title: Detection of S1 and S2 heart sounds by high frequency signatures.
abstract: A new unsupervised and low complexity method for detection of S1 and S2 components of heart sound without the ECG reference is described The most reliable and invariant feature applied in current state-of-the-art of unsupervised heart sound segmentation algorithms is implicitly or explicitly the S1-S2 interval regularity. However; this criterion is inherently prone to noise influence and does not appropriately tackle the heart sound segmentation of arrhythmic cases. A solution based upon a high frequency marker; which is extracted from heart sound using the fast wavelet decomposition, is proposed in order to estimate instantaneous heart rate. This marker is physiologically motivated by the accentuated pressure differences found across heart valves, both in native and prosthetic valves, which leads to distinct high frequency signatures of the valve closing sounds. The algorithm has been validated with heart sound samples collected from patients with mechanical and bio prosthetic heart valve implants in different locations, as well as with patients with native valves. This approach exhibits high sensitivity and specificity without being dependent on the valve type nor their implant position. Further more, it exhibits invariance with respect to normal sinus rhythm (NSR) arrhythmias and sound recording location.
authors.affiliation: Centre for Informatics and Systems, University of Coimbra, Portugal. dinesh@dei.uc.pt
authors.forename: D
authors.initials: D
authors.lastname: Kumar
authors.forename: P
authors.initials: P
authors.lastname: Carvalho
authors.forename: M
authors.initials: M
authors.lastname: Antunes
authors.forename: J
authors.initials: J
authors.lastname: Henriques
authors.forename: L
authors.initials: L
authors.lastname: Eugenio
authors.forename: R
authors.initials: R
authors.lastname: Schmidt
authors.forename: J
authors.initials: J
authors.lastname: Habetha
mesh_terms: D000465:Algorithms; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006326:Heart Auscultation / Q000379:methods*; D006349:Heart Valve Diseases / Q000175:diagnosis* / Q000517:prevention & control; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013018:Sound Spectrography / Q000379:methods*