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

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 automate...