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Sadiq MT, Siuly S, Almogren A, Li Y, Wen P. Efficient novel network and index for alcoholism detection from EEGs. Health Inf Sci Syst 2023; 11:27. [PMID: 37337563 PMCID: PMC10276798 DOI: 10.1007/s13755-023-00227-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Accepted: 05/17/2023] [Indexed: 06/21/2023] Open
Abstract
Background Alcoholism is a catastrophic condition that causes brain damage as well as neurological, social, and behavioral difficulties. Limitations This illness is often assessed using the Cut down, Annoyed, Guilty, and Eye-opener examination technique, which assesses the intensity of an alcohol problem. This technique is protracted, arduous, error-prone, and errant. Method As a result, the intention of this paper is to design a cutting-edge system for automatically identifying alcoholism utilizing electroencephalography (EEG) signals, that can alleviate these problems and aid practitioners and investigators. First, we investigate the feasibility of using the Fast Walsh-Hadamard transform of EEG signals to explore the unpredictable essence and variability of EEG indicators in the suggested framework. Second, thirty-six linear and nonlinear features for deciphering the dynamic pattern of healthy and alcoholic EEG signals are discovered. Subsequently, we suggested a strategy for selecting powerful features. Finally, nineteen machine learning algorithms and five neural network classifiers are used to assess the overall performance of selected attributes. Results The extensive experiments show that the suggested method provides the best classification efficiency, with 97.5% accuracy, 96.7% sensitivity, and 98.3% specificity for the features chosen using the correlation-based FS approach with Recurrent Neural Networks. With recently introduced matrix determinant features, a classification accuracy of 93.3% is also attained. Moreover, we developed a novel index that uses clinically meaningful features to differentiate between healthy and alcoholic categories with a unique integer. This index can assist health care workers, commercial companies, and design engineers in developing a real-time system with 100% classification results for the computerized framework.
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Affiliation(s)
- Muhammad Tariq Sadiq
- Advanced Engineering Centre, School of Architecture, Technology and Engineering, University of Brighton, Brighton, BN2 4AT UK
| | - Siuly Siuly
- Institute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne, 3011 Australia
| | - Ahmad Almogren
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, 11633 Riyadh, Saudi Arabia
| | - Yan Li
- School of Mathematics Physics and Computing, University of Southern Queensland, Toowoomba Campus, Toowoomba, 4350 Australia
| | - Paul Wen
- School of Engineering, University of Southern Queensland, Toowoomba Campus, Toowoomba, 4350 Australia
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Loh HW, Ooi CP, Oh SL, Barua PD, Tan YR, Molinari F, March S, Acharya UR, Fung DSS. Deep neural network technique for automated detection of ADHD and CD using ECG signal. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 241:107775. [PMID: 37651817 DOI: 10.1016/j.cmpb.2023.107775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 08/09/2023] [Accepted: 08/22/2023] [Indexed: 09/02/2023]
Abstract
BACKGROUND AND OBJECTIVE Attention Deficit Hyperactivity problem (ADHD) is a common neurodevelopment problem in children and adolescents that can lead to long-term challenges in life outcomes if left untreated. Also, ADHD is frequently associated with Conduct Disorder (CD), and multiple research have found similarities in clinical signs and behavioral symptoms between both diseases, making differentiation between ADHD, ADHD comorbid with CD (ADHD+CD), and CD a subjective diagnosis. Therefore, the goal of this pilot study is to create the first explainable deep learning (DL) model for objective ECG-based ADHD/CD diagnosis as having an objective biomarker may improve diagnostic accuracy. METHODS The dataset used in this study consist of ECG data collected from 45 ADHD, 62 ADHD+CD, and 16 CD patients at the Child Guidance Clinic in Singapore. The ECG data were segmented into 2 s epochs and directly used to train our 1-dimensional (1D) convolutional neural network (CNN) model. RESULTS The proposed model yielded 96.04% classification accuracy, 96.26% precision, 95.99% sensitivity, and 96.11% F1-score. The Gradient-weighted class activation mapping (Grad-CAM) function was also used to highlight the important ECG characteristics at specific time points that most impact the classification score. CONCLUSION In addition to achieving model performance results with our suggested DL method, Grad-CAM's implementation also offers vital temporal data that clinicians and other mental healthcare professionals can use to make wise medical judgments. We hope that by conducting this pilot study, we will be able to encourage larger-scale research with a larger biosignal dataset. Hence allowing biosignal-based computer-aided diagnostic (CAD) tools to be implemented in healthcare and ambulatory settings, as ECG can be easily obtained via wearable devices such as smartwatches.
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Affiliation(s)
- Hui Wen Loh
- School of Science and Technology, Singapore University of Social Sciences, Singapore
| | - Chui Ping Ooi
- School of Science and Technology, Singapore University of Social Sciences, Singapore
| | - Shu Lih Oh
- Cogninet Australia, Sydney, NSW 2010, Australia
| | - Prabal Datta Barua
- Cogninet Australia, Sydney, NSW 2010, Australia; Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia; School of Business (Information System), University of Southern Queensland, Australia; Australian International Institute of Higher Education, Sydney, NSW 2000, Australia; School of Science & Technology, University of New England, Australia; School of Biosciences, Taylor's University, Malaysia; School of Computing, SRM Institute of Science and Technology, India; School of Science and Technology, Kumamoto University, Japan; Sydney School of Education and Social work, University of Sydney, Australia
| | - Yi Ren Tan
- Developmental Psychiatry, Institute of Mental Health, Singapore
| | - Filippo Molinari
- Biolab, Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Torino, Italy
| | - Sonja March
- Centre for Health Research and School of Psychology and Wellbeing, University of Southern Queensland, Springfield, Australia
| | - U Rajendra Acharya
- School of Mathematics, Physics, and Computing, University of Southern Queensland, Springfield, Australia.
| | - Daniel Shuen Sheng Fung
- Developmental Psychiatry, Institute of Mental Health, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, DUKE NUS Medical School, Yong Loo Lin School of Medicine, National University of Singapore
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Parsa M, Rad HY, Vaezi H, Hossein-Zadeh GA, Setarehdan SK, Rostami R, Rostami H, Vahabie AH. EEG-based classification of individuals with neuropsychiatric disorders using deep neural networks: A systematic review of current status and future directions. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 240:107683. [PMID: 37406421 DOI: 10.1016/j.cmpb.2023.107683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Revised: 05/23/2023] [Accepted: 06/18/2023] [Indexed: 07/07/2023]
Abstract
The use of deep neural networks for electroencephalogram (EEG) classification has rapidly progressed and gained popularity in recent years, but automatic feature extraction from EEG signals remains a challenging task. The classification of neuropsychiatric disorders demands the extraction of neuro-markers for use in automated EEG classification. Numerous advanced deep learning algorithms can be used for this purpose. In this article, we present a comprehensive review of the main factors and parameters that affect the performance of deep neural networks in classifying different neuropsychiatric disorders using EEG signals. We also analyze the EEG features used for improving classification performance. Our analysis includes 82 scientific journal papers that applied deep neural networks for subject-wise classification based on EEG signals. We extracted information on the EEG dataset and types of disorders, deep neural network structures, performance, and hyperparameters. The results show that most studies have focused on clinical classification, achieving an average accuracy of 91.83 ± 7.34, with convolutional neural networks (CNNs) being the most frequently used network architecture and resting-state EEG signals being the most commonly used data type. Additionally, the review reveals that depression (N = 18), Alzheimer's (N = 11), and schizophrenia (N = 11) were studied more frequently than other types of neuropsychiatric disorders. Our review provides insight into the performance of deep neural networks in EEG classification and highlights the importance of EEG feature extraction in improving classification accuracy. By identifying the main factors and parameters that affect deep neural network performance in EEG classification, our review can guide future research in this area. We hope that our findings will encourage further exploration of deep learning methods for EEG classification and contribute to the development of more accurate and effective methods for diagnosing and monitoring neuropsychiatric disorders using EEG signals.
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Affiliation(s)
- Mohsen Parsa
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, North Kargar St., P.O. Box 14395/515, Tehran, Iran; School of Cognitive Sciences, Institute for Research in Fundamental Sciences, Artesh Highway, P.O. Box 19568-36484, Tehran, Iran
| | - Habib Yousefi Rad
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, North Kargar St., P.O. Box 14395/515, Tehran, Iran; School of Cognitive Sciences, Institute for Research in Fundamental Sciences, Artesh Highway, P.O. Box 19568-36484, Tehran, Iran
| | - Hadi Vaezi
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, North Kargar St., P.O. Box 14395/515, Tehran, Iran; School of Cognitive Sciences, Institute for Research in Fundamental Sciences, Artesh Highway, P.O. Box 19568-36484, Tehran, Iran
| | - Gholam-Ali Hossein-Zadeh
- Control and Intelligent Processing Center of Excellence, Faculty of Electrical and Computer Engineering, University of Tehran, North Kargar St., P.O. Box 14395/515, Tehran, Iran
| | - Seyed Kamaledin Setarehdan
- Control and Intelligent Processing Center of Excellence, Faculty of Electrical and Computer Engineering, University of Tehran, North Kargar St., P.O. Box 14395/515, Tehran, Iran
| | - Reza Rostami
- Faculty of Psychology and Education, University of Tehran, Jalal-Al-e-Ahmed, P.O. Box 14155-6456, Tehran, Iran
| | - Hana Rostami
- ACNC, Atieh Clinical Neuroscience Center, Valiasr St., P.O. Box 19697-13663, Tehran, Iran
| | - Abdol-Hossein Vahabie
- Faculty of Psychology and Education, University of Tehran, Jalal-Al-e-Ahmed, P.O. Box 14155-6456, Tehran, Iran; Cognitive Systems Laboratory, Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, P.O. Box 14395/515, Tehran, Iran; Pasargad Institute for Advanced Innovative Solutions (PIAIS), Tehran, Iran.
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Li Y, Yang B, Wang Z, Huang R, Lu X, Bi X, Zhou S. EEG assessment of brain dysfunction for patients with chronic primary pain and depression under auditory oddball task. Front Neurosci 2023; 17:1133834. [PMID: 37034156 PMCID: PMC10079993 DOI: 10.3389/fnins.2023.1133834] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 03/09/2023] [Indexed: 04/11/2023] Open
Abstract
In 2019, the International Classification of Diseases 11th Revision International Classification of Diseases (ICD-11) put forward a new concept of "chronic primary pain" (CPP), a kind of chronic pain characterized by severe functional disability and emotional distress, which is a medical problem that deserves great attention. Although CPP is closely related to depressive disorder, its potential neural characteristics are still unclear. This paper collected EEG data from 67 subjects (23 healthy subjects, 22 patients with depression, and 22 patients with CPP) under the auditory oddball paradigm, systematically analyzed the brain network connection matrix and graph theory characteristic indicators, and classified the EEG and PLI matrices of three groups of people by frequency band based on deep learning. The results showed significant differences in brain network connectivity between CPP patients and depressive patients. Specifically, the connectivity within the frontoparietal network of the Theta band in CPP patients is significantly enhanced. The CNN classification model of EEG is better than that of PLI, with the highest accuracy of 85.01% in Gamma band in former and 79.64% in Theta band in later. We propose hyperexcitability in attentional control in CPP patients and provide a novel method for objective assessment of chronic primary pain.
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Affiliation(s)
- Yunzhe Li
- School of Medicine, School of Mechatronic Engineering and Automation, Research Center of Brain Computer Engineering, Shanghai University, Shanghai, China
| | - Banghua Yang
- School of Medicine, School of Mechatronic Engineering and Automation, Research Center of Brain Computer Engineering, Shanghai University, Shanghai, China
- Shanghai Shaonao Sensing Technology Ltd., Shanghai, China
- *Correspondence: Banghua Yang,
| | - Zuowei Wang
- Division of Mood Disorders, Shanghai Hongkou Mental Health Center, Shanghai, China
| | - Ruyan Huang
- Division of Mood Disorders, Shanghai Hongkou Mental Health Center, Shanghai, China
| | - Xi Lu
- Department of Neurology, Shanghai Changhai Hospital, Second Military Medical University, Shanghai, China
| | - Xiaoying Bi
- Department of Neurology, Shanghai Changhai Hospital, Second Military Medical University, Shanghai, China
- Xiaoying Bi,
| | - Shu Zhou
- Department of Neurology, Shanghai Changhai Hospital, Second Military Medical University, Shanghai, China
- Shu Zhou,
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Shen M, Wen P, Song B, Li Y. Detection of alcoholic EEG signals based on whole brain connectivity and convolution neural networks. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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Akter S, Prodhan RA, Pias TS, Eisenberg D, Fresneda Fernandez J. M1M2: Deep-Learning-Based Real-Time Emotion Recognition from Neural Activity. SENSORS (BASEL, SWITZERLAND) 2022; 22:8467. [PMID: 36366164 PMCID: PMC9654596 DOI: 10.3390/s22218467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 10/20/2022] [Accepted: 10/28/2022] [Indexed: 06/16/2023]
Abstract
Emotion recognition, or the ability of computers to interpret people's emotional states, is a very active research area with vast applications to improve people's lives. However, most image-based emotion recognition techniques are flawed, as humans can intentionally hide their emotions by changing facial expressions. Consequently, brain signals are being used to detect human emotions with improved accuracy, but most proposed systems demonstrate poor performance as EEG signals are difficult to classify using standard machine learning and deep learning techniques. This paper proposes two convolutional neural network (CNN) models (M1: heavily parameterized CNN model and M2: lightly parameterized CNN model) coupled with elegant feature extraction methods for effective recognition. In this study, the most popular EEG benchmark dataset, the DEAP, is utilized with two of its labels, valence, and arousal, for binary classification. We use Fast Fourier Transformation to extract the frequency domain features, convolutional layers for deep features, and complementary features to represent the dataset. The M1 and M2 CNN models achieve nearly perfect accuracy of 99.89% and 99.22%, respectively, which outperform every previous state-of-the-art model. We empirically demonstrate that the M2 model requires only 2 seconds of EEG signal for 99.22% accuracy, and it can achieve over 96% accuracy with only 125 milliseconds of EEG data for valence classification. Moreover, the proposed M2 model achieves 96.8% accuracy on valence using only 10% of the training dataset, demonstrating our proposed system's effectiveness. Documented implementation codes for every experiment are published for reproducibility.
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Affiliation(s)
- Sumya Akter
- Martin Tuchman School of Management, New Jersey Institute of Technology, Newark, NJ 07102, USA
| | - Rumman Ahmed Prodhan
- Martin Tuchman School of Management, New Jersey Institute of Technology, Newark, NJ 07102, USA
| | - Tanmoy Sarkar Pias
- Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, USA
| | - David Eisenberg
- Department of Information Systems, Ying Wu College of Computing, New Jersey Institute of Technology, Newark, NJ 07102, USA
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Jawed S, Malik AS, Abd Rashid RB, Mohamad Saad MN. Deep learning-based diagnosis of Alcohol use disorder (AUD) using EEG. 2022 IEEE 12TH INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS (ICCE-BERLIN) 2022. [DOI: 10.1109/icce-berlin56473.2022.9937134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Affiliation(s)
- Soyiba Jawed
- Brno University of Technology,Faculty of Information Technology,Brno,Czech Republic
| | - Aamir Saeed Malik
- Brno University of Technology,Faculty of Information Technology,Brno,Czech Republic
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Li H, Wu L. EEG Classification of Normal and Alcoholic by Deep Learning. Brain Sci 2022; 12:778. [PMID: 35741663 PMCID: PMC9220822 DOI: 10.3390/brainsci12060778] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 06/06/2022] [Accepted: 06/11/2022] [Indexed: 12/21/2022] Open
Abstract
Alcohol dependence is a common mental disease worldwide. Excessive alcohol consumption may lead to alcoholism and many complications. In severe cases, it will lead to inhibition and paralysis of the centers of the respiratory and circulatory systems and even death. In addition, there is a lack of effective standard test procedures to detect alcoholism. EEG signals are data obtained by measuring brain changes in the cerebral cortex and can be used for the diagnosis of alcoholism. Existing diagnostic methods mainly employ machine learning techniques, which rely on human intervention to learn. In contrast, deep learning, as an end-to-end learning method, can automatically extract EEG signal features, which is more convenient. Nonetheless, there are few studies on the classification of alcohol's EEG signals using deep learning models. Therefore, in this paper, a new deep learning method is proposed to automatically extract and classify EEG's features. The method first adopts a multilayer discrete wavelet transform to denoise the input data. Then, the denoised data are used as input, and a convolutional neural network and bidirectional long short-term memory network are used for feature extraction. Finally, alcohol EEG signal classification is performed. The experimental results show that the method proposed in this study can be utilized to effectively diagnose patients with alcoholism, achieving a diagnostic accuracy of 99.32%, which is better than most current algorithms.
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Affiliation(s)
- Houchi Li
- School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411100, China;
| | - Lei Wu
- Hunan Engineering Research Center for Intelligent Decision Making and Big Data on Industrial Development, Hunan University of Science and Technology, Xiangtan 411100, China
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Komolovaitė D, Maskeliūnas R, Damaševičius R. Deep Convolutional Neural Network-Based Visual Stimuli Classification Using Electroencephalography Signals of Healthy and Alzheimer’s Disease Subjects. Life (Basel) 2022; 12:life12030374. [PMID: 35330125 PMCID: PMC8950142 DOI: 10.3390/life12030374] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 02/28/2022] [Accepted: 03/02/2022] [Indexed: 11/20/2022] Open
Abstract
Visual perception is an important part of human life. In the context of facial recognition, it allows us to distinguish between emotions and important facial features that distinguish one person from another. However, subjects suffering from memory loss face significant facial processing problems. If the perception of facial features is affected by memory impairment, then it is possible to classify visual stimuli using brain activity data from the visual processing regions of the brain. This study differentiates the aspects of familiarity and emotion by the inversion effect of the face and uses convolutional neural network (CNN) models (EEGNet, EEGNet SSVEP (steady-state visual evoked potentials), and DeepConvNet) to learn discriminative features from raw electroencephalography (EEG) signals. Due to the limited number of available EEG data samples, Generative Adversarial Networks (GAN) and Variational Autoencoders (VAE) are introduced to generate synthetic EEG signals. The generated data are used to pretrain the models, and the learned weights are initialized to train them on the real EEG data. We investigate minor facial characteristics in brain signals and the ability of deep CNN models to learn them. The effect of face inversion was studied, and it was observed that the N170 component has a considerable and sustained delay. As a result, emotional and familiarity stimuli were divided into two categories based on the posture of the face. The categories of upright and inverted stimuli have the smallest incidences of confusion. The model’s ability to learn the face-inversion effect is demonstrated once more.
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Affiliation(s)
- Dovilė Komolovaitė
- Department of Multimedia Engineering, Kaunas University of Technology, 51368 Kaunas, Lithuania;
| | - Rytis Maskeliūnas
- Department of Multimedia Engineering, Kaunas University of Technology, 51368 Kaunas, Lithuania;
- Correspondence:
| | - Robertas Damaševičius
- Department of Applied Informatics, Vytautas Magnus University, 44404 Kaunas, Lithuania;
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Salankar N, Qaisar SM, Pławiak P, Tadeusiewicz R, Hammad M. EEG based alcoholism detection by oscillatory modes decomposition second order difference plots and machine learning. Biocybern Biomed Eng 2022; 42:173-186. [DOI: 10.1016/j.bbe.2021.12.009] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
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An Approach for Pronunciation Classification of Classical Arabic Phonemes Using Deep Learning. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app12010238] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
A mispronunciation of Arabic short vowels can change the meaning of a complete sentence. For this reason, both the students and teachers of Classical Arabic (CA) are required extra practice for correcting students’ pronunciation of Arabic short vowels. That makes the teaching and learning task cumbersome for both parties. An intelligent process of students’ evaluation can make learning and teaching easier for both students and teachers. Given that online learning has become a norm these days, modern learning requires assessment by virtual teachers. In our case, the task is about recognizing the exact pronunciation of Arabic alphabets according to the standards. A major challenge in the recognition of precise pronunciation of Arabic alphabets is the correct identification of a large number of short vowels, which cannot be dealt with using traditional statistical audio processing techniques and machine learning models. Therefore, we developed a model that classifies Arabic short vowels using Deep Neural Networks (DNN). The model is constructed from scratch by: (i) collecting a new audio dataset, (ii) developing a neural network architecture, and (iii) optimizing and fine-tuning the developed model through several iterations to achieve high classification accuracy. Given a set of unseen audio samples of uttered short vowels, our proposed model has reached the testing accuracy of 95.77%. We can say that our results can be used by the experts and researchers for building better intelligent learning support systems in Arabic speech processing.
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Buriro AB, Ahmed B, Baloch G, Ahmed J, Shoorangiz R, Weddell SJ, Jones RD. Classification of alcoholic EEG signals using wavelet scattering transform-based features. Comput Biol Med 2021; 139:104969. [PMID: 34700252 DOI: 10.1016/j.compbiomed.2021.104969] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 10/20/2021] [Accepted: 10/20/2021] [Indexed: 11/15/2022]
Abstract
Following the research question and the relevant dataset, feature extraction is the most important component of machine learning and data science pipelines. The wavelet scattering transform (WST) is a recently developed knowledge-based feature extraction technique and is structurally like a convolutional neural network (CNN). It preserves information in high-frequency, is insensitive to signal deformations, and generates low variance features of real-valued signals generally required in classification tasks. With data from a publicly-available UCI database, we investigated the ability of WST-based features extracted from multichannel electroencephalogram (EEG) signals to discriminate 1.0-s EEG records of 20 male subjects with alcoholism and 20 male healthy subjects. Using record-wise 10-fold cross-validation, we found that WST-based features, inputted to a support vector machine (SVM) classifier, were able to correctly classify all alcoholic and normal EEG records. Similar performances were achieved with 1D CNN. In contrast, the highest independent-subject-wise mean 10-fold cross-validation performance was achieved with WST-based features fed to a linear discriminant (LDA) classifier. The results achieved with two 10-fold cross-validation approaches suggest that the WST together with a conventional classifier is an alternative to CNN for classification of alcoholic and normal EEGs. WST-based features from occipital and parietal regions were the most informative at discriminating between alcoholic and normal EEG records.
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Affiliation(s)
- Abdul Baseer Buriro
- Department of Electrical Engineering, Sukkur IBA University, Sukkur, 65200, Pakistan.
| | - Bilal Ahmed
- Department of Electrical Engineering, Sukkur IBA University, Sukkur, 65200, Pakistan
| | - Gulsher Baloch
- Department of Electrical Engineering, Sukkur IBA University, Sukkur, 65200, Pakistan
| | - Junaid Ahmed
- Department of Electrical Engineering, Sukkur IBA University, Sukkur, 65200, Pakistan
| | - Reza Shoorangiz
- Department of Electrical and Computer Engineering, University of Canterbury, Christchurch, 8041, New Zealand; New Zealand Brain Research Institute, Christchurch, 8011, New Zealand; School of Psychology, Speech and Hearing, University of Canterbury, Christchurch, 8041, New Zealand; Department of Medicine, University of Otago, Christchurch, 8011, New Zealand
| | - Stephen J Weddell
- Department of Electrical and Computer Engineering, University of Canterbury, Christchurch, 8041, New Zealand
| | - Richard D Jones
- Department of Electrical and Computer Engineering, University of Canterbury, Christchurch, 8041, New Zealand; New Zealand Brain Research Institute, Christchurch, 8011, New Zealand; School of Psychology, Speech and Hearing, University of Canterbury, Christchurch, 8041, New Zealand; Department of Medicine, University of Otago, Christchurch, 8011, New Zealand
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