PCCNN: A CNN classification model integrating EEG time-frequency features for stroke classification

PCCNN: A CNN classification model integrating EEG time-frequency features for stroke classification

Stroke classification is crucial for timely diagnosis and treatment, as it helps differentiate between hemorrhagic and ischemic strokes, which require distinct clinical interventions. This paper proposes a stroke classification method using multi-channel electroencephalography (EEG) data. Unlike sin...

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Bibliographic Details
Main Authors: Teng Wang, Fenglian Li, Jia Yang, Wenhui Jia, Fengyun Hu
Format: Article
Language:English
Published: KeAi Communications Co. Ltd. 2025-01-01
Series:Cognitive Robotics
Subjects:
Stroke
EEG
Empirical mode decomposition
Discrete wavelet transform
Convolutional neural network
Online Access:http://www.sciencedirect.com/science/article/pii/S266724132500014X
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