Rao A Prabhakara, Ranjan Rakesh, Sahana Bikash Chandra, Kumar G Prasanna
Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Bowrampet, Hyderabad, Telangana, 500043, India.
Department of Electronics and Communication Engineering, National Institute of Technology Patna, Bihar, 800005, India.
Phys Eng Sci Med. 2025 Mar;48(1):285-299. doi: 10.1007/s13246-024-01512-y. Epub 2025 Jan 6.
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.
精神分裂症（SZ）是一种慢性神经精神障碍，其特征在于认知、感知、社交、情感和行为功能的紊乱。传统的SZ诊断依赖于精神科医生对个体的主观评估，这可能导致偏差、程序冗长以及潜在的错误诊断。这凸显了对SZ进行早期检测和治疗的迫切需求，以便提供及时的支持并将长期影响降至最低。利用脑电图（EEG）信号捕捉大脑活动动态的能力，本文引入了一种新颖的轻量级改进型MobileNetV2架构（SchizoLMNet），用于使用从选定EEG通道数据导出的频谱图图像高效诊断SZ。所提出的方法涉及对从Kaggle数据存储库收集的81名受试者的原始EEG数据进行预处理。应用短时傅里叶变换（STFT）将预处理后的EEG信号转换为频谱图图像，随后进行数据增强。此外，将生成的图像输入深度学习（DL）模型以执行二元分类任务。利用所提出的模型，在留出法、受试者独立测试和受试者依赖测试中，SZ与健康分类的准确率分别达到了98.17%、97.03%和95.55%。与各种预训练的DL模型和现有技术相比，SchizoLMNet模型表现出卓越的性能。所提出的框架将通过移动边缘计算设备进一步转化为实时临床应用。这种创新方法将成为医护人员与患者之间的桥梁，促进智能沟通并协助有效管理SZ。
