SchizoLMNet: a modified lightweight MobileNetV2- architecture for automated schizophrenia detection using EEG-derived spectrograms

• Published in: Australasian physical & engineering sciences in medicine Vol. 48; no. 1; pp. 285 - 299
• Main Authors: Rao, A. Prabhakara, Ranjan, Rakesh, Sahana, Bikash Chandra, Kumar, G. Prasanna
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.03.2025; Springer Nature B.V
• Subjects: Adult; Automation; Biological and Medical Physics; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Biophysics; Classification; Data augmentation; Deep Learning; Edge computing; Electroencephalography; Female; Fourier Analysis; Fourier transforms; Humans; Machine learning; Male; Medical and Radiation Physics; Medical imaging; Mobile computing; Real time; Schizophrenia; Schizophrenia - diagnosis; Schizophrenia - physiopathology; Scientific Paper; Signal Processing, Computer-Assisted; Spectrograms; Subjective assessment; Pretrained models; Time-frequency representation (TFR); Schizophrenia (SZ); Classification; Electroencephalogram (EEG); MobilNetV2; Spectrogram
• ISSN: 2662-4729; 0158-9938; 2662-4737; 2662-4737; 1879-5447
• DOI: 10.1007/s13246-024-01512-y

Schizophrenia (SZ) is a chronic neuropsychiatric disorder characterized by disturbances in cognitive, perceptual, social, emotional, and behavioral functions. The conventional SZ diagnosis relies on subjective assessments of individuals by psychiatrists, which can result in bias, prolonged procedures, and potentially false diagnoses. This emphasizes the crucial need for early detection and treatment of SZ to provide timely support and minimize long-term impacts. Utilizing the ability of electroencephalogram (EEG) signals to capture brain activity dynamics, this article introduces a novel lightweight modified MobileNetV2- architecture (SchizoLMNet) for efficiently diagnosing SZ using spectrogram images derived from selected EEG channel data. The proposed methodology involves preprocessing of raw EEG data of 81 subjects collected from Kaggle data repository. Short-time Fourier transform (STFT) is applied to transform pre-processed EEG signals into spectrogram images followed by data augmentation. Further, the generated images are subjected to deep learning (DL) models to perform the binary classification task. Utilizing the proposed model, it achieved accuracies of 98.17%, 97.03%, and 95.55% for SZ versus healthy classification in hold-out, subject independent testing, and subject-dependent testing respectively. The SchizoLMNet model demonstrates superior performance compared to various pretrained DL models and state-of-the-art techniques. The proposed framework will be further translated into real-time clinical settings through a mobile edge computing device. This innovative approach will serve as a bridge between medical staff and patients, facilitating intelligent communication and assisting in effective SZ management.