Detection of ADHD Disorder in Children using Layer-wise Relevance Propagation and Convolutional Neural Network: an EEG Analysis
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Abstract
Purpose: Attention-Deficit-Hyperactivity-Disorder (ADHD) is a neurodevelopmental disorder that begins in early childhood and often persists into adulthood, causing personality issues and social behavior problems. Thus, detecting ADHD in its early stages and developing an effective therapy is of tremendous interest. This study presents a deep learning-based model for ADHD diagnosis in children.
Materials and Methods: The 'First-National-EEG-Data-Analysis-Competition-with-Clinical-Application' dataset is used for this purpose. Following preprocessing, data is segmented into 3-second epochs, and frequency features are extracted from these epochs. The Fourier transform is applied to each channel separately, and the resulting two-dimensional matrix (channel×frequency) for each epoch is used as the Convolutional Neural Network's (CNN) input. The CNN is made up of two convolutional layers, two max pooling layers and two fully connected layers as well as the output layer (a total of 9 layers) for classification. To improve the method's performance, the output of the classification of each input variable is analyzed. In other words, the role of each channel/frequency in the final classification is being investigated using the Layer-wise Relevance Propagation (LRP) algorithm.
Results: According to the results of the LRP algorithm, only efficient channels are employed as Convolutional Neural Network (CNN) inputs in the following stage. This method yields a final accuracy of 94.52% for validation data. In this study, the feature space is visualized, useful channels are selected, and deep structure capabilities are exploited to diagnose ADHD disorder.
Conclusion: The findings suggest that the proposed technique can be used to effectively diagnose ADHD in children.
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21- Zahra Tabanfar, Farzaneh Yousefipoor, Mohammad Firoozabadi, Zeynab Khodakarami, and Zeinab Shankayi, "Recognition of two emotional states of joy and sadness using phase lag index and SVM classifier." in 2016 23rd Iranian Conference on Biomedical Engineering and 2016 1st International Iranian Conference on Biomedical Engineering (ICBME), (2016): IEEE, pp. 327-30.
22- Amin Behzadnia, Farnaz Ghassemi, Soghra A Chermahini, Zahra Tabanfar, and Athena Taymourtash, "The neural correlation of sustained attention in performing conjunctive continuous performance task: an event-related potential study." Neuroreport, Vol. 29 (No. 11), pp. 954-61, (2018).
23- Ali Nouri and Kyanoosh Azizi, "Introducing a Convolutional Neural Network and Visualization of its Filters for Classification of EEG Signal for SSVEP Task." Frontiers in Biomedical Technologies, (2020).
24- Mahdiyeh Sarraf Razavi, Mehdi Tehranidoost, Farnaz Ghassemi, Parivash Purabassi, and Athena Taymourtash, "Emotional face recognition in children with attention deficit/hyperactivity disorder: Evidence from event related gamma oscillation." Basic and Clinical Neuroscience, Vol. 8 (No. 5), p. 419, (2017).
25- Ansarinasab, Sheida, et al. "Investigation of Phase Synchronization in Functional Brain Networks of Children with ADHD using Nonlinear Recurrence Measure." Journal of Theoretical Biology: 111381, (2022).
26- Vangelis Sakkalis, Michalis Zervakis, and Sifis Micheloyannis, "Significant EEG features involved in mathematical reasoning: evidence from wavelet analysis." Brain Topography, Vol. 19 (No. 1), pp. 53-60, (2006).
27- Peter J Uhlhaas and Wolf Singer, "The development of neural synchrony and large-scale cortical networks during adolescence: relevance for the pathophysiology of schizophrenia and neurodevelopmental hypothesis." Schizophrenia bulletin, Vol. 37 (No. 3), pp. 514-23, (2011).
28- Robert J Barry, Adam R Clarke, Mihaly Hajos, Rory McCarthy, Mark Selikowitz, and Jason M Bruggemann, "Acute atomoxetine effects on the EEG of children with attention-deficit/hyperactivity disorder." Neuropharmacology, Vol. 57 (No. 7-8), pp. 702-07, (2009).
29- László Tombor, Brigitta Kakuszi, Szilvia Papp, János Réthelyi, István Bitter, and Pál Czobor, "Decreased resting gamma activity in adult attention deficit/hyperactivity disorder." The World Journal of Biological Psychiatry, (2018).
2- Mario Muñoz-Organero, Lauren Powell, Ben Heller, Val Harpin, and Jack Parker, "Using recurrent neural networks to compare movement patterns in ADHD and normally developing children based on acceleration signals from the wrist and ankle." Sensors, Vol. 19 (No. 13), p. 2935, (2019).
3- Mark L Wolraich et al., "ADHD diagnosis and treatment guidelines: a historical perspective." Pediatrics, Vol. 144 (No. 4), p. e20191682, (2019).
4- Marta Ribasés et al., "Meta-Analysis of Genome-Wide Association Studies On Adult Attention-Deficit and Hyperactivity Disorder." European Neuropsychopharmacology, Vol. 29, pp. S758-S59, (2019).
5- Maddalena Mauri et al., "Hemodynamic and behavioral peculiarities in response to emotional stimuli in children with attention deficit hyperactivity disorder: An fNIRS study." Journal of Affective Disorders, Vol. 277pp. 671-80, (2020).
6- Austin Schimmel, "ADHD and Fidget Spinners: Using fNIRs to detect changes in Relative Neural Efficiency during the Purdue Pegboard Test." University of Delaware, (2020).
7- Atif Riaz, Muhammad Asad, Eduardo Alonso, and Greg Slabaugh, "DeepFMRI: End-to-end deep learning for functional connectivity and classification of ADHD using fMRI." Journal of Neuroscience Methods, Vol. 335p. 108506, (2020).
8- Hossein Dini, Farnaz Ghassemi, and Mohammad SE Sendi, "Investigation of Brain Functional Networks in Children Suffering from Attention Deficit Hyperactivity Disorder." Brain Topography, pp. 1-18, (2020).
9- Matthew A Jarrett, Philip A Gable, Ana T Rondon, Lauren B Neal, Hannah F Price, and Dane C Hilton, "An EEG study of children with and without ADHD symptoms: between-group differences and associations with sluggish cognitive tempo symptoms." Journal of attention disorders, Vol. 24 (No. 7), pp. 1002-10, (2020).
10- Y.-J.; Chang Chu, C.-F.; Shieh, J.-S.; Lee, W.-T., " The Potential Application of Multiscale Entropy Analysis of Electroencephalography in Children with Neurological and Neuropsychiatric Disorders." Entropy, Vol. 19 (No. 8), p. 428, (2017).
11- Laura Dubreuil-Vall, Giulio Ruffini, and Joan A Camprodon, "Deep learning convolutional neural networks discriminate adult ADHD from healthy individuals on the basis of event-related spectral EEG." Frontiers in neuroscience, Vol. 14p. 251, (2020).
12- He Chen, Yan Song, and Xiaoli Li, "A deep learning framework for identifying children with ADHD using an EEG-based brain network." Neurocomputing, Vol. 356pp. 83-96, (2019).
13- Amirali Vahid, Annet Bluschke, Veit Roessner, Sebastian Stober, and Christian Beste, "Deep learning based on event-related EEG differentiates children with ADHD from healthy controls." Journal of clinical medicine, Vol. 8 (No. 7), p. 1055, (2019).
14- Majid Moghaddari, Mina Zolfy Lighvan, and Sebelan Danishvar, "Diagnose ADHD disorder in children using convolutional neural network based on continuous mental task EEG." Computer Methods and Programs in Biomedicine, Vol. 197p. 105738, (2020).
15- Yang Chang, Cory Stevenson, I-Chun Chen, Dar-Shong Lin, and Li-Wei Ko, "Neurological state changes indicative of ADHD in children learned via EEG-based LSTM networks." Journal of neural engineering, Vol. 19 (No. 1), p. 016021, (2022).
16- Amirmasoud Ahmadi, Mehrdad Kashefi, Hassan Shahrokhi, and Mohammad Ali Nazari, "Computer aided diagnosis system using deep convolutional neural networks for ADHD subtypes." Biomedical Signal Processing and Control, Vol. 63p. 102227.
17- Robin Tibor Schirrmeister et al., "Deep learning with convolutional neural networks for EEG decoding and visualization." Human brain mapping, Vol. 38 (No. 11), pp. 5391-420, (2017).
18- He Chen, Yan Song, and Xiaoli Li, "Use of deep learning to detect personalized spatial-frequency abnormalities in EEGs of children with ADHD." Journal of neural engineering, Vol. 16 (No. 6), p. 066046, (2019).
19- Laura Dubreuil-Vall, Giulio Ruffini, and Joan A Camprodon, "Deep learning convolutional neural networks discriminate adult ADHD from healthy individuals on the basis of event-related spectral EEG." Frontiers in neuroscience, Vol. 14(2020).
20- Armin Allahverdy Ali Motie Nasrabadi, Mehdi Samavati, Mohammad Reza Mohammadi, "EEG data for ADHD/Control Children ("EEG Competition Data")." National Brain Mapping Laboratory of Iran, Ed., ed. IEEE Dataport, (2020).
21- Zahra Tabanfar, Farzaneh Yousefipoor, Mohammad Firoozabadi, Zeynab Khodakarami, and Zeinab Shankayi, "Recognition of two emotional states of joy and sadness using phase lag index and SVM classifier." in 2016 23rd Iranian Conference on Biomedical Engineering and 2016 1st International Iranian Conference on Biomedical Engineering (ICBME), (2016): IEEE, pp. 327-30.
22- Amin Behzadnia, Farnaz Ghassemi, Soghra A Chermahini, Zahra Tabanfar, and Athena Taymourtash, "The neural correlation of sustained attention in performing conjunctive continuous performance task: an event-related potential study." Neuroreport, Vol. 29 (No. 11), pp. 954-61, (2018).
23- Ali Nouri and Kyanoosh Azizi, "Introducing a Convolutional Neural Network and Visualization of its Filters for Classification of EEG Signal for SSVEP Task." Frontiers in Biomedical Technologies, (2020).
24- Mahdiyeh Sarraf Razavi, Mehdi Tehranidoost, Farnaz Ghassemi, Parivash Purabassi, and Athena Taymourtash, "Emotional face recognition in children with attention deficit/hyperactivity disorder: Evidence from event related gamma oscillation." Basic and Clinical Neuroscience, Vol. 8 (No. 5), p. 419, (2017).
25- Ansarinasab, Sheida, et al. "Investigation of Phase Synchronization in Functional Brain Networks of Children with ADHD using Nonlinear Recurrence Measure." Journal of Theoretical Biology: 111381, (2022).
26- Vangelis Sakkalis, Michalis Zervakis, and Sifis Micheloyannis, "Significant EEG features involved in mathematical reasoning: evidence from wavelet analysis." Brain Topography, Vol. 19 (No. 1), pp. 53-60, (2006).
27- Peter J Uhlhaas and Wolf Singer, "The development of neural synchrony and large-scale cortical networks during adolescence: relevance for the pathophysiology of schizophrenia and neurodevelopmental hypothesis." Schizophrenia bulletin, Vol. 37 (No. 3), pp. 514-23, (2011).
28- Robert J Barry, Adam R Clarke, Mihaly Hajos, Rory McCarthy, Mark Selikowitz, and Jason M Bruggemann, "Acute atomoxetine effects on the EEG of children with attention-deficit/hyperactivity disorder." Neuropharmacology, Vol. 57 (No. 7-8), pp. 702-07, (2009).
29- László Tombor, Brigitta Kakuszi, Szilvia Papp, János Réthelyi, István Bitter, and Pál Czobor, "Decreased resting gamma activity in adult attention deficit/hyperactivity disorder." The World Journal of Biological Psychiatry, (2018).
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| Issue | Vol 11 No 1 (2024) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/fbt.v11i1.14507 | |
| Keywords | ||
| Attention Deficit Hyperactivity Disorder Convolutional Neural Network Layer-Wise Relevance Propagation Algorithm Electroencephalogram Signal Processing | ||
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How to Cite
1.
Nouri A, Tabanfar Z. Detection of ADHD Disorder in Children using Layer-wise Relevance Propagation and Convolutional Neural Network: an EEG Analysis. Frontiers Biomed Technol. 2024;11(1):14-21.
