Extracting features for cardiovascular disease classification based on ballistocardiography

Yalong Song; Hongbo Ni; Xingshe Zhou; Weichao Zhao; Tianben Wang

doi:10.1109/UIC-ATC-ScalCom-CBDCom-IoP.2015.223

Extracting features for cardiovascular disease classification based on ballistocardiography

Yalong Song
, Hongbo Ni
, Xingshe Zhou
, Weichao Zhao
, Tianben Wang

School of Computer Science

Northwestern Polytechnical University Xian

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

27 Scopus citations

Abstract

Cardiovascular disease affects the health of people seriously in the world, especially in the elderly. This paper proposes an effective approach of detecting and analyzing the health status of the elderly. In this work, we continuously acquire Ballisto cardiography (BCG) signal with the micro-movement sensitive mattress (MSM) during non-intrusive sleep in home environment. In the paper, we propose a new method to extract heartbeat intervals (RR) based on Ensemble Empirical Mode Decomposition (EEMD), and extract the signal features by calculating the parameters of heart rate variability (HRV) from time domain analysis, frequency domain analysis and nonlinear analysis. A Naïve Bayesian Classification method is applied to classify the normal persons, hypertension patients and coronary heart disease (CHD) patients by using the obtained features. The proposed method is evaluated by using the BCG datasets from eighteen subjects, including eight females and ten males (age 40-72). The results are satisfactory and can provide a classification precision of 92.3%.

Original language	English
Title of host publication	Proceedings - 2015 IEEE 12th International Conference on Ubiquitous Intelligence and Computing, 2015 IEEE 12th International Conference on Advanced and Trusted Computing, 2015 IEEE 15th International Conference on Scalable Computing and Communications, 2015 IEEE International Conference on Cloud and Big Data Computing, 2015 IEEE International Conference on Internet of People and Associated Symposia/Workshops, UIC-ATC-ScalCom-CBDCom-IoP 2015
Editors	Jianhua Ma, Ali Li, Huansheng Ning, Laurence T. Yang
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1230-1235
Number of pages	6
ISBN (Electronic)	9781467372114
DOIs	https://doi.org/10.1109/UIC-ATC-ScalCom-CBDCom-IoP.2015.223
State	Published - 20 Jul 2016
Event	Proceedings - 2015 IEEE 12th International Conference on Ubiquitous Intelligence and Computing, 2015 IEEE 12th International Conference on Advanced and Trusted Computing, 2015 IEEE 15th International Conference on Scalable Computing and Communications, 2015 IEEE International Conference on Cloud and Big Data Computing, 2015 IEEE International Conference on Internet of People and Associated Symposia/Workshops, UIC-ATC-ScalCom-CBDCom-IoP 2015 - Beijing, China Duration: 10 Aug 2015 → 14 Aug 2015

Publication series

Name

Proceedings - 2015 IEEE 12th International Conference on Ubiquitous Intelligence and Computing, 2015 IEEE 12th International Conference on Advanced and Trusted Computing, 2015 IEEE 15th International Conference on Scalable Computing and Communications, 2015 IEEE International Conference on Cloud and Big Data Computing, 2015 IEEE International Conference on Internet of People and Associated Symposia/Workshops, UIC-ATC-ScalCom-CBDCom-IoP 2015

Conference

Conference	Proceedings - 2015 IEEE 12th International Conference on Ubiquitous Intelligence and Computing, 2015 IEEE 12th International Conference on Advanced and Trusted Computing, 2015 IEEE 15th International Conference on Scalable Computing and Communications, 2015 IEEE International Conference on Cloud and Big Data Computing, 2015 IEEE International Conference on Internet of People and Associated Symposia/Workshops, UIC-ATC-ScalCom-CBDCom-IoP 2015
Country/Territory	China
City	Beijing
Period	10/08/15 → 14/08/15

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Ballistocardiography
Cardiovascular Disease
EEMD
Naïve Bayesian Classification

Access to Document

10.1109/UIC-ATC-ScalCom-CBDCom-IoP.2015.223

Cite this

Song, Y., Ni, H., Zhou, X., Zhao, W., & Wang, T. (2016). Extracting features for cardiovascular disease classification based on ballistocardiography. In J. Ma, A. Li, H. Ning, & L. T. Yang (Eds.), Proceedings - 2015 IEEE 12th International Conference on Ubiquitous Intelligence and Computing, 2015 IEEE 12th International Conference on Advanced and Trusted Computing, 2015 IEEE 15th International Conference on Scalable Computing and Communications, 2015 IEEE International Conference on Cloud and Big Data Computing, 2015 IEEE International Conference on Internet of People and Associated Symposia/Workshops, UIC-ATC-ScalCom-CBDCom-IoP 2015 (pp. 1230-1235). Article 7518402 (Proceedings - 2015 IEEE 12th International Conference on Ubiquitous Intelligence and Computing, 2015 IEEE 12th International Conference on Advanced and Trusted Computing, 2015 IEEE 15th International Conference on Scalable Computing and Communications, 2015 IEEE International Conference on Cloud and Big Data Computing, 2015 IEEE International Conference on Internet of People and Associated Symposia/Workshops, UIC-ATC-ScalCom-CBDCom-IoP 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/UIC-ATC-ScalCom-CBDCom-IoP.2015.223

Song, Yalong ; Ni, Hongbo ; Zhou, Xingshe et al. / Extracting features for cardiovascular disease classification based on ballistocardiography. Proceedings - 2015 IEEE 12th International Conference on Ubiquitous Intelligence and Computing, 2015 IEEE 12th International Conference on Advanced and Trusted Computing, 2015 IEEE 15th International Conference on Scalable Computing and Communications, 2015 IEEE International Conference on Cloud and Big Data Computing, 2015 IEEE International Conference on Internet of People and Associated Symposia/Workshops, UIC-ATC-ScalCom-CBDCom-IoP 2015. editor / Jianhua Ma ; Ali Li ; Huansheng Ning ; Laurence T. Yang. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 1230-1235 (Proceedings - 2015 IEEE 12th International Conference on Ubiquitous Intelligence and Computing, 2015 IEEE 12th International Conference on Advanced and Trusted Computing, 2015 IEEE 15th International Conference on Scalable Computing and Communications, 2015 IEEE International Conference on Cloud and Big Data Computing, 2015 IEEE International Conference on Internet of People and Associated Symposia/Workshops, UIC-ATC-ScalCom-CBDCom-IoP 2015).

@inproceedings{ebc3c5aad5954c149bbc51751f1923aa,

title = "Extracting features for cardiovascular disease classification based on ballistocardiography",

abstract = "Cardiovascular disease affects the health of people seriously in the world, especially in the elderly. This paper proposes an effective approach of detecting and analyzing the health status of the elderly. In this work, we continuously acquire Ballisto cardiography (BCG) signal with the micro-movement sensitive mattress (MSM) during non-intrusive sleep in home environment. In the paper, we propose a new method to extract heartbeat intervals (RR) based on Ensemble Empirical Mode Decomposition (EEMD), and extract the signal features by calculating the parameters of heart rate variability (HRV) from time domain analysis, frequency domain analysis and nonlinear analysis. A Na{\"i}ve Bayesian Classification method is applied to classify the normal persons, hypertension patients and coronary heart disease (CHD) patients by using the obtained features. The proposed method is evaluated by using the BCG datasets from eighteen subjects, including eight females and ten males (age 40-72). The results are satisfactory and can provide a classification precision of 92.3\%.",

keywords = "Ballistocardiography, Cardiovascular Disease, EEMD, Na{\"i}ve Bayesian Classification",

author = "Yalong Song and Hongbo Ni and Xingshe Zhou and Weichao Zhao and Tianben Wang",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; Proceedings - 2015 IEEE 12th International Conference on Ubiquitous Intelligence and Computing, 2015 IEEE 12th International Conference on Advanced and Trusted Computing, 2015 IEEE 15th International Conference on Scalable Computing and Communications, 2015 IEEE International Conference on Cloud and Big Data Computing, 2015 IEEE International Conference on Internet of People and Associated Symposia/Workshops, UIC-ATC-ScalCom-CBDCom-IoP 2015 ; Conference date: 10-08-2015 Through 14-08-2015",

year = "2016",

month = jul,

day = "20",

doi = "10.1109/UIC-ATC-ScalCom-CBDCom-IoP.2015.223",

language = "英语",

series = "Proceedings - 2015 IEEE 12th International Conference on Ubiquitous Intelligence and Computing, 2015 IEEE 12th International Conference on Advanced and Trusted Computing, 2015 IEEE 15th International Conference on Scalable Computing and Communications, 2015 IEEE International Conference on Cloud and Big Data Computing, 2015 IEEE International Conference on Internet of People and Associated Symposia/Workshops, UIC-ATC-ScalCom-CBDCom-IoP 2015",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1230--1235",

editor = "Jianhua Ma and Ali Li and Huansheng Ning and Yang, \{Laurence T.\}",

booktitle = "Proceedings - 2015 IEEE 12th International Conference on Ubiquitous Intelligence and Computing, 2015 IEEE 12th International Conference on Advanced and Trusted Computing, 2015 IEEE 15th International Conference on Scalable Computing and Communications, 2015 IEEE International Conference on Cloud and Big Data Computing, 2015 IEEE International Conference on Internet of People and Associated Symposia/Workshops, UIC-ATC-ScalCom-CBDCom-IoP 2015",

}

Song, Y, Ni, H, Zhou, X, Zhao, W & Wang, T 2016, Extracting features for cardiovascular disease classification based on ballistocardiography. in J Ma, A Li, H Ning & LT Yang (eds), Proceedings - 2015 IEEE 12th International Conference on Ubiquitous Intelligence and Computing, 2015 IEEE 12th International Conference on Advanced and Trusted Computing, 2015 IEEE 15th International Conference on Scalable Computing and Communications, 2015 IEEE International Conference on Cloud and Big Data Computing, 2015 IEEE International Conference on Internet of People and Associated Symposia/Workshops, UIC-ATC-ScalCom-CBDCom-IoP 2015., 7518402, Proceedings - 2015 IEEE 12th International Conference on Ubiquitous Intelligence and Computing, 2015 IEEE 12th International Conference on Advanced and Trusted Computing, 2015 IEEE 15th International Conference on Scalable Computing and Communications, 2015 IEEE International Conference on Cloud and Big Data Computing, 2015 IEEE International Conference on Internet of People and Associated Symposia/Workshops, UIC-ATC-ScalCom-CBDCom-IoP 2015, Institute of Electrical and Electronics Engineers Inc., pp. 1230-1235, Proceedings - 2015 IEEE 12th International Conference on Ubiquitous Intelligence and Computing, 2015 IEEE 12th International Conference on Advanced and Trusted Computing, 2015 IEEE 15th International Conference on Scalable Computing and Communications, 2015 IEEE International Conference on Cloud and Big Data Computing, 2015 IEEE International Conference on Internet of People and Associated Symposia/Workshops, UIC-ATC-ScalCom-CBDCom-IoP 2015, Beijing, China, 10/08/15. https://doi.org/10.1109/UIC-ATC-ScalCom-CBDCom-IoP.2015.223

Extracting features for cardiovascular disease classification based on ballistocardiography. / Song, Yalong; Ni, Hongbo; Zhou, Xingshe et al.
Proceedings - 2015 IEEE 12th International Conference on Ubiquitous Intelligence and Computing, 2015 IEEE 12th International Conference on Advanced and Trusted Computing, 2015 IEEE 15th International Conference on Scalable Computing and Communications, 2015 IEEE International Conference on Cloud and Big Data Computing, 2015 IEEE International Conference on Internet of People and Associated Symposia/Workshops, UIC-ATC-ScalCom-CBDCom-IoP 2015. ed. / Jianhua Ma; Ali Li; Huansheng Ning; Laurence T. Yang. Institute of Electrical and Electronics Engineers Inc., 2016. p. 1230-1235 7518402 (Proceedings - 2015 IEEE 12th International Conference on Ubiquitous Intelligence and Computing, 2015 IEEE 12th International Conference on Advanced and Trusted Computing, 2015 IEEE 15th International Conference on Scalable Computing and Communications, 2015 IEEE International Conference on Cloud and Big Data Computing, 2015 IEEE International Conference on Internet of People and Associated Symposia/Workshops, UIC-ATC-ScalCom-CBDCom-IoP 2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Extracting features for cardiovascular disease classification based on ballistocardiography

AU - Song, Yalong

AU - Ni, Hongbo

AU - Zhou, Xingshe

AU - Zhao, Weichao

AU - Wang, Tianben

PY - 2016/7/20

Y1 - 2016/7/20

N2 - Cardiovascular disease affects the health of people seriously in the world, especially in the elderly. This paper proposes an effective approach of detecting and analyzing the health status of the elderly. In this work, we continuously acquire Ballisto cardiography (BCG) signal with the micro-movement sensitive mattress (MSM) during non-intrusive sleep in home environment. In the paper, we propose a new method to extract heartbeat intervals (RR) based on Ensemble Empirical Mode Decomposition (EEMD), and extract the signal features by calculating the parameters of heart rate variability (HRV) from time domain analysis, frequency domain analysis and nonlinear analysis. A Naïve Bayesian Classification method is applied to classify the normal persons, hypertension patients and coronary heart disease (CHD) patients by using the obtained features. The proposed method is evaluated by using the BCG datasets from eighteen subjects, including eight females and ten males (age 40-72). The results are satisfactory and can provide a classification precision of 92.3%.

AB - Cardiovascular disease affects the health of people seriously in the world, especially in the elderly. This paper proposes an effective approach of detecting and analyzing the health status of the elderly. In this work, we continuously acquire Ballisto cardiography (BCG) signal with the micro-movement sensitive mattress (MSM) during non-intrusive sleep in home environment. In the paper, we propose a new method to extract heartbeat intervals (RR) based on Ensemble Empirical Mode Decomposition (EEMD), and extract the signal features by calculating the parameters of heart rate variability (HRV) from time domain analysis, frequency domain analysis and nonlinear analysis. A Naïve Bayesian Classification method is applied to classify the normal persons, hypertension patients and coronary heart disease (CHD) patients by using the obtained features. The proposed method is evaluated by using the BCG datasets from eighteen subjects, including eight females and ten males (age 40-72). The results are satisfactory and can provide a classification precision of 92.3%.

KW - Ballistocardiography

KW - Cardiovascular Disease

KW - EEMD

KW - Naïve Bayesian Classification

UR - https://www.scopus.com/pages/publications/84983416412

U2 - 10.1109/UIC-ATC-ScalCom-CBDCom-IoP.2015.223

DO - 10.1109/UIC-ATC-ScalCom-CBDCom-IoP.2015.223

M3 - 会议稿件

AN - SCOPUS:84983416412

T3 - Proceedings - 2015 IEEE 12th International Conference on Ubiquitous Intelligence and Computing, 2015 IEEE 12th International Conference on Advanced and Trusted Computing, 2015 IEEE 15th International Conference on Scalable Computing and Communications, 2015 IEEE International Conference on Cloud and Big Data Computing, 2015 IEEE International Conference on Internet of People and Associated Symposia/Workshops, UIC-ATC-ScalCom-CBDCom-IoP 2015

SP - 1230

EP - 1235

BT - Proceedings - 2015 IEEE 12th International Conference on Ubiquitous Intelligence and Computing, 2015 IEEE 12th International Conference on Advanced and Trusted Computing, 2015 IEEE 15th International Conference on Scalable Computing and Communications, 2015 IEEE International Conference on Cloud and Big Data Computing, 2015 IEEE International Conference on Internet of People and Associated Symposia/Workshops, UIC-ATC-ScalCom-CBDCom-IoP 2015

A2 - Ma, Jianhua

A2 - Li, Ali

A2 - Ning, Huansheng

A2 - Yang, Laurence T.

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - Proceedings - 2015 IEEE 12th International Conference on Ubiquitous Intelligence and Computing, 2015 IEEE 12th International Conference on Advanced and Trusted Computing, 2015 IEEE 15th International Conference on Scalable Computing and Communications, 2015 IEEE International Conference on Cloud and Big Data Computing, 2015 IEEE International Conference on Internet of People and Associated Symposia/Workshops, UIC-ATC-ScalCom-CBDCom-IoP 2015

Y2 - 10 August 2015 through 14 August 2015

ER -

Song Y, Ni H, Zhou X, Zhao W, Wang T. Extracting features for cardiovascular disease classification based on ballistocardiography. In Ma J, Li A, Ning H, Yang LT, editors, Proceedings - 2015 IEEE 12th International Conference on Ubiquitous Intelligence and Computing, 2015 IEEE 12th International Conference on Advanced and Trusted Computing, 2015 IEEE 15th International Conference on Scalable Computing and Communications, 2015 IEEE International Conference on Cloud and Big Data Computing, 2015 IEEE International Conference on Internet of People and Associated Symposia/Workshops, UIC-ATC-ScalCom-CBDCom-IoP 2015. Institute of Electrical and Electronics Engineers Inc. 2016. p. 1230-1235. 7518402. (Proceedings - 2015 IEEE 12th International Conference on Ubiquitous Intelligence and Computing, 2015 IEEE 12th International Conference on Advanced and Trusted Computing, 2015 IEEE 15th International Conference on Scalable Computing and Communications, 2015 IEEE International Conference on Cloud and Big Data Computing, 2015 IEEE International Conference on Internet of People and Associated Symposia/Workshops, UIC-ATC-ScalCom-CBDCom-IoP 2015). doi: 10.1109/UIC-ATC-ScalCom-CBDCom-IoP.2015.223

Extracting features for cardiovascular disease classification based on ballistocardiography

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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