Research Article |
Advancing Sleep Stage Classification with EEG Signal Analysis: LSTM Optimization Using Puffer Fish Algorithm and Explainable AI
Author(s): Srinivasa Rao Vemula, Maruthi Vemula, Ghamya Kotapati, Lokesh Sai Kiran Vatsavai, Lakshmi Naga Jayaprada Gavarraju and Ramesh Vatambeti*
Published In : International Journal of Electrical and Electronics Research (IJEER) Volume 12, Issue 2
Publisher : FOREX Publication
Published : 25 June 2024
e-ISSN : 2347-470X
Page(s) : 596-604
Abstract
In this study, we introduce SleepXAI, a Convolutional Neural Network-Conditional Random Field (CNN-CRF) technique for automatic multi-class sleep stage classification from polysomnography data. SleepXAI enhances classification accuracy while ensuring explainability by highlighting crucial signal segments. Leveraging Long Short-Term Memory (LSTM) networks, it effectively categorizes epileptic EEG signals. Continuous Wavelet Transform (CWT) optimizes signal quality by analyzing eigenvalue characteristics and removing noise. Eigenvalues, which are scalar values indicating the scaling effect on eigenvectors during linear transformations, are used to ensure clean and representative EEG signals. The Puffer Fish Optimization Algorithm fine-tunes LSTM parameters, achieving heightened accuracy by reducing trainable parameters. Evaluation on the Sleep-EDF-20, Sleep-EDF-78, and SHHS datasets shows promising results, with regular accuracy ranging from 85% to 89%. The proposed LSTM-PFOA algorithm demonstrates efficacy for autonomous sleep categorization network development, promising improved sleep stage classification accuracy and facilitating comprehensive health monitoring practices.
Keywords: Sleep Stage Classification
, Convolutional neural network
, Continuous Wavelet Transform
, Puffer Fish Optimization Algorithm
.
Srinivasa Rao Vemula, Software Test Analyst Senior, FIS Management Services, Durham, North Carolina 27703-8589, USA; Email: srinivas.vemula@fisglobal.com
Maruthi Vemula, Student, North Carolina School of Science and Mathematics, Durham, North Carolina 27705, USA; Email: vemula24m@ncssm.edu
Ghamya Kotapati, Department of AI & ML, School of Computing, Mohan Babu University, Tirupati 517102, India; Email: ghamyakotapati@gmail.com
Lokesh Sai Kiran Vatsavai, Department of Information Technology, SRKR Engineering College, Bhimavaram 534204, India; Email: lokesh3069@srkrec.ac.in
Lakshmi Naga Jayaprada Gavarraju, Department of Computer Science and Engineering, Malla Reddy College of Engineering and Technology, Hyderabad 500100, India; Email: lakshminagajayaprada.g@mrcet.ac.in
Ramesh Vatambeti*, School of Computer Science and Engineering, VIT-AP University, Vijayawada 522237, India; Email: v2ramesh634@gmail.com
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