Multiresolution feature fusion for smart diagnosis of schizophrenia in adolescents using EEG signals

• Published in: Cognitive neurodynamics Vol. 18; no. 5; pp. 2779 - 2807
• Main Authors: Ranjan, Rakesh, Sahana, Bikash Chandra
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.10.2024; Springer Nature B.V
• Subjects: Accuracy; Adolescents; Algorithms; Artificial Intelligence; Artificial neural networks; Automation; Biochemistry; Biomedical and Life Sciences; Biomedicine; Brain research; Channels; Chronic illnesses; Classification; Cognitive Psychology; Computer Science; Decision making; Deep learning; Diagnosis; Diagnostic systems; EEG; Electroencephalography; Fourier transforms; Machine learning; Mental disorders; Mental health; Neural networks; Neurosciences; Patients; Regression analysis; Research Article; Schizophrenia; Signal analysis; Signal processing; Support vector machines; Teenagers; Wavelet analysis; Wavelet transforms; Empirical mode decomposition; Empirical wavelet transform; Feature fusion; Electroencephalogram; Convolutional neural network; Classification
• ISSN: 1871-4080; 1871-4099
• DOI: 10.1007/s11571-024-10120-1

Numerous studies on early detection of schizophrenia (SZ) have utilized all available channels or employed set of a few time domain or frequency domain features, while a limited number of features may not be sufficient enough to perform diagnosis efficiently. To encounter these problems, an automated diagnosis model is proposed for the efficient diagnosis of schizophrenia symptomatic adolescent subjects from electroencephalogram (EEG) signals via machine intelligence. A publicly accessible EEG dataset featuring 16-channels EEG obtained from 84 adolescents (45 SZ symptomatic and 39 healthy control) is used to demonstrate the work. Initially, the signals are decomposed into sub-bands using two multi-resolution signal analysis methods: Empirical Wavelet Transform and Empirical mode decomposition. 75 unique features from each sub-bands are extracted and the few selective prominent features are applied to machine learning classifiers for optimal sub-band selection. Subsequently, a hybrid model is proposed, combining convolutional neural network (CNN) and ensemble bagged tree, incorporating both deep learning and handcrafted features to perform SZ diagnosis. This innovative model achieved superior classification performance compared to existing methods, offering a promising approach for SZ diagnosis. Furthermore, the study explores the impact of different brain regions and combined regional data in SZ diagnosis comprehensively. Hence, this computer-assisted decision-making model minimizes the limitations of prior studies by providing a more robust and efficient diagnostic system for schizophrenia.