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Tor HT, Ooi CP, Lim-Ashworth NS, Wei JKE, Jahmunah V, Oh SL, Acharya UR, Fung DSS. Automated detection of conduct disorder and attention deficit hyperactivity disorder using decomposition and nonlinear techniques with EEG signals. Comput Methods Programs Biomed 2021; 200:105941. [PMID: 33486340 DOI: 10.1016/j.cmpb.2021.105941] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Accepted: 01/10/2021] [Indexed: 05/22/2023]
Abstract
BACKGROUND AND OBJECTIVES Attention deficit hyperactivity disorder (ADHD) is often presented with conduct disorder (CD). There is currently no objective laboratory test or diagnostic method to discern between ADHD and CD, and diagnosis is further made difficult as ADHD is a common neuro-developmental disorder often presenting with other co-morbid difficulties; and in particular with conduct disorder which has a high degree of associated behavioural challenges. A novel automated system (AS) is proposed as a convenient supplementary tool to support clinicians in their diagnostic decisions. To the best of our knowledge, we are the first group to develop an automated classification system to classify ADHD, CD and ADHD+CD classes using brain signals. METHODS The empirical mode decomposition (EMD) and discrete wavelet transform (DWT) methods were employed to decompose the electroencephalogram (EEG) signals. Autoregressive modelling coefficients and relative wavelet energy were then computed on the signals. Various nonlinear features were extracted from the decomposed coefficients. Adaptive synthetic sampling (ADASYN) was then employed to balance the dataset. The significant features were selected using sequential forward selection method. The highly discriminatory features were subsequently fed to an array of classifiers. RESULTS The highest accuracy of 97.88% was achieved with the K-Nearest Neighbour (KNN) classifier. The proposed system was developed using ten-fold validation strategy on EEG data from 123 children. To the best of our knowledge this is the first study to develop an AS for the classification of ADHD, CD and ADHD+CD classes using EEG signals. POTENTIAL APPLICATION Our AS can potentially be used as a web-based application with cloud system to aid the clinical diagnosis of ADHD and/or CD, thus supporting faster and accurate treatment for the children. It is important to note that testing with larger data is required before the AS can be employed for clinical applications.
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Affiliation(s)
- Hui Tian Tor
- 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
| | | | | | - V Jahmunah
- School of Engineering, Ngee Ann Polytechnic, Singapore
| | - Shu Lih Oh
- School of Engineering, Ngee Ann Polytechnic, Singapore
| | - U Rajendra Acharya
- School of Engineering, Ngee Ann Polytechnic, Singapore; Department of Bioinformatics and Medical Engineering, Asia University, Taiwan, ROC; School of Management and Enterprise 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, National University of Singapore; Yong Loo Lin School of Medicine, National University of Singapore
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Raghavendra U, Pham TH, Gudigar A, Vidhya V, Rao BN, Sabut S, Wei JKE, Ciaccio EJ, Acharya UR. Novel and accurate non-linear index for the automated detection of haemorrhagic brain stroke using CT images. COMPLEX INTELL SYST 2021. [DOI: 10.1007/s40747-020-00257-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
AbstractBrain stroke is an emergency medical condition which occurs mainly due to insufficient blood flow to the brain. It results in permanent cellular-level damage. There are two main types of brain stroke, ischemic and hemorrhagic. Ischemic brain stroke is caused by a lack of blood flow, and the haemorrhagic form is due to internal bleeding. The affected part of brain will not function properly after this attack. Hence, early detection is important for more efficacious treatment. Computer-aided diagnosis is a type of non-invasive diagnostic tool which can help in detecting life-threatening disease in its early stage by utilizing image processing and soft computing techniques. In this paper, we have developed one such model to assess intracerebral haemorrhage by employing non-linear features combined with a probabilistic neural network classifier and computed tomography (CT) images. Our model achieved a maximum accuracy of 97.37% in discerning normal versus haemorrhagic subjects. An intracerebral haemorrhage index is also developed using only three significant features. The clinical and statistical validation of the model confirms its suitability in providing for improved treatment planning and in making strategic decisions.
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Pham TH, Vicnesh J, Wei JKE, Oh SL, Arunkumar N, Abdulhay EW, Ciaccio EJ, Acharya UR. Autism Spectrum Disorder Diagnostic System Using HOS Bispectrum with EEG Signals. Int J Environ Res Public Health 2020; 17:E971. [PMID: 32033231 PMCID: PMC7038220 DOI: 10.3390/ijerph17030971] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/25/2019] [Revised: 01/29/2020] [Accepted: 01/30/2020] [Indexed: 11/16/2022]
Abstract
Autistic individuals often have difficulties expressing or controlling emotions and have poor eye contact, among other symptoms. The prevalence of autism is increasing globally, posing a need to address this concern. Current diagnostic systems have particular limitations; hence, some individuals go undiagnosed or the diagnosis is delayed. In this study, an effective autism diagnostic system using electroencephalogram (EEG) signals, which are generated from electrical activity in the brain, was developed and characterized. The pre-processed signals were converted to two-dimensional images using the higher-order spectra (HOS) bispectrum. Nonlinear features were extracted thereafter, and then reduced using locality sensitivity discriminant analysis (LSDA). Significant features were selected from the condensed feature set using Student's t-test, and were then input to different classifiers. The probabilistic neural network (PNN) classifier achieved the highest accuracy of 98.70% with just five features. Ten-fold cross-validation was employed to evaluate the performance of the classifier. It was shown that the developed system can be useful as a decision support tool to assist healthcare professionals in diagnosing autism.
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Affiliation(s)
- The-Hanh Pham
- School of Engineering, Ngee Ann Polytechnic, 535 Clementi Rd, Singapore 599489, Singapore; (T.-H.P.); (J.V.); (J.K.E.W.); (S.L.O.)
| | - Jahmunah Vicnesh
- School of Engineering, Ngee Ann Polytechnic, 535 Clementi Rd, Singapore 599489, Singapore; (T.-H.P.); (J.V.); (J.K.E.W.); (S.L.O.)
| | - Joel Koh En Wei
- School of Engineering, Ngee Ann Polytechnic, 535 Clementi Rd, Singapore 599489, Singapore; (T.-H.P.); (J.V.); (J.K.E.W.); (S.L.O.)
| | - Shu Lih Oh
- School of Engineering, Ngee Ann Polytechnic, 535 Clementi Rd, Singapore 599489, Singapore; (T.-H.P.); (J.V.); (J.K.E.W.); (S.L.O.)
| | - N. Arunkumar
- Department of Electronics and Instrumentation, SASTRA University, Thirumalaisamudram, Thanjavur 613401, India;
| | - Enas. W. Abdulhay
- Biomedical Engineering Department, Faculty of Engineering, Jordan University of Science and Technology, P.O.Box 3030, Irbid 22110, Jordan;
| | - Edward J. Ciaccio
- Department of Medicine – Columbia University New York, 630 W 168th St, New York, NY 10032, USA;
| | - U. Rajendra Acharya
- School of Engineering, Ngee Ann Polytechnic, 535 Clementi Rd, Singapore 599489, Singapore; (T.-H.P.); (J.V.); (J.K.E.W.); (S.L.O.)
- Department of Bioinformatics and Medical Engineering, Asia University, 500, Lioufeng Rd., Wufeng, Taichung 41354, Taiwan
- International Research Organization for Advanced Science and Technology (IROAST) Kumamoto University, Kumamoto, 2-39-1 Kurokami Chuo-ku, Kumamoto 860-855, Japan
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Jahmunah V, Oh SL, Wei JKE, Ciaccio EJ, Chua K, San TR, Acharya UR. Computer-aided diagnosis of congestive heart failure using ECG signals - A review. Phys Med 2019; 62:95-104. [PMID: 31153403 DOI: 10.1016/j.ejmp.2019.05.004] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Revised: 05/02/2019] [Accepted: 05/04/2019] [Indexed: 12/16/2022] Open
Abstract
The heart muscle pumps blood to vital organs, which is indispensable for human life. Congestive heart failure (CHF) is characterized by the inability of the heart to pump blood adequately throughout the body without an increase in intracardiac pressure. The symptoms include lung and peripheral congestion, leading to breathing difficulty and swollen limbs, dizziness from reduced delivery of blood to the brain, as well as arrhythmia. Coronary artery disease, myocardial infarction, and medical co-morbidities such as kidney disease, diabetes, and high blood pressure all take a toll on the heart and can impair myocardial function. CHF prevalence is growing worldwide. It afflicts millions of people globally, and is a leading cause of death. Hence, proper diagnosis, monitoring and management are imperative. The importance of an objective CHF diagnostic tool cannot be overemphasized. Standard diagnostic tests for CHF include chest X-ray, magnetic resonance imaging (MRI), nuclear imaging, echocardiography, and invasive angiography. However, these methods are costly, time-consuming, and they can be operator-dependent. Electrocardiography (ECG) is inexpensive and widely accessible, but ECG changes are typically not specific for CHF diagnosis. A properly designed computer-aided detection (CAD) system for CHF, based on the ECG, would potentially reduce subjectivity and provide quantitative assessment for informed decision-making. Herein, we review existing CAD for automatic CHF diagnosis, and highlight the development of an ECG-based CAD diagnostic system that employs deep learning algorithms to automatically detect CHF.
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Affiliation(s)
- V Jahmunah
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore.
| | - Shu Lih Oh
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore
| | - Joel Koh En Wei
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore
| | | | - Kuang Chua
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore
| | | | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore; School of Medicine, Faculty of Health and Medical Sciences, Taylor's University, 47500 Subang Jaya, Malaysia.
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Vicnesh J, Wei JKE, Ciaccio EJ, Oh SL, Bhagat G, Lewis SK, Green PH, Acharya UR. Automated diagnosis of celiac disease by video capsule endoscopy using DAISY Descriptors. J Med Syst 2019; 43:157. [PMID: 31028562 DOI: 10.1007/s10916-019-1285-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2019] [Accepted: 04/09/2019] [Indexed: 12/24/2022]
Abstract
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 be 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.
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Affiliation(s)
- Jahmunah Vicnesh
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, 599489, Singapore
| | - Joel Koh En Wei
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, 599489, Singapore
| | - Edward J Ciaccio
- Department of Medicine - Celiac Disease Center, Columbia University, New York, NY, USA
| | - Shu Lih Oh
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, 599489, Singapore
| | - Govind Bhagat
- Department of Medicine - Celiac Disease Center, Columbia University, New York, NY, USA.,Department of Pathology and Cell Biology, Columbia University, New York, NY, USA
| | - Suzanne K Lewis
- Department of Medicine - Celiac Disease Center, Columbia University, New York, NY, USA
| | - Peter H Green
- Department of Medicine - Celiac Disease Center, Columbia University, New York, NY, USA
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, 599489, Singapore. .,Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore, Singapore. .,School of Medicine, Faculty of Health and Medical Sciences, Taylor's University, 47500, Subang Jaya, Malaysia.
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