Deep Recurrent Learning for Heart Sounds Segmentation based on
Instantaneous Frequency Features
- URL: http://arxiv.org/abs/2201.11320v1
- Date: Thu, 27 Jan 2022 04:40:09 GMT
- Title: Deep Recurrent Learning for Heart Sounds Segmentation based on
Instantaneous Frequency Features
- Authors: Alvaro Joaqu\'in Gaona, Pedro David Arini
- Abstract summary: We will show a deep recurrent neural network capable of segmenting a PCG into its main components.
The present approach was tested on heart sound signals longer than 5 seconds and shorter than 35 seconds from freely-available databases.
This approach 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%.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- 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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