Algorithm for heart rate extraction in a novel wearable acoustic sensor. Issue 1 (24th February 2015)

• Record Type: Journal Article
• Authors: Chen, Guangwei; Imtiaz, Syed Anas; Aguilar–Pelaez, Eduardo; Rodriguez–Villegas, Esther
• Is Part Of: Healthcare technology letters. Volume 2:Issue 1(2015)
• Page Start: 28
• Page End: 33
• Publication Date: 2015-02-24
• Subjects: phonocardiography -- feature extraction -- body sensor networks -- acoustic transducers -- biomedical transducers -- medical signal processing -- acoustic signal processing -- pneumodynamics -- patient monitoring -- data acquisition -- signal classification; heart rate extraction algorithm -- novel wearable acoustic sensor -- phonocardiography -- heart sound listening -- cardiac abnormalities -- heart cycle -- acoustic signal acquisition -- S1 heart sound detection -- S2 heart sound detection -- heart rate extraction -- signal acquisition -- commercial devices -- data acquisition -- dataset -- acoustic heart sound classification -- breathing monitoring -- long‐term wearable vital signs monitoring; Biomedical engineering -- Periodicals; Medical technology -- Periodicals; 610.28
• DOI: 10.1049/htl.2014.0095
• Languages: English
• ISSNs: 2053-3713

Abstract

Abstract : Phonocardiography is a widely used method of listening to the heart sounds and indicating the presence of cardiac abnormalities. Each heart cycle consists of two major sounds – S 1 and S 2 – that can be used to determine the heart rate. The conventional method of acoustic signal acquisition involves placing the sound sensor at the chest where this sound is most audible. Presented is a novel algorithm for the detection of S 1 and S 2 heart sounds and the use of them to extract the heart rate from signals acquired by a small sensor placed at the neck. This algorithm achieves an accuracy of 90.73 and 90.69%, with respect to heart rate value provided by two commercial devices, evaluated on more than 38 h of data acquired from ten different subjects during sleep in a pilot clinical study. This is the largest dataset for acoustic heart sound classification and heart rate extraction in the literature to date. The algorithm in this study used signals from a sensor designed to monitor breathing. This shows that the same sensor and signal can be used to monitor both breathing and heart rate, making it highly useful for long‐term wearable vital signs monitoring.