  This paper proposes a novel framework for the segmentation of phonocardiogram
(PCG) signals into heart states, exploiting the temporal evolution of the PCG
as well as considering the salient information that it provides for the
detection of the heart state. We propose the use of recurrent neural networks
and exploit recent advancements in attention based learning to segment the PCG
signal. This allows the network to identify the most salient aspects of the
signal and disregard uninformative information. The proposed method attains
state-of-the-art performance on multiple benchmarks including both human and
animal heart recordings. Furthermore, we empirically analyse different feature
combinations including envelop features, wavelet and Mel Frequency Cepstral
Coefficients (MFCC), and provide quantitative measurements that explore the
importance of different features in the proposed approach. We demonstrate that
a recurrent neural network coupled with attention mechanisms can effectively
learn from irregular and noisy PCG recordings. Our analysis of different
feature combinations shows that MFCC features and their derivatives offer the
best performance compared to classical wavelet and envelop features. Heart
sound segmentation is a crucial pre-processing step for many diagnostic
applications. The proposed method provides a cost effective alternative to
labour extensive manual segmentation, and provides a more accurate segmentation
than existing methods. As such, it can improve the performance of further
analysis including the detection of murmurs and ejection clicks. The proposed
method is also applicable for detection and segmentation of other one
dimensional biomedical signals.
