Abstract
Electrocardiogram (ECG) signals offer rich information for analyzing and understanding the cardiac activity of a person. The continuous monitoring of ECG can help diagnose cardiac disorders, such as arrhythmia, effectively. While many wearable healthcare platforms offer continuous ECG monitoring, these devices are cumbersome in the fact that they need to be continuously attached to the human body, which causes uncomfortableness, and limits their usage when monitoring a person's ECG throughout the night as they sleep. In this work, we propose a fully non-intrusive sensing system for monitoring the ECG of a person while in bed. Specifically, we present Heartquake, a geophone-based sensing system for extracting ECG patterns using heartbeat vibrations that penetrate through the mattress. The cardiac activity-originated vibration patterns are captured on the geophone and sent to a server, where the data is filtered to remove external noise and passed on to a bidirectional long short term memory (Bi-LSTM) deep learning model for ECG waveform extraction. Our experimental results with 21study participants suggest that Heartquake can detect all five ECG peaks (e.g., P, Q, R, S, T) with an average error of as low as 16 msec when participants are stationary on the bed. With additional noise factors, this error shows an increase, but can be mitigated from model personalization to still be sufficient enough as a screening tool to detect urgent situations.
| Original language | English |
|---|---|
| Title of host publication | MobiSys 2019 - Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services |
| Publisher | Association for Computing Machinery, Inc |
| Pages | 568-569 |
| Number of pages | 2 |
| ISBN (Electronic) | 9781450366618 |
| DOIs | |
| Publication status | Published - 2019 Jun 12 |
| Event | 17th ACM International Conference on Mobile Systems, Applications, and Services, MobiSys 2019 - Seoul, Korea, Republic of Duration: 2019 Jun 17 → 2019 Jun 21 |
Publication series
| Name | MobiSys 2019 - Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services |
|---|
Conference
| Conference | 17th ACM International Conference on Mobile Systems, Applications, and Services, MobiSys 2019 |
|---|---|
| Country | Korea, Republic of |
| City | Seoul |
| Period | 19/6/17 → 19/6/21 |
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All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Computer Networks and Communications
Cite this
}
Deep ECG estimation using a bed-attached geophone. / Park, Jae Yeon; Cho, Hyeon; Hwang, Wonjun; Balan, Rajesh Krishna; Ko, Jeong Gil.
MobiSys 2019 - Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services. Association for Computing Machinery, Inc, 2019. p. 568-569 (MobiSys 2019 - Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Deep ECG estimation using a bed-attached geophone
AU - Park, Jae Yeon
AU - Cho, Hyeon
AU - Hwang, Wonjun
AU - Balan, Rajesh Krishna
AU - Ko, Jeong Gil
PY - 2019/6/12
Y1 - 2019/6/12
N2 - Electrocardiogram (ECG) signals offer rich information for analyzing and understanding the cardiac activity of a person. The continuous monitoring of ECG can help diagnose cardiac disorders, such as arrhythmia, effectively. While many wearable healthcare platforms offer continuous ECG monitoring, these devices are cumbersome in the fact that they need to be continuously attached to the human body, which causes uncomfortableness, and limits their usage when monitoring a person's ECG throughout the night as they sleep. In this work, we propose a fully non-intrusive sensing system for monitoring the ECG of a person while in bed. Specifically, we present Heartquake, a geophone-based sensing system for extracting ECG patterns using heartbeat vibrations that penetrate through the mattress. The cardiac activity-originated vibration patterns are captured on the geophone and sent to a server, where the data is filtered to remove external noise and passed on to a bidirectional long short term memory (Bi-LSTM) deep learning model for ECG waveform extraction. Our experimental results with 21study participants suggest that Heartquake can detect all five ECG peaks (e.g., P, Q, R, S, T) with an average error of as low as 16 msec when participants are stationary on the bed. With additional noise factors, this error shows an increase, but can be mitigated from model personalization to still be sufficient enough as a screening tool to detect urgent situations.
AB - Electrocardiogram (ECG) signals offer rich information for analyzing and understanding the cardiac activity of a person. The continuous monitoring of ECG can help diagnose cardiac disorders, such as arrhythmia, effectively. While many wearable healthcare platforms offer continuous ECG monitoring, these devices are cumbersome in the fact that they need to be continuously attached to the human body, which causes uncomfortableness, and limits their usage when monitoring a person's ECG throughout the night as they sleep. In this work, we propose a fully non-intrusive sensing system for monitoring the ECG of a person while in bed. Specifically, we present Heartquake, a geophone-based sensing system for extracting ECG patterns using heartbeat vibrations that penetrate through the mattress. The cardiac activity-originated vibration patterns are captured on the geophone and sent to a server, where the data is filtered to remove external noise and passed on to a bidirectional long short term memory (Bi-LSTM) deep learning model for ECG waveform extraction. Our experimental results with 21study participants suggest that Heartquake can detect all five ECG peaks (e.g., P, Q, R, S, T) with an average error of as low as 16 msec when participants are stationary on the bed. With additional noise factors, this error shows an increase, but can be mitigated from model personalization to still be sufficient enough as a screening tool to detect urgent situations.
UR - http://www.scopus.com/inward/record.url?scp=85069221880&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85069221880&partnerID=8YFLogxK
U2 - 10.1145/3307334.3328629
DO - 10.1145/3307334.3328629
M3 - Conference contribution
AN - SCOPUS:85069221880
T3 - MobiSys 2019 - Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services
SP - 568
EP - 569
BT - MobiSys 2019 - Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services
PB - Association for Computing Machinery, Inc
ER -