A Survey on the Role of Artificial Intelligence in the Prediction and Diagnosis of Schizophrenia (original) (raw)
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Springer, 2022
Computer Aided Diagnosis systems assist radiologists and doctors in the early diagnosis of mental disorders such as Alzheimer's, bipolar disorder, depression, autism, dementia, and schizophrenia using neuroimaging. Advancements in Artificial Intelligence (AI) have leveraged neuroimaging research to unfold numerous techniques for analyzing and interpreting thousands of scans in order to detect and classify various mental illnesses. Schizophrenia is a long-standing psychiatric disorder affecting millions of people worldwide. It causes hallucinations, delusions, and defacement in thinking, behavior, and cognition. Machine Learning and Deep Learning are the subsets of AI which are used for the detection and diagnosis of schizophrenia by gathering insights from different types of modalities. This paper work examines several methods of AI used for the automated diagnosis of schizophrenia using three primary modalities-EEG, structural MRI, and functional MRI. This paper explores different datasets available for schizophrenia along with the techniques and software used to pre-process the EEG and MR images. Further this paper focuses on the different feature extraction and selection techniques to retrieve an appropriate set of features along with the brief overview of machine learning & deep learning approaches. We have also reviewed numerous studies on the prognosis of schizophrenia and presented an exhaustive analysis of the machine learning and deep learning techniques used across EEG and MRI.
Journal of Ambient Intelligence and Humanized Computing
Schizophrenia (SCZ) is a brain disorder where different people experience different symptoms, such as hallucination, delusion, flat-talk, disorganized thinking, etc. In the long term, this can cause severe effects and diminish life expectancy by more than ten years. Therefore, early and accurate diagnosis of SCZ is prevalent, and modalities like structural magnetic resonance imaging (sMRI), functional MRI (fMRI), diffusion tensor imaging (DTI), and electroencephalogram (EEG) assist in witnessing the brain abnormalities of the patients. Moreover, for accurate diagnosis of SCZ, researchers have used machine learning (ML) algorithms for the past decade to distinguish the brain patterns of healthy and SCZ brains using MRI and fMRI images. This paper seeks to acquaint SCZ researchers with ML and to discuss its recent applications to the field of SCZ study. This paper comprehensively reviews state-of-the-art techniques such as ML classifiers, artificial neural network (ANN), deep learning (DL) models, methodological fundamentals, and applications with previous studies. The motivation of this paper is to benefit from finding the research gaps that may lead to the development of a new model for accurate SCZ diagnosis. The paper concludes with the research finding, followed by the future scope that directly contributes to new research directions.
Evaluation of a deep learning model for automated detection of schizophrenia using EEG signals
Advances in Engineering and Intelligence Systems, 2024
Schizophrenia is a brain disorder that disrupts behavioral and cognitive functions such as thinking,perception, and speech. Early diagnosis of schizophrenia plays an important role in treating and limiting the effects of the disease. This research suggests an automated diagnosis system for schizophrenia detection using a deep learning model. For this purpose, EEG signals were captured from 36 patients with schizophrenia and 36 healthy controls at rest. After data preprocessing to reduce noise and artifacts from the EEGs, an 11-layer deep learning model consisting of convolution and LSTM layers with a LeakyReLU activation function and various kernel sizes was implemented to automatically extract and classify features. The proposed deep learning network achieved impressive classification accuracies of 99.33% and 98.49% for 10-fold cross-validation and random splitting methods, respectively. The framework successfully classified schizophrenia patients versus healthy controls with an overall accuracy of 99.33%, sensitivity of 99.26%, specificity of 99.42%,and PPV of 99.50%. This robust end-to-end system is expected to be a valuable diagnostic tool for clinicians and provide significant support in the assessment of schizophrenia due to its automated nature for EEG processing.
Automatic Diagnosis of Schizophrenia in EEG Signals Using CNN-LSTM Models
Frontiers in Neuroinformatics, 2021
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, naïve Bayes, random forest, ...
Automatic Diagnosis of Schizophrenia using EEG Signals and CNN-LSTM Models
2021
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 EEG signals. The obtained results are compared with those of conventional intelligent methods. In order to implement the proposed methods, the dataset of the Institute of Psychiatry and Neurology in Warsaw, Poland, has been used. First, EEG signals are divided into 25-seconds time frames and then were normalized by zscore or norm L2. In the classification step, two different approaches are considered for SZ diagnosis via EEG signals. In this step, the classification of EEG signals is first carried out by conventional DL methods, e.g., KNN, DT, SVM, Bayes, bagging, RF, and ET. Various proposed DL models, including LSTMs, 1D-CNNs, and 1D-CNN-LSTMs, are...
International journal of health sciences
Early detection of individuals susceptible to Schizophrenia (SZ) is critical for early intervention, which can reduce the risk of psychosis. This research proposes a deep learning method for classifying EEG data by picking important discriminative EEG features. While existing systems employ an R-CNN methodology, we propose a hybrid CNN–Bi- LSTM automated system that analyses EEG statistical data and performs the prediction. It uses a CNN for an optimised feature selection process to select the most important and informative features, with Bi-LSTM for prediction of susceptibility to develop SZ. The model when run on EEG data of schizophrenic paradigms gives output as ”Schizophrenic” or ”Non-schizophrenic”. This method has a high level of classification accuracy when compared to most existing machine learning models. While it displays a lower accuracy than some complex deep learning systems, it is much more stable and easy to interpret and thus is more practical for clinical settings.
ArXiv, 2021
Schizophrenia (SZ) is a mental disorder that typically emerges in late adolescence or early adulthood. It reduces the life expectancy of patients by 15 years. Abnormal behavior, perception of emotions, social relationships, and reality perception are among its most significant symptoms. Past studies have revealed the temporal and anterior lobes of hippocampus regions of brain get affected by SZ. Also, increased volume of cerebrospinal fluid (CSF) and decreased volume of white and gray matter can be observed due to this disease. The magnetic resonance imaging (MRI) is the popular neuroimaging technique used to explore structural/functional brain abnormalities in SZ disorder owing to its high spatial resolution. Various artificial intelligence (AI) techniques have been employed ∗Corresponding author Email address: afshin.shoeibi@gmail.com (Afshin Shoeibi) 1Equal contribution Preprint submitted to Elsevier March 5, 2021 ar X iv :2 10 3. 03 08 1v 1 [ cs .L G ] 2 4 Fe b 20 21 with advanc...
Front. Psychiatry 4:95. doi: 10.3389/fpsyt.2013.00095
www.frontiersin.org Citation: Iwabuchi S, Liddle PF and Palaniyappan L(2013) Clinical utility of machine learning approaches in schizophrenia: Improving diagnostic confidence for translational neuroimaging. Front. Psychiatry 4:95. Article URL: http://www.frontiersin.org/Journal/Abstract.aspx?s=764& name=neuropsychiatric%20imaging%20and%20stimulation& ART_DOI=10.3389/fpsyt.2013.00095 (If clicking on the link doesn't work, try copying and pasting it into your browser.)
Detecting Schizophrenia With 3D Structural Brain MRI Using Deep Learning
Schizophrenia is a chronic neuropsychiatric disorder that causes distinct structural alterations within the brain. We hypothesize that deep learning applied to a structural neuroimaging dataset could detect disease-related alteration and improve classification and diagnostic accuracy. We tested this hypothesis using a single, widely available, and conventional T1-weighted MRI scan, from which we extracted the 3D whole-brain structure using standard post-processing methods. A deep learning model was then developed, optimized, and evaluated on three open datasets with T1-weighted MRI scans of patients with schizophrenia. Our proposed model outperformed the benchmark model, which was also trained with structural MR images using a 3D CNN architecture. Our model is capable of almost perfectly (area under the ROC curve = 0.987) distinguishing schizophrenia patients from healthy controls on unseen structural MRI scans. Regional analysis localized subcortical regions and ventricles as the m...