# Deep learning models for segmenting phonocardiogram signals: a comparative study

## TL;DR

This paper compares deep learning models for segmenting heart sound signals, achieving high accuracy on multiple datasets.

## Contribution

The study is the first to apply deep learning models to the CirCor DigiScope dataset for PCG signal segmentation.

## Key findings

- GRU, Bidirectional-GRU, and BILSTM models achieved 97.2% accuracy on the PhysioNet dataset.
- The models achieved 96.98% accuracy on the MITHSDB dataset and 92.5% on the CirCor DigiScope dataset.
- The approach demonstrates high efficiency and reliability for healthcare applications.

## Abstract

Cardiac auscultation requires the mechanical vibrations occurring on the body’s surface, which carries a range of sound frequencies. These sounds are generated by the movement and pulsation of different cardiac structures as they facilitate blood circulation. Subsequently, these sounds are identified as phonocardiogram (PCG). In this research, deep learning models, namely gated recurrent neural Network (GRU), Bidirectional-GRU, and Bi-directional long-term memory (BILSTM) are applied separately to segment four specific regions within the PCG signal, namely S1 (lub sound), the systolic region, S2 (dub sound), and the diastolic region. These models are applied to three well-known datasets: PhysioNet/Computing in Cardiology Challenge 2016, Massachusetts Institute of Technology (MITHSDB), and CirCor DigiScope Phonocardiogram.The PCG signal underwent a series of pre-processing steps, including digital filtering and empirical mode decomposition, after then deep learning algorithms were applied to achieve the highest level of segmentation accuracy. Remarkably, the proposed approach achieved an accuracy of 97.2% for the PhysioNet dataset and 96.98% for the MITHSDB dataset. Notably, this paper represents the first investigation into the segmentation process of the CirCor DigiScop dataset, achieving an accuracy of 92.5%. This study compared the performance of various deep learning models using the aforementioned datasets, demonstrating its efficiency, accuracy, and reliability as a software tool in healthcare settings.