EEG-Based Pathology Detection for Home Health Monitoring
Abstract
An electroencephalogram (EEG)-based remote pathology detection system is proposed in this study. The system uses a deep convolutional network consisting of 1D and 2D convolutions. Features from different convolutional layers are fused using a fusion network. Various types of networks are investigated; the types include a multilayer perceptron (MLP) with a varying number of hidden layers, and an autoencoder. Experiments are done using a publicly available EEG signal database that contains two classes: normal and abnormal. The experimental results demonstrate that the proposed system achieves greater than 89% accuracy using the convolutional network followed by the MLP with two hidden layers. The proposed system is also evaluated in a cloud-based framework, and its performance is found to be comparable with the performance obtained using only a local server.
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In today's healthcare systems, a huge number of inbound and outbound networks must be managed, resulting in a problem with supporting a large number of high-speed wireless systems without causing system speed degradation.
“In today’s healthcare systems, a huge number of inbound and outbound networks must be managed. As a result, there is a problem with supporting a large number of high-speed wireless systems without causing system speed degradation [206].”
From Federated learning-based AI approaches in smart healthcare: Concepts, taxonomies, challenges and open issues (2023) · cites this paper as [206]
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- Authors
- Muhammad, G., Hossain, M. S., & Kumar, N.
- Affiliations
- King Saud University (SA)
- Thapar Institute of Engineering & Technology (IN)
- Venue
- IEEE Journal on Selected Areas in Communications
- Published
- 2021-02
- Volume
- 39
- Issue
- 2
- Pages
- 603-610
- ISSN
- 0733-8716 (ISSN-L)
- 1558-0008
- OpenAlex
- W3081817842
- Cited by
- 148
- References
- 34
- Citations by year
- 2026: 3 · 2025: 17 · 2024: 16 · 2023: 27 · 2022: 28 · 2021: 51 · 2020: 5
- FWCI
- 14.94
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Journal on Selected Areas in Communications (publishedVersion)
- Funders
- Deanship of Scientific Research at King Saud University, Riyadh, Saudi Arabia for funding through the Research Group (RG-1436-023)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- EEG, home health monitoring, convolutional neural networks, feature fusion, multilayer perceptron, autoencoder, cloud-based healthcare
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