Wearable Accelerometer based Pseudo-ECG Generation

Kalyana Chakaravarthy Veluvolu

doi:10.1109/IconDSC.2019.8817000

Wearable Accelerometer based Pseudo-ECG Generation

Kalyana Chakaravarthy Veluvolu

School of Electronics Engineering

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

2 Scopus citations

Abstract

For monitoring and diagnostics of the cardiovascular health, Electrocardiogram (ECG)is considered as a standard modality. However, direct contact of electrodes with skin, and signal corruption due to sweat and the daily-activity induced motion limits the use of conventional ECG in wearable-sensor based long term health monitoring devices. Accelerometers are light-weight, inexpensive and can be used simultaneously in multiple applications. Therefore, we propose an approach for generating pseudo-ECG from a chest-worn accelerometer. First a non-linear regression is identified between the chest surface acceleration and the conventional ECG signals. Based on the identified regression model, a pseudo-ECG signal is generated from the chest acceleration signal. The proposed method was validated on 15 trials data recorded from 3 human subjects. Results show that the generated ECG matches well with the real ECG with an average correlation coefficient of 0.8.

Original language	English
Title of host publication	2019 International Conference on Data Science and Communication, IconDSC 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781538693193
DOIs	https://doi.org/10.1109/IconDSC.2019.8817000
State	Published - Mar 2019
Event	2019 International Conference on Data Science and Communication, IconDSC 2019 - Bangalore, India Duration: 1 Mar 2019 → 2 Mar 2019

Publication series

Name	2019 International Conference on Data Science and Communication, IconDSC 2019

Conference

Conference	2019 International Conference on Data Science and Communication, IconDSC 2019
Country/Territory	India
City	Bangalore
Period	1/03/19 → 2/03/19

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

Cardiac Health Monitoring
Personalized Monitoring
Pseudo-ECG
Wearable Accelerometer
Wearable Sensors

Access to Document

10.1109/IconDSC.2019.8817000

Cite this

@inproceedings{38935c1b7f6f4295ab29d2cd0d62fa60,

title = "Wearable Accelerometer based Pseudo-ECG Generation",

abstract = "For monitoring and diagnostics of the cardiovascular health, Electrocardiogram (ECG)is considered as a standard modality. However, direct contact of electrodes with skin, and signal corruption due to sweat and the daily-activity induced motion limits the use of conventional ECG in wearable-sensor based long term health monitoring devices. Accelerometers are light-weight, inexpensive and can be used simultaneously in multiple applications. Therefore, we propose an approach for generating pseudo-ECG from a chest-worn accelerometer. First a non-linear regression is identified between the chest surface acceleration and the conventional ECG signals. Based on the identified regression model, a pseudo-ECG signal is generated from the chest acceleration signal. The proposed method was validated on 15 trials data recorded from 3 human subjects. Results show that the generated ECG matches well with the real ECG with an average correlation coefficient of 0.8.",

keywords = "Cardiac Health Monitoring, Personalized Monitoring, Pseudo-ECG, Wearable Accelerometer, Wearable Sensors",

author = "Veluvolu, \{Kalyana Chakaravarthy\}",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 International Conference on Data Science and Communication, IconDSC 2019 ; Conference date: 01-03-2019 Through 02-03-2019",

year = "2019",

month = mar,

doi = "10.1109/IconDSC.2019.8817000",

language = "English",

series = "2019 International Conference on Data Science and Communication, IconDSC 2019",

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

booktitle = "2019 International Conference on Data Science and Communication, IconDSC 2019",

address = "United States",

}

Veluvolu, KC 2019, Wearable Accelerometer based Pseudo-ECG Generation. in 2019 International Conference on Data Science and Communication, IconDSC 2019., 8817000, 2019 International Conference on Data Science and Communication, IconDSC 2019, Institute of Electrical and Electronics Engineers Inc., 2019 International Conference on Data Science and Communication, IconDSC 2019, Bangalore, India, 1/03/19. https://doi.org/10.1109/IconDSC.2019.8817000

Wearable Accelerometer based Pseudo-ECG Generation. / Veluvolu, Kalyana Chakaravarthy.
2019 International Conference on Data Science and Communication, IconDSC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 8817000 (2019 International Conference on Data Science and Communication, IconDSC 2019).

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

TY - GEN

T1 - Wearable Accelerometer based Pseudo-ECG Generation

AU - Veluvolu, Kalyana Chakaravarthy

PY - 2019/3

Y1 - 2019/3

N2 - For monitoring and diagnostics of the cardiovascular health, Electrocardiogram (ECG)is considered as a standard modality. However, direct contact of electrodes with skin, and signal corruption due to sweat and the daily-activity induced motion limits the use of conventional ECG in wearable-sensor based long term health monitoring devices. Accelerometers are light-weight, inexpensive and can be used simultaneously in multiple applications. Therefore, we propose an approach for generating pseudo-ECG from a chest-worn accelerometer. First a non-linear regression is identified between the chest surface acceleration and the conventional ECG signals. Based on the identified regression model, a pseudo-ECG signal is generated from the chest acceleration signal. The proposed method was validated on 15 trials data recorded from 3 human subjects. Results show that the generated ECG matches well with the real ECG with an average correlation coefficient of 0.8.

AB - For monitoring and diagnostics of the cardiovascular health, Electrocardiogram (ECG)is considered as a standard modality. However, direct contact of electrodes with skin, and signal corruption due to sweat and the daily-activity induced motion limits the use of conventional ECG in wearable-sensor based long term health monitoring devices. Accelerometers are light-weight, inexpensive and can be used simultaneously in multiple applications. Therefore, we propose an approach for generating pseudo-ECG from a chest-worn accelerometer. First a non-linear regression is identified between the chest surface acceleration and the conventional ECG signals. Based on the identified regression model, a pseudo-ECG signal is generated from the chest acceleration signal. The proposed method was validated on 15 trials data recorded from 3 human subjects. Results show that the generated ECG matches well with the real ECG with an average correlation coefficient of 0.8.

KW - Cardiac Health Monitoring

KW - Personalized Monitoring

KW - Pseudo-ECG

KW - Wearable Accelerometer

KW - Wearable Sensors

UR - https://www.scopus.com/pages/publications/85072781391

U2 - 10.1109/IconDSC.2019.8817000

DO - 10.1109/IconDSC.2019.8817000

M3 - Conference contribution

AN - SCOPUS:85072781391

T3 - 2019 International Conference on Data Science and Communication, IconDSC 2019

BT - 2019 International Conference on Data Science and Communication, IconDSC 2019

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 International Conference on Data Science and Communication, IconDSC 2019

Y2 - 1 March 2019 through 2 March 2019

ER -

Wearable Accelerometer based Pseudo-ECG Generation

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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