IEICE TRANSACTIONS on Information
Noise Tolerant Heart Rate Extraction Algorithm Using Short-Term Autocorrelation for Wearable Healthcare Systems
Summary :
This report describes a robust method of instantaneous heart rate (IHR) extraction from noisy electrocardiogram (ECG) signals. Generally, R-waves are extracted from ECG using a threshold to calculate the IHR from the interval of R-waves. However, noise increases the incidence of misdetection and false detection in wearable healthcare systems because the power consumption and electrode distance are limited to reduce the size and weight. To prevent incorrect detection, we propose a short-time autocorrelation (STAC) technique. The proposed method extracts the IHR by determining the search window shift length which maximizes the correlation coefficient between the template window and the search window. It uses the similarity of the QRS complex waveform beat-by-beat. Therefore, it has no threshold calculation process. Furthermore, it is robust against noisy environments. The proposed method was evaluated using MIT-BIH arrhythmia and noise stress test databases. Simulation results show that the proposed method achieves a state-of-the-art success rate of IHR extraction in a noise stress test using a muscle artifact and a motion artifact.
- Publication
- IEICE TRANSACTIONS on Information Vol.E98-D No.5 pp.1095-1103
- Publication Date
- 2015/05/01
- Publicized
- 2015/01/26
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2014EDP7161
- Type of Manuscript
- PAPER
- Category
- Biological Engineering
Authors
Shintaro IZUMI
Kobe University
Masanao NAKANO
Kobe University
Ken YAMASHITA
Kobe University
Yozaburo NAKAI
Kobe University
Hiroshi KAWAGUCHI
Kobe University
Masahiko YOSHIMOTO
Kobe University
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Shintaro IZUMI, Masanao NAKANO, Ken YAMASHITA, Yozaburo NAKAI, Hiroshi KAWAGUCHI, Masahiko YOSHIMOTO, "Noise Tolerant Heart Rate Extraction Algorithm Using Short-Term Autocorrelation for Wearable Healthcare Systems" in IEICE TRANSACTIONS on Information,
vol. E98-D, no. 5, pp. 1095-1103, May 2015, doi: 10.1587/transinf.2014EDP7161.
Abstract: This report describes a robust method of instantaneous heart rate (IHR) extraction from noisy electrocardiogram (ECG) signals. Generally, R-waves are extracted from ECG using a threshold to calculate the IHR from the interval of R-waves. However, noise increases the incidence of misdetection and false detection in wearable healthcare systems because the power consumption and electrode distance are limited to reduce the size and weight. To prevent incorrect detection, we propose a short-time autocorrelation (STAC) technique. The proposed method extracts the IHR by determining the search window shift length which maximizes the correlation coefficient between the template window and the search window. It uses the similarity of the QRS complex waveform beat-by-beat. Therefore, it has no threshold calculation process. Furthermore, it is robust against noisy environments. The proposed method was evaluated using MIT-BIH arrhythmia and noise stress test databases. Simulation results show that the proposed method achieves a state-of-the-art success rate of IHR extraction in a noise stress test using a muscle artifact and a motion artifact.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2014EDP7161/_p
Copy
@ARTICLE{e98-d_5_1095,
author={Shintaro IZUMI, Masanao NAKANO, Ken YAMASHITA, Yozaburo NAKAI, Hiroshi KAWAGUCHI, Masahiko YOSHIMOTO, },
journal={IEICE TRANSACTIONS on Information},
title={Noise Tolerant Heart Rate Extraction Algorithm Using Short-Term Autocorrelation for Wearable Healthcare Systems},
year={2015},
volume={E98-D},
number={5},
pages={1095-1103},
abstract={This report describes a robust method of instantaneous heart rate (IHR) extraction from noisy electrocardiogram (ECG) signals. Generally, R-waves are extracted from ECG using a threshold to calculate the IHR from the interval of R-waves. However, noise increases the incidence of misdetection and false detection in wearable healthcare systems because the power consumption and electrode distance are limited to reduce the size and weight. To prevent incorrect detection, we propose a short-time autocorrelation (STAC) technique. The proposed method extracts the IHR by determining the search window shift length which maximizes the correlation coefficient between the template window and the search window. It uses the similarity of the QRS complex waveform beat-by-beat. Therefore, it has no threshold calculation process. Furthermore, it is robust against noisy environments. The proposed method was evaluated using MIT-BIH arrhythmia and noise stress test databases. Simulation results show that the proposed method achieves a state-of-the-art success rate of IHR extraction in a noise stress test using a muscle artifact and a motion artifact.},
keywords={},
doi={10.1587/transinf.2014EDP7161},
ISSN={1745-1361},
month={May},}
Copy
TY - JOUR
TI - Noise Tolerant Heart Rate Extraction Algorithm Using Short-Term Autocorrelation for Wearable Healthcare Systems
T2 - IEICE TRANSACTIONS on Information
SP - 1095
EP - 1103
AU - Shintaro IZUMI
AU - Masanao NAKANO
AU - Ken YAMASHITA
AU - Yozaburo NAKAI
AU - Hiroshi KAWAGUCHI
AU - Masahiko YOSHIMOTO
PY - 2015
DO - 10.1587/transinf.2014EDP7161
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E98-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2015
AB - This report describes a robust method of instantaneous heart rate (IHR) extraction from noisy electrocardiogram (ECG) signals. Generally, R-waves are extracted from ECG using a threshold to calculate the IHR from the interval of R-waves. However, noise increases the incidence of misdetection and false detection in wearable healthcare systems because the power consumption and electrode distance are limited to reduce the size and weight. To prevent incorrect detection, we propose a short-time autocorrelation (STAC) technique. The proposed method extracts the IHR by determining the search window shift length which maximizes the correlation coefficient between the template window and the search window. It uses the similarity of the QRS complex waveform beat-by-beat. Therefore, it has no threshold calculation process. Furthermore, it is robust against noisy environments. The proposed method was evaluated using MIT-BIH arrhythmia and noise stress test databases. Simulation results show that the proposed method achieves a state-of-the-art success rate of IHR extraction in a noise stress test using a muscle artifact and a motion artifact.
ER -
English
