Authors:
Guy Amit
1
;
Jonathan Lessick
2
;
Noam Gavriely
3
and
Nathan Intrator
1
Affiliations:
1
School of Computer Science, Tel-AvivUniversity, Israel
;
2
Rambam Medical Center, Technion-Israel Institute of Technology, Israel
;
3
Technion-Israel Institute of Technology, Israel
Keyword(s):
Heart sounds, time-frequency analysis, feature extraction, cardiac functionality.
Related
Ontology
Subjects/Areas/Topics:
Acoustic Signal Processing
;
Applications and Services
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Medical Image Detection, Acquisition, Analysis and Processing
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
Abstract:
The mechanical processes of the cardiac cycle generate vibratory and acoustic signals that are received on the chest wall. We describe signal processing and feature extraction methods utilizing these signals for continuous
non-invasive monitoring of cardiac systolic function. Vibro-acoustic heart signals were acquired from eleven subjects during a routine pharmacological stress echocardiography test. Principal component analysis, applied to the joint time-frequency distribution of the first heart sound (S1), revealed a pattern of an increase in the spectral energy and the frequency bandwidth of the signal associated with the increase of cardiac contractility during the stress test. Novel acoustic indices of S1 that compactly describe this pattern showed good linear correlation with reference indices of systolic functionality estimated by strain-echocardiography. The acoustic indices may therefore be used to improve monitoring and diagnosis of cardiac systolic dysfunctions.