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arXiv · 2201.11320

Deep Recurrent Learning for Heart Sounds Segmentation based on Instantaneous Frequency Features

Alvaro Joaquín Gaona·Pedro David Arini
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Abstract

In this work, a novel stack of well-known technologies is presented to determine an automatic method to segment the heart sounds in a phonocardiogram (PCG). We will show a deep recurrent neural network (DRNN) capable of segmenting a PCG into its main components and a very specific way of extracting instantaneous frequency that will play an important role in the training and testing of the proposed model. More specifically, it involves a Long Short-Term Memory (LSTM) neural network accompanied by the Fourier Synchrosqueezed Transform (FSST) used to extract instantaneous time-frequency features from a PCG. The present approach was tested on heart sound signals longer than 5 seconds and shorter than 35 seconds from freely-available databases. This approach proved that, with a relatively small architecture, a small set of data, and the right features, this method achieved an almost state-of-the-art performance, showing an average sensitivity of 89.5%, an average positive predictive value of 89.3\% and an average accuracy of 91.3%.

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BibTeXRIS

Alvaro Joaquín Gaona, Pedro David Arini. 2022-01-27. Deep Recurrent Learning for Heart Sounds Segmentation based on Instantaneous Frequency Features. https://doi.org/10.37537/rev.elektron.4.2.101.2020

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