, the RRI (R-R Interval) estimation has been studied extensively, since the RRI variation is highly related with the stress of a subject. Various Doppler sensor based-heartbeat detection methods have been proposed so far, thanks to non-contact and non-invasive features of a Doppler sensor. In our previous research, we have proposed a Doppler sensor-based RRI estimation method by a spectrogram. In this method, the spectra due to heartbeats are integrated on a spectrogram, and then the RRI is estimated by detecting the peaks of the integrated spectrum. However, the undesired peaks sometimes appear even in the situation where a subject sits still. In this paper, as the extended version of our previous method, we propose a Doppler sensor- based RRI estimation method leveraging the accurate peak detection. In the proposed method, to prevent the incorrect peak detection, the peaks due to heartbeats are detected using some peaks before and after the investigated peak. Through the experiments on 10 subjects in the cases where a subject was sitting still, typing, and speaking, we confirmed that the proposed method improved our previous and state-of-the-art ones by the RMSE (Root Mean Squared Error) of the RRI. Furthermore, based on the estimated RRI, we calculated the stress index LF/HF (Low-Frequency/High-Frequency), which is one of the useful indices to evaluate the stress of a subject. As a result, our proposed method outperformed the other ones by the RE (Relative Error) of the LF/HF.

AB - Demands for vital sign monitoring are increasing in the field of health care. In particular, the RRI (R-R Interval) estimation has been studied extensively, since the RRI variation is highly related with the stress of a subject. Various Doppler sensor based-heartbeat detection methods have been proposed so far, thanks to non-contact and non-invasive features of a Doppler sensor. In our previous research, we have proposed a Doppler sensor-based RRI estimation method by a spectrogram. In this method, the spectra due to heartbeats are integrated on a spectrogram, and then the RRI is estimated by detecting the peaks of the integrated spectrum. However, the undesired peaks sometimes appear even in the situation where a subject sits still. In this paper, as the extended version of our previous method, we propose a Doppler sensor- based RRI estimation method leveraging the accurate peak detection. In the proposed method, to prevent the incorrect peak detection, the peaks due to heartbeats are detected using some peaks before and after the investigated peak. Through the experiments on 10 subjects in the cases where a subject was sitting still, typing, and speaking, we confirmed that the proposed method improved our previous and state-of-the-art ones by the RMSE (Root Mean Squared Error) of the RRI. Furthermore, based on the estimated RRI, we calculated the stress index LF/HF (Low-Frequency/High-Frequency), which is one of the useful indices to evaluate the stress of a subject. As a result, our proposed method outperformed the other ones by the RE (Relative Error) of the LF/HF.

KW - Doppler effect

KW - Doppler sensor

KW - Estimation

KW - Health care

KW - Heart beat

KW - Heartbeat

KW - Microwave theory and techniques

KW - Microwaves

KW - Rail to rail inputs

KW - RRI (R-R Interval)

KW - Spectrogram

KW - Stress

UR - http://www.scopus.com/inward/record.url?scp=85055055899&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85055055899&partnerID=8YFLogxK

U2 - 10.1109/ACCESS.2018.2875737

DO - 10.1109/ACCESS.2018.2875737

M3 - Article

JO - IEEE Access

JF - IEEE Access

SN - 2169-3536

ER -