Phase-Based Recurrence Analysis of EEG Signals for Enhanced Detection of Major Depressive Disorder
- 24 May 2026
- book chapter
- Published by Springer Nature
- p. 325-340
Abstract
No abstract availableKeywords
This publication has 15 references indexed in Scilit:
- Automated detection of depression using wavelet scattering networksMedical Engineering & Physics, 2024
- A novel EEG-based graph convolution network for depression detection: Incorporating secondary subject partitioning and attention mechanismExpert Systems with Applications, 2023
- Tutorial on Empirical Mode Decomposition: Basis Decomposition and Frequency Adaptive Graduation in Non-Stationary Time SeriesIEEE Access, 2023
- In-phase matrix profile: A novel method for the detection of major depressive disorderBiomedical Signal Processing and Control, 2023
- Analysis of region of interest (RoI) of brain for detection of depression using EEG signalMultimedia Tools and Applications, 2023
- An End-to-End Deep Learning Model for EEG-Based Major Depressive Disorder ClassificationIEEE Access, 2023
- Computer Aided Detection of Major Depressive Disorder (MDD) Using Electroencephalogram SignalsIEEE Access, 2023
- Automated Detection of Major Depressive Disorder With EEG Signals: A Time Series Classification Using Deep LearningIEEE Access, 2022
- Grand challenges in global mental healthNature, 2011
- The Hamilton Depression Rating Scale (HDRS): changes in scores as a function of training and version usedJournal of Affective Disorders, 1991