Abstract 4112524: Novel Contactless and AI-Based Method Can Determine Heart Rate and Cardiac-Induced Vibrations of Chest

Introduction: Seismocardiography (SCG) - measurements of cardiovascular-induced vibrations on the chest - has shown potential for providing clinical information for cardiac conditions. SCG is conventionally recorded by an accelerometer attached to a single point on chest. Recent research suggests multichannel SCG (mSCG) - measurements from multiple chest locations - can provide extra and more accurate clinical information. Current mSCG methods are limited to accelerometer arrays, laser Doppler vibrometry, and airborne ultrasound that are either costly, difficult for inexperienced users, or need bulky equipment, thereby impeding their use beyond research or clinical settings. Hypothesis: mSCG signals can be accurately estimated from tiny chest movements in chest videos recorded by ordinary cameras, e.g., those in smartphones. Methods: We enrolled 10 subjects (sbjs) with no history of CVDs (21.7 ± 1.7 years, 40% women). ECG and chest video of sbjs were recorded at rest for 15 sec during breath hold at the end of inhalation followed by another 15 sec recording during breath hold at the end of exhalation. We developed an AI-powered mobile app to record the chest videos and convert them to 0-30 Hz mSCG in right-to-left (RL) and head-to-foot (HF) directions (Fig 1a). Heart rate (HR) based on ECG RR interval and mSCG was measured and compared. Results: HR estimated from mSCG in both RL and HF directions had a good agreement with ECG-based HR using Bland-Altman analysis [RL: bias = 1.4 bpm, 95% CI = 5.6 bpm; HF: bias = 0.8 bpm, 95% CI = 6.2 bpm (Fig 1b)]. High-quality mSCG and ECG measurements were obtained for all sbjs. Conclusion: Clinically relevant information can be accurately extracted from chest videos using our novel, contactless, AI-based method. Given that the vast majority of Americans have access to a camera phone, future developments of this method may provide new means of remote and accessible cardiac monitoring.

Authors

Institutions

Publication Details

Journal
Circulation
Published
2024-11-12
DOI
https://doi.org/10.1161/circ.150.suppl_1.4112524
Primary Topic
Non-Invasive Vital Sign Monitoring
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Abstract 4112524: Novel Contactless and AI-Based Method Can Determine Heart Rate and Cardiac-Induced Vibrations of Chest

Mohammad Muntasir Rahman, Amirtahà Taebi, Faezeh Taghva
Circulation
Non-Invasive Vital Sign Monitoring
article

Abstract 4112524: Novel Contactless and AI-Based Method Can Determine Heart Rate and Cardiac-Induced Vibrations of Chest

Mohammad Muntasir Rahman, Amirtahà Taebi, Faezeh Taghva
article en

Abstract

Introduction: Seismocardiography (SCG) - measurements of cardiovascular-induced vibrations on the chest - has shown potential for providing clinical information for cardiac conditions. SCG is conventionally recorded by an accelerometer attached to a single point on chest. Recent research suggests multichannel SCG (mSCG) - measurements from multiple chest locations - can provide extra and more accurate clinical information. Current mSCG methods are limited to accelerometer arrays, laser Doppler vibrometry, and airborne ultrasound that are either costly, difficult for inexperienced users, or need bulky equipment, thereby impeding their use beyond research or clinical settings. Hypothesis: mSCG signals can be accurately estimated from tiny chest movements in chest videos recorded by ordinary cameras, e.g., those in smartphones. Methods: We enrolled 10 subjects (sbjs) with no history of CVDs (21.7 ± 1.7 years, 40% women). ECG and chest video of sbjs were recorded at rest for 15 sec during breath hold at the end of inhalation followed by another 15 sec recording during breath hold at the end of exhalation. We developed an AI-powered mobile app to record the chest videos and convert them to 0-30 Hz mSCG in right-to-left (RL) and head-to-foot (HF) directions (Fig 1a). Heart rate (HR) based on ECG RR interval and mSCG was measured and compared. Results: HR estimated from mSCG in both RL and HF directions had a good agreement with ECG-based HR using Bland-Altman analysis [RL: bias = 1.4 bpm, 95% CI = 5.6 bpm; HF: bias = 0.8 bpm, 95% CI = 6.2 bpm (Fig 1b)]. High-quality mSCG and ECG measurements were obtained for all sbjs. Conclusion: Clinically relevant information can be accurately extracted from chest videos using our novel, contactless, AI-based method. Given that the vast majority of Americans have access to a camera phone, future developments of this method may provide new means of remote and accessible cardiac monitoring.

CirculationVol. 150(Suppl_1)
University of California, Berkeley (US), Mississippi State University (US)
Good health and well-being
Openalex Percentile: Top 81%
Non-Invasive Vital Sign Monitoring
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.