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Deep Convolutional Neural Network Model for Automated Diagnosis of Schizophrenia Using EEG Signals

Shu Lih Oh, Jahmunah Vicnesh, Edward J Ciaccio, Rajamanickam Yuvaraj, U Rajendra Acharya
2019 Applied Sciences  
A computerized detection system for the diagnosis of Schizophrenia (SZ) using a convolutional neural system is described in this study. Schizophrenia is an anomaly in the brain characterized by behavioral symptoms such as hallucinations and disorganized speech. Electroencephalograms (EEG) indicate brain disorders and are prominently used to study brain diseases. We collected EEG signals from 14 healthy subjects and 14 SZ patients and developed an eleven-layered convolutional neural network
more » ... model to analyze the signals. Conventional machine learning techniques are often laborious and subject to intra-observer variability. Deep learning algorithms that have the ability to automatically extract significant features and classify them are thus employed in this study. Features are extracted automatically at the convolution stage, with the most significant features extracted at the max-pooling stage, and the fully connected layer is utilized to classify the signals. The proposed model generated classification accuracies of 98.07% and 81.26% for non-subject based testing and subject based testing, respectively. The developed model can likely aid clinicians as a diagnostic tool to detect early stages of SZ.
doi:10.3390/app9142870 fatcat:qxado5af45hghgzat4qtelzibi

Automated diagnosis of celiac disease by video capsule endoscopy using DAISY Descriptors

Jahmunah Vicnesh, Joel Koh En Wei, Edward J. Ciaccio, Shu Lih Oh, Govind Bhagat, Suzanne K. Lewis, Peter H. Green, U. Rajendra Acharya
2019 Journal of medical systems  
Celiac disease is a genetically determined disorder of the small intestine, occurring due to an immune response to ingested gluten-containing food. The resulting damage to the small intestinal mucosa hampers nutrient absorption, and is characterized by diarrhea, abdominal pain, and a variety of extra-intestinal manifestations. Invasive and costly methods such as endoscopic biopsy are currently used to diagnose celiac disease. Detection of the disease by histopathologic analysis of biopsies can
more » ... e challenging due to suboptimal sampling. Video capsule images were obtained from celiac patients and controls for comparison and classification. This study exploits the use of DAISY descriptors to project two-dimensional images onto one-dimensional vectors. Shannon entropy is then used to extract features, after which a particle swarm optimization algorithm coupled with normalization is employed to select the 30 best features for classification. Statistical measures of this paradigm were tabulated. The accuracy, positive predictive value, sensitivity and specificity obtained in distinguishing celiac versus control video capsule images were 89.82%, 89.17%, 94.35% and 83.20% respectively, using the 10-fold cross-validation technique. When employing manual methods rather than the automated means described in this study, technical limitations and inconclusive results may hamper diagnosis. Our findings suggest that the computer-aided detection system presented herein can render diagnostic information, and thus may provide clinicians with an important tool to validate a diagnosis of celiac disease.
doi:10.1007/s10916-019-1285-6 pmid:31028562 fatcat:nbyrinlkhbdbzorca2gyup7viu

Automated Detection of Sleep Stages Using Deep Learning Techniques: A Systematic Review of the Last Decade (2010–2020)

Hui Wen Loh, Chui Ping Ooi, Jahmunah Vicnesh, Shu Lih Oh, Oliver Faust, Arkadiusz Gertych, U. Rajendra Acharya
2020 Applied Sciences  
Sleep is vital for one's general well-being, but it is often neglected, which has led to an increase in sleep disorders worldwide. Indicators of sleep disorders, such as sleep interruptions, extreme daytime drowsiness, or snoring, can be detected with sleep analysis. However, sleep analysis relies on visuals conducted by experts, and is susceptible to inter- and intra-observer variabilities. One way to overcome these limitations is to support experts with a programmed diagnostic tool (PDT)
more » ... on artificial intelligence for timely detection of sleep disturbances. Artificial intelligence technology, such as deep learning (DL), ensures that data are fully utilized with low to no information loss during training. This paper provides a comprehensive review of 36 studies, published between March 2013 and August 2020, which employed DL models to analyze overnight polysomnogram (PSG) recordings for the classification of sleep stages. Our analysis shows that more than half of the studies employed convolutional neural networks (CNNs) on electroencephalography (EEG) recordings for sleep stage classification and achieved high performance. Our study also underscores that CNN models, particularly one-dimensional CNN models, are advantageous in yielding higher accuracies for classification. More importantly, we noticed that EEG alone is not sufficient to achieve robust classification results. Future automated detection systems should consider other PSG recordings, such as electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG) signals, along with input from human experts, to achieve the required sleep stage classification robustness. Hence, for DL methods to be fully realized as a practical PDT for sleep stage scoring in clinical applications, inclusion of other PSG recordings, besides EEG recordings, is necessary. In this respect, our report includes methods published in the last decade, underscoring the use of DL models with other PSG recordings, for scoring of sleep stages.
doi:10.3390/app10248963 fatcat:kv2ty7b3bbcjff25avwhqnjffq

Artificial Intelligence Enabled Personalised Assistive Tools to Enhance Education of Children with Neurodevelopmental Disorders—A Review

Prabal Datta Barua, Jahmunah Vicnesh, Raj Gururajan, Shu Lih Oh, Elizabeth Palmer, Muhammad Mokhzaini Azizan, Nahrizul Adib Kadri, U. Rajendra Acharya
2022 International Journal of Environmental Research and Public Health  
Mental disorders (MDs) with onset in childhood or adolescence include neurodevelopmental disorders (NDDs) (intellectual disability and specific learning disabilities, such as dyslexia, attention deficit disorder (ADHD), and autism spectrum disorders (ASD)), as well as a broad range of mental health disorders (MHDs), including anxiety, depressive, stress-related and psychotic disorders. There is a high co-morbidity of NDDs and MHDs. Globally, there have been dramatic increases in the diagnosis
more » ... childhood-onset mental disorders, with a 2- to 3-fold rise in prevalence for several MHDs in the US over the past 20 years. Depending on the type of MD, children often grapple with social and communication deficits and difficulties adapting to changes in their environment, which can impact their ability to learn effectively. To improve outcomes for children, it is important to provide timely and effective interventions. This review summarises the range and effectiveness of AI-assisted tools, developed using machine learning models, which have been applied to address learning challenges in students with a range of NDDs. Our review summarises the evidence that AI tools can be successfully used to improve social interaction and supportive education. Based on the limitations of existing AI tools, we provide recommendations for the development of future AI tools with a focus on providing personalised learning for individuals with NDDs.
doi:10.3390/ijerph19031192 pmid:35162220 pmcid:PMC8835076 fatcat:2mzjgapffngphmllmrt4nhd3e4

Fusion of Higher Order Spectra and Texture Extraction Methods for Automated Stroke Severity Classification with MRI Images

Oliver Faust, Joel En Wei Koh, Vicnesh Jahmunah, Sukant Sabut, Edward J. Ciaccio, Arshad Majid, Ali Ali, Gregory Y. H. Lip, U. Rajendra Acharya
2021 International Journal of Environmental Research and Public Health  
This paper presents a scientific foundation for automated stroke severity classification. We have constructed and assessed a system which extracts diagnostically relevant information from Magnetic Resonance Imaging (MRI) images. The design was based on 267 images that show the brain from individual subjects after stroke. They were labeled as either Lacunar Syndrome (LACS), Partial Anterior Circulation Syndrome (PACS), or Total Anterior Circulation Stroke (TACS). The labels indicate different
more » ... siological processes which manifest themselves in distinct image texture. The processing system was tasked with extracting texture information that could be used to classify a brain MRI image from a stroke survivor into either LACS, PACS, or TACS. We analyzed 6475 features that were obtained with Gray-Level Run Length Matrix (GLRLM), Higher Order Spectra (HOS), as well as a combination of Discrete Wavelet Transform (DWT) and Gray-Level Co-occurrence Matrix (GLCM) methods. The resulting features were ranked based on the p-value extracted with the Analysis Of Variance (ANOVA) algorithm. The ranked features were used to train and test four types of Support Vector Machine (SVM) classification algorithms according to the rules of 10-fold cross-validation. We found that SVM with Radial Basis Function (RBF) kernel achieves: Accuracy (ACC) = 93.62%, SPE
doi:10.3390/ijerph18158059 fatcat:4yqwokktcjaafhvoidy4dqp6zq

Interactive personalized recommendation systems for human health

Ching-Hsien Hsu, Amir H. Alavi, Mianxiong Dong
2021 Journal of Ambient Intelligence and Humanized Computing  
The prime focus of the paper entitled "Accurate Detection of Myocardial Infarction Using Non-Linear Features with ECG Signals" by Chaitra Sridar, Oh Shu Lih, Jahmunah Vicnesh, Joel En Wei Koh, is to detect  ... 
doi:10.1007/s12652-021-02950-2 pmid:33680211 pmcid:PMC7913042 fatcat:mb4na3ph5jagnhwayhvoqajfxe

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

Afshin Shoeibi, Delaram Sadeghi, Parisa Moridian, Navid Ghassemi, Jónathan Heras, Roohallah Alizadehsani, Ali Khadem, Yinan Kong, Saeid Nahavandi, Yu-Dong Zhang, Juan Manuel Gorriz
2021 Frontiers in Neuroinformatics  
Recurrent neural networks (RNNs) are a group of DL models employed in speech recognition (Ogunfunmi et al., 2019) , natural language processing (Deng and Liu, 2018) , and biomedical signal processing (Vicnesh  ...  ., 2019; Jahmunah et al., 2019; Li et al., 2019; Naira and Alamo, 2019; Oh et al., 2019; Phang et al., 2019a,b; Aristizabal et al., 2020; Luo et al., 2020; Prabhakar et al., 2020; Shalbaf et al., 2020;  ... 
doi:10.3389/fninf.2021.777977 pmid:34899226 pmcid:PMC8657145 fatcat:z7l333t5jrbwzilbkbu3efrowi

A novel automated autism spectrum disorder detection system

Shu Lih Oh, V. Jahmunah, N. Arunkumar, Enas W. Abdulhay, Raj Gururajan, Nahrizul Adib, Edward J. Ciaccio, Kang Hao Cheong, U. Rajendra Acharya
2021 Complex & Intelligent Systems  
These features have also been used in a study of congestive heart failure detection by Vicnesh et al. [48] .  ... 
doi:10.1007/s40747-021-00408-8 fatcat:jclmn4cogvbo5emfiibatdydja