24 February 2015
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, A0650D, Data gathering, processing, and recording, data displays including digital techniques, A8745H, Haemodynamics, pneumodynamics, A8760B, Sonic and ultrasonic radiation (medical uses), A8770E, Patient diagnostic methods and instrumentation, B6140, Signal processing and detection, B6250K, Wireless sensor networks, B7210G, Data acquisition systems, B7230, Sensing devices and transducers, B7510H, Sonic and ultrasonic radiation (biomedical imaging/measurement), B7810C, Sonic and ultrasonic transducers and sensors, B7820, Sonic and ultrasonic applications, C5260, Digital signal processing, C5520, Data acquisition equipment and techniques, C7330, Biology and medical computing, A0670D, Sensing and detecting devices
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.