A Comparison Study on Training Optimization Algorithms in the biLSTM Neural Network for Classification of PCG Signals

EasyChair Preprint 7476

Abstract

A trained neural network classifier is often used to detect cardiac problems by the classification of heart sound signals, also known as phonocardiogram (PCG) signals. The choice of an appropriate training optimization algorithm for such a classification problem, on the other hand, is still being debated. In this paper, we use the bidirectional long short-term memory (biLSTM) network for the classification of sequences of short-time features extracted from labelled PCG signals. The classification performance of four different trained biLSTM models is described in terms of three different optimization algorithms that are used to train the classifier. The elaborated results on testing PCG signals showed that the biLSTM classifier performs better when trained with the stochastic gradient descent with momentum (SGDM) algorithm than when trained with the RMSprop (root mean squared propagation) optimizer or the adaptive moment (ADAM) optimization algorithm. Furthermore, this classification method outperforms a baseline method.

Keyphrases: Adam, BiLSTM model, Feature classification, PCG signals, RMSProp, SGDM, heart sound

@booklet{EasyChair:7476,
  author    = {Mahmoud Fakhry and Abeer Fathallah Brery},
  title     = {A Comparison Study on Training Optimization Algorithms in the biLSTM Neural Network for Classification of PCG Signals},
  howpublished = {EasyChair Preprint 7476},
  year      = {EasyChair, 2022}}