Comparative Study of Brain Signals for Early Detection of Sleep Disorder Using Machine and Deep Learning Algorithm

Santosh Kumar Satapathy, Vanshita Patel, Manan Gandhi, Rajesh Kumar Mohapatra

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Sleep disorders are a common health concern affecting a significant portion of the global population. Early detection and intervention are crucial for preventing the detrimental impact of sleep disorders on an individual's physical and mental health. This study presents a comparative analysis of the effectiveness of machine and deep learning algorithms in the early detection of sleep disorders using brain signals. The research leverages electroencephalogram (EEG) data collected from individuals with varying sleep disorders. We extract relevant features from EEG signals, including spectral, temporal, and spatial features, which provide insight into the brain's activity during different sleep stages. These features serve as input to both machine learning and deep learning models. In the machine learning approach, we employ classical algorithms such as Support Vector Machines (SVM), Random Forest (RF), and K-nearest neighbors (KNN). In the deep learning approach, we use convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to process the EEG data. The study evaluates the performance of these algorithms in terms of accuracy, sensitivity, specificity, and precision while considering different sleep disorder categories, including insomnia, sleep apnea, and narcolepsy. Our findings indicate that deep learning models, particularly CNNs and RNNs, outperform traditional machine learning algorithms in accurately identifying sleep disorders. The deep learning models demonstrate the ability to capture intricate patterns and dependencies within EEG data, leading to more accurate and early detection.

Original languageEnglish
Title of host publication2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024
PublisherInstitute of Electrical and Electronics Engineers
ISBN (Electronic)9798350360523
DOIs
StatePublished - 1 Jan 2024
Externally publishedYes
Event2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024 - Gwalior, India
Duration: 14 Mar 202416 Mar 2024

Publication series

Name2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

Conference

Conference2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024
Country/TerritoryIndia
CityGwalior
Period14/03/2416/03/24

Keywords

  • Deep Learning Algorithms
  • Electroencephalogram (EEG)
  • Machine Learning Algorithms
  • Sleep stages

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Information Systems and Management
  • Management, Monitoring, Policy and Law
  • Control and Optimization
  • Health Informatics
  • Communication

Fingerprint

Dive into the research topics of 'Comparative Study of Brain Signals for Early Detection of Sleep Disorder Using Machine and Deep Learning Algorithm'. Together they form a unique fingerprint.

Cite this