Detection of the first heart sound using fibre-optic interferometric measurements and neural networks

Fiber-optic interferometry is used to measure subtle changes of the optical fibre length. It has been shown that in this way also the heart activity can be detected if the fibre is in direct or indirect contact with human body. The measured interferometric signal must be first demodulated and band-pass filtered to separate superimposed contributions of signal components. Only then their detection and classification is feasible. In this paper, we deploy feedforward neural network for detecting the first heart sound (S1) from fibre-optic interferometric signals. A reliable and robust classification of S1 and finding its location in time importantly support diagnosing of cardiac arrhythmias and valve abnormalities. Our experimental results on a group of ten healthy subjects that underwent submaximal stress testing before fibre-optic measurements yield 98.2±1.5% and 98.4±0.9% for sensitivity and precision of S1 detection, respectively.