Fusing convolutional learning and attention-based Bi-LSTM networks for early Alzheimer’s diagnosis from EEG signals towards IoMT

Fusing convolutional learning and attention-based Bi-LSTM networks for early Alzheimer’s diagnosis from EEG signals towards IoMT

Abstract The Internet of Medical Things (IoMT) is poised to play a pivotal role in future medical support systems, enabling pervasive health monitoring in smart cities. Alzheimer’s disease (AD) afflicts millions globally, and this paper explores the potential of electroencephalogram (EEG) data in ad...

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Bibliographic Details
Main Authors: Mohamadreza Khosravi, Hossein Parsaei, Khosro Rezaee, Mohammad Sadegh Helfroush
Format: Article
Language:English
Published: Nature Portfolio 2024-10-01
Series:Scientific Reports
Subjects:
Alzheimer’s disease
Attention-time-aware
EEG signals
BiLSTM
Convolutional learning
Deep learning
Online Access:https://doi.org/10.1038/s41598-024-77876-8

Internet

https://doi.org/10.1038/s41598-024-77876-8

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