Automated depression detection via cloud based EEG analysis with transfer learning and synchrosqueezed wavelet transform

• Published in: Scientific reports Vol. 15; no. 1; pp. 18008 - 19
• Main Authors: Bagherzadeh, Sara, Norouzi, Mohammad Reza, Ghasri, Amirhesam, Tolou Kouroshi, Pouya, Bahri Hampa, Sepideh, Farokhshad, Fatemeh, Shalbaf, Ahmad
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 23.05.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166/985; 692/617/375; Adult; Cloud Computing; Convolutional neural network (CNN); COVID-19; Deep Learning; Deep learning (DL); Depression - diagnosis; Depression detection; Depressive Disorder, Major - diagnosis; Diagnosis, Computer-Assisted - methods; EEG; Electrodes; Electroencephalogram (EEG); Electroencephalography; Electroencephalography - methods; Female; Humanities and Social Sciences; Humans; Image processing; Male; Mental depression; Middle Aged; multidisciplinary; Neural networks; Neural Networks, Computer; SARS-CoV-2; Science; Science (multidisciplinary); Synchrosqueezing wavelet transform (SSWT); Transfer learning; Wavelet Analysis; Wavelet transforms; Depression detection; Convolutional neural network (CNN); Electroencephalogram (EEG); Deep learning (DL); Synchrosqueezing wavelet transform (SSWT)

Post-COVID-19, depression rates have risen sharply, increasing the need for early diagnosis using electroencephalogram (EEG) and deep learning. To tackle this, we developed a cloud-based computer-aided depression diagnostic (CCADD) system that utilizes EEG signals from local databases. This system w...