Unsupervised segmentation of heart sounds

Digitized and recorded heart sounds contain significant diagnostic information, and are easily acquired in clinical practice. Part of a hypothetical diagnostic system is the segmentation subsystem which locates the boundaries between heartbeats. This thesis examines the design and performance of two approaches to segmentation: peak energy detection and sliding window autocorrelation. Emphasis is placed on synchronous detection of the heartbeats, so that subsequent subsystems can superimpose the heartbeats.An experimental database of heart sounds was compiled to assess the performance of the proposed algorithms. 206 sound files were gathered from 4 sources; the database includes 2709 heartbeats and covers a range of sound quality and complexity. A heart rate estimator is presented and tested, yielding a meaningful beat period in 90- 96% of typical heart sounds.The peak energy segmentation algorithm compares the signal energy against a threshold proportional to the signal's local average energy. It correctly segments 64% of the heartbeats from the experimental database, indicating the difficulty of segmentation in general.The novel sliding window autocorrelation segmenter is designed to take advantage of the self consistency of heart sounds. The algorithm operates by calculating multiple local autocorrelations in order to estimate the time offset to the next heart beat. A change in the time offset marks the boundary between heartbeats. It correctly segmented 83% of the heartbeats

Name thesis:degree_name

Master of Applied Science (M.App.Sc.)

Level thesis:degree_level

Master's

Discipline thesis:degree_discipline

Engineering, Systems and Computer

Grantor dc:publisher

Carleton University

Year dc:date.issued

2007

Author dc:creator

• Beirne, Patrick

Statement dc:rights

• Copyright © 2007 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.

Language dc:language.iso

en

DOI

10.22215/etd/2007-08158

OAI identifier oai:identifier

oai:carleton.scholaris.ca:20.500.14718/37290