ArticlePhysical and engineering sciences in medicine2022
A deep learning approach in automated detection of schizophrenia using scalogram images of EEG signals.
Article in Physical and engineering sciences in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of EEG based automated schizophrenia classification through machine learning and deep learning.Frontiers in human neuroscience · 2024Pooled it
- OGTCN-E-MGO: an optimized deep learning framework for EEG-based schizophrenia detection.Physical and engineering sciences in medicine · 2026Article
- Fusing EEG Features Extracted by Microstate Analysis and Empirical Mode Decomposition for Diagnosis of Schizophrenia.Sensors (Basel, Switzerland) · 2026Article
- Quantitative electroencephalography as a potential neurophysiological diagnostic biomarker of schizophrenia and first-episode psychosis: a systematic review of clinical implications.Frontiers in psychiatry · 2026Review
- Transformer architecture for diagnosing schizophrenia disabilities through EEG analysis.Frontiers in physiology · 2026Article
- Exploring brain lobe-specific insights in an explainable framework for EEG-based schizophrenia detection.PloS one · 2026Article
- AI-driven early diagnosis of specific mental disorders: a comprehensive study.Cognitive neurodynamics · 2025Article
- Enhanced hybrid deep neural network for EEG-based schizophrenia diagnosis using functional and temporal features.Scientific reports · 2025Article
- Exploring brain lobe-specific insights in an explainable framework for EEG-based schizophrenia detection.bioRxiv : the preprint server for biology · 2025Article
- Diagnosis of Schizophrenia Using Feature Extraction from EEG Signals Based on Markov Transition Fields and Deep Learning.Biomimetics (Basel, Switzerland) · 2025Article
- Schizophrenia detection from electroencephalogram signals using image encoding and wrapper-based deep feature selection approach.Scientific reports · 2025Article
- Transfer learning and self-distillation for automated detection of schizophrenia using single-channel EEG and scalogram images.Physical and engineering sciences in medicine · 2025Article
- SchizoLMNet: a modified lightweight MobileNetV2- architecture for automated schizophrenia detection using EEG-derived spectrograms.Physical and engineering sciences in medicine · 2025Article
- Schizophrenia Detection and Classification: A Systematic Review of the Last Decade.Diagnostics (Basel, Switzerland) · 2024Review
- Multiresolution feature fusion for smart diagnosis of schizophrenia in adolescents using EEG signals.Cognitive neurodynamics · 2024Article
- EEG-based schizophrenia detection using fusion of effective connectivity maps and convolutional neural networks with transfer learning.Cognitive neurodynamics · 2024Article
- A deep learning approach for diagnosis of schizophrenia disorder via data augmentation based on convolutional neural network and long short-term memory.Biomedical engineering letters · 2024Article
- Quantum Machine-Based Decision Support System for the Detection of Schizophrenia from EEG Records.Journal of medical systems · 2024Article
- Classification of Targets and Distractors in an Audiovisual Attention Task Based on Electroencephalography.Sensors (Basel, Switzerland) · 2023Article
- Automatic Detection and Classification of Epileptic Seizures from EEG Data: Finding Optimal Acquisition Settings and Testing Interpretable Machine Learning Approach.Biomedicines · 2023Article
Corrections and comments
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Authors and funding
2 authors.
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Abstract
This study presents a method with high accuracy performance that aims to automatically detect schizophrenia (SZ) from electroencephalography (EEG) records. Unlike related literature studies using traditional machine learning algorithms, the features required for the training of the network are automatically extracted from the EEG records in our method. In order to obtain the time frequency features of the EEG signals, the signal was converted into 2D by using the Continuous Wavelet Transform method. This study has the highest accuracy performance in the relevant literature by using 2D time frequency features in automatic detection of SZ disease. It is trained with Visual Geometry Group-16 (VGG16), an advanced convolutional neural networks (CNN) deep learning network architecture, to extract key features found on scalogram images and train the network. The study shows a high success in classifying SZ patients and healthy individuals with a very satisfactory accuracy of 98% and 99.5%, respectively, using two different datasets consisting of individuals from different age groups. Using different techniques [Activization Maximization, Saliency Map, and Gradient-weighted Class Activation Mapping (Grad-CAM)] to visualize the learning outcomes of the CNN network, the relationship of frequency components between SZ and the healthy individual is clearly shown. Moreover, with these interpretable outcomes, the difference between SZ patients and healthy individuals can be distinguished very easily help for expert opinion.
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