DeepAI AI Chat
Log In Sign Up

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

by   Alvaro Joaquín Gaona, et al.
University of Buenos Aires

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 predictive value of 89.3% and an average accuracy of 91.3


page 1

page 3


Murmur Detection Using Parallel Recurrent & Convolutional Neural Networks

In this article, we propose a novel technique for classification of the ...

Multi-Language Identification Using Convolutional Recurrent Neural Network

Language Identification, being an important aspect of Automatic Speaker ...

LiteLSTM Architecture for Deep Recurrent Neural Networks

Long short-term memory (LSTM) is a robust recurrent neural network archi...

RETURNN: The RWTH Extensible Training framework for Universal Recurrent Neural Networks

In this work we release our extensible and easily configurable neural ne...

Heart Sound Segmentation using Bidirectional LSTMs with Attention

This paper proposes a novel framework for the segmentation of phonocardi...

A Novel Approach for Earthquake Early Warning System Design using Deep Learning Techniques

Earthquake signals are non-stationary in nature and thus in real-time, i...