# Automatic Diagnosis of Schizophrenia in EEG Signals Using CNN-LSTM Models

arXiv: https://arxiv.org/abs/2109.01120 · HTML: https://mlsanity.com/paper/2109.01120/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 75 references that are in the map.
- First posted on arXiv 2 Sep 2021.

## Builds on (most cited first, 9 of 9)

- [Learning Convolutional Neural Networks for Graphs](https://mlsanity.com/paper/1605.05273/) (2016)
- [Epileptic Seizures Detection Using Deep Learning Techniques: A Review](https://mlsanity.com/paper/2007.01276/) (2020)
- [Deep Learning for Neuroimaging-based Diagnosis and Rehabilitation of Autism Spectrum Disorder: A Review](https://mlsanity.com/paper/2007.01285/) (2020)
- [An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future works](https://mlsanity.com/paper/2103.03081/) (2021)
- [Handling of uncertainty in medical data using machine learning and probability theory techniques: A review of 30 years (1991-2020)](https://mlsanity.com/paper/2008.10114/) (2020)
- [Classification of EEG-Based Brain Connectivity Networks in Schizophrenia Using a Multi-Domain Connectome Convolutional Neural Network](https://mlsanity.com/paper/1903.08858/) (2019)
- [Individual Recognition in Schizophrenia using Deep Learning Methods with Random Forest and Voting Classifiers: Insights from Resting State EEG Streams](https://mlsanity.com/paper/1707.03467/) (2017)
- [Deep Learning in current Neuroimaging: a multivariate approach with power and type I error control but arguable generalization ability](https://mlsanity.com/paper/2103.16685/) (2021)
- [A connection between the pattern classification problem and the General Linear Model for statistical inference](https://mlsanity.com/paper/2012.08903/) (2020)

## Built on by (most cited first, 13 of 13)

- [Detection of Epileptic Seizures on EEG Signals Using ANFIS Classifier, Autoencoders and Fuzzy Entropies](https://mlsanity.com/paper/2109.04364/) (2021)
- [Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review](https://mlsanity.com/paper/2206.11233/) (2022)
- [Automatic diagnosis of schizophrenia and attention deficit hyperactivity disorder in rs-fMRI modality using convolutional autoencoder model and interval type-2 fuzzy regression](https://mlsanity.com/paper/2205.15858/) (2022)
- [An Explainable Deep Learning-Based Method For Schizophrenia Diagnosis Using Generative Data-Augmentation](https://mlsanity.com/paper/2310.16867/) (2023)
- [Deep Learning-Powered Electrical Brain Signals Analysis: Advancing Neurological Diagnostics](https://mlsanity.com/paper/2502.17213/) (2025)
- [Automatic Diagnosis of Myocarditis Disease in Cardiac MRI Modality using Deep Transformers and Explainable Artificial Intelligence](https://mlsanity.com/paper/2210.14611/) (2022)
- [A Comparative Study of Conventional and Tripolar EEG for High-Performance Reach-to-Grasp BCI Systems](https://mlsanity.com/paper/2402.09448/) (2024)
- [Automated Diagnosis of Cardiovascular Diseases from Cardiac Magnetic Resonance Imaging Using Deep Learning Models: A Review](https://mlsanity.com/paper/2210.14909/) (2022)
- [Automated Huntington's Disease Prognosis via Biomedical Signals and Shallow Machine Learning](https://mlsanity.com/paper/2302.03605/) (2023)
- [Empowering Precision Medicine: AI-Driven Schizophrenia Diagnosis via EEG Signals: A Comprehensive Review from 2002-2023](https://mlsanity.com/paper/2309.12202/) (2023)
- [ChronoPscychosis: Temporal Segmentation and Its Impact on Schizophrenia Classification Using Motor Activity Data](https://mlsanity.com/paper/2311.12590/) (2023)
- [From Epilepsy Seizures Classification to Detection: A Deep Learning-based Approach for Raw EEG Signals](https://mlsanity.com/paper/2410.03385/) (2024)
- [ChatImage: Navigating Long-Form LLM Answers through Interactive Images](https://mlsanity.com/paper/2607.05290/) (2026)

## Abstract (arXiv)

Schizophrenia (SZ) is a mental disorder whereby due to the secretion of specific chemicals in the brain, the function of some brain regions is out of balance, leading to the lack of coordination between thoughts, actions, and emotions. This study provides various intelligent deep learning (DL)-based methods for automated SZ diagnosis via electroencephalography (EEG) signals. The obtained results are compared with those of conventional intelligent methods. To implement the proposed methods, the dataset of the Institute of Psychiatry and Neurology in Warsaw, Poland, has been used. First, EEG signals were divided into 25 s time frames and then were normalized by z-score or norm L2. In the classification step, two different approaches were considered for SZ diagnosis via EEG signals. In this step, the classification of EEG signals was first carried out by conventional machine learning methods, e.g., support vector machine, k-nearest neighbors, decision tree, na"ive Bayes, random forest, extremely randomized trees, and bagging. Various proposed DL models, namely, long short-term memories (LSTMs), one-dimensional convolutional networks (1D-CNNs), and 1D-CNN-LSTMs, were used in the following. In this step, the DL models were implemented and compared with different activation functions. Among the proposed DL models, the CNN-LSTM architecture has had the best performance. In this architecture, the ReLU activation function with the z-score and L2-combined normalization was used. The proposed CNN-LSTM model has achieved an accuracy percentage of 99.25%, better than the results of most former studies in this field. It is worth mentioning that to perform all simulations, the k-fold cross-validation method with k = 5 has been used.