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 – S1 and S2 – 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 S1 and S2 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.
Highlights
Heart sounds have been a source of information for diagnosis of patients’ conditions since the late 19th century via the use of the stethoscope [1]
The conventional method of analysing heart sounds is known as phonocardiography (PCG) where a microphone, normally placed on the chest, is used to record the sounds, which can be analysed by a doctor
Each heart cycle consists of two major sounds: S1 followed by S2
Summary
Heart sounds have been a source of information for diagnosis of patients’ conditions since the late 19th century via the use of the stethoscope [1]. Liang et al [3] presented a method for heart sound segmentation by detecting peaks from the normalised average Shannon energy of the low-pass filtered input signal. They tested the algorithm using 515 cardiac cycles obtained from 37 subjects and reported sensitivities of 93 and 84% on clean and normal signals, respectively. Popov et al [14] used a different approach involving a piezoelectric sensor placed on the throat to acquire carotid pulse sounds They applied autocorrelation analysis to 20 s recording sections of band-pass filtered input signal for the estimation of heart rate.
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