Deep neural network technique for automated detection of ADHD and CD using ECG signal.
- Publisher:
- Elsevier
- Publication Type:
- Journal Article
- Citation:
- Comput Methods Programs Biomed, 2023, 241, pp. 107775
- Issue Date:
- 2023-11
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Full metadata record
Field | Value | Language |
---|---|---|
dc.contributor.author | Loh, HW | |
dc.contributor.author | Ooi, CP | |
dc.contributor.author | Oh, SL | |
dc.contributor.author | Barua, PD | |
dc.contributor.author | Tan, YR | |
dc.contributor.author | Molinari, F | |
dc.contributor.author | March, S | |
dc.contributor.author | Acharya, UR | |
dc.contributor.author | Fung, DSS | |
dc.date.accessioned | 2024-04-05T01:02:55Z | |
dc.date.available | 2023-08-22 | |
dc.date.available | 2024-04-05T01:02:55Z | |
dc.date.issued | 2023-11 | |
dc.identifier.citation | Comput Methods Programs Biomed, 2023, 241, pp. 107775 | |
dc.identifier.issn | 0169-2607 | |
dc.identifier.issn | 1872-7565 | |
dc.identifier.uri | http://hdl.handle.net/10453/177487 | |
dc.description.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. | |
dc.format | Print-Electronic | |
dc.language | eng | |
dc.publisher | Elsevier | |
dc.relation.ispartof | Comput Methods Programs Biomed | |
dc.relation.isbasedon | 10.1016/j.cmpb.2023.107775 | |
dc.rights | info:eu-repo/semantics/openAccess | |
dc.subject | 0801 Artificial Intelligence and Image Processing, 0903 Biomedical Engineering, 0906 Electrical and Electronic Engineering | |
dc.subject.classification | Medical Informatics | |
dc.subject.classification | 4003 Biomedical engineering | |
dc.subject.classification | 4601 Applied computing | |
dc.subject.classification | 4603 Computer vision and multimedia computation | |
dc.subject.mesh | Adolescent | |
dc.subject.mesh | Child | |
dc.subject.mesh | Humans | |
dc.subject.mesh | Conduct Disorder | |
dc.subject.mesh | Attention Deficit Disorder with Hyperactivity | |
dc.subject.mesh | Pilot Projects | |
dc.subject.mesh | Neural Networks, Computer | |
dc.subject.mesh | Electrocardiography | |
dc.subject.mesh | Humans | |
dc.subject.mesh | Electrocardiography | |
dc.subject.mesh | Pilot Projects | |
dc.subject.mesh | Attention Deficit Disorder with Hyperactivity | |
dc.subject.mesh | Conduct Disorder | |
dc.subject.mesh | Adolescent | |
dc.subject.mesh | Child | |
dc.subject.mesh | Neural Networks, Computer | |
dc.subject.mesh | Adolescent | |
dc.subject.mesh | Child | |
dc.subject.mesh | Humans | |
dc.subject.mesh | Conduct Disorder | |
dc.subject.mesh | Attention Deficit Disorder with Hyperactivity | |
dc.subject.mesh | Pilot Projects | |
dc.subject.mesh | Neural Networks, Computer | |
dc.subject.mesh | Electrocardiography | |
dc.title | Deep neural network technique for automated detection of ADHD and CD using ECG signal. | |
dc.type | Journal Article | |
utslib.citation.volume | 241 | |
utslib.location.activity | Ireland | |
utslib.for | 0801 Artificial Intelligence and Image Processing | |
utslib.for | 0903 Biomedical Engineering | |
utslib.for | 0906 Electrical and Electronic Engineering | |
pubs.organisational-group | University of Technology Sydney | |
pubs.organisational-group | University of Technology Sydney/Faculty of Engineering and Information Technology | |
pubs.organisational-group | University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering | |
utslib.copyright.status | open_access | * |
dc.date.updated | 2024-04-05T01:02:53Z | |
pubs.publication-status | Published | |
pubs.volume | 241 |
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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