Automatic detection of abnormal EEG signals using multiscale features with ensemble learning

Electroencephalogram (EEG) is an economical and convenient auxiliary test to aid in the diagnosis and analysis of brain-related neurological diseases. In recent years, machine learning has shown great potential in clinical EEG abnormality detection. However, existing methods usually fail to consider...

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Bibliographic Details
Published in:Frontiers in human neuroscience 2022-09, Vol.16, p.943258-943258
Main Authors: Wu, Tao, Kong, Xiangzeng, Zhong, Yunning, Chen, Lifei
Format: Article
Language:English
Subjects:
Age
Automation
Classification
Datasets
discrete wavelet transform
EEG
Electroencephalography
ensemble learning
Experiments
Machine learning
multi-scale aggregation
Neurological diseases
Neuroscience
Pathology
Patients
Performance evaluation
Wavelet transforms
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Summary:Electroencephalogram (EEG) is an economical and convenient auxiliary test to aid in the diagnosis and analysis of brain-related neurological diseases. In recent years, machine learning has shown great potential in clinical EEG abnormality detection. However, existing methods usually fail to consider the issue of feature redundancy when extracting the relevant EEG features. In addition, the importance of utilizing the patient age information in EEG detection is ignored. In this paper, a new framework is proposed for distinguishing an unknown EEG recording as either normal or abnormal by identifying different types of EEG-derived significant features. In the proposed framework, different hierarchical salient features are extracted using a time-wise multi-scale aggregation strategy, based on a selected group of statistical characteristics calculated from the optimum discrete wavelet transform coefficients. We also fuse the age information with multi-scale features for further improving discrimination. The integrated features are classified using three ensemble learning classifiers, CatBoost, LightGBM, and random forest. Experimental results show that our method with CatBoost classifier can yield superior performance vis-a-vis competing techniques, which indicates the great promise of our methodology in EEG pathology detection.
ISSN:1662-5161
1662-5161
DOI:10.3389/fnhum.2022.943258