LSGP-USFNet: Automated Attention Deficit Hyperactivity Disorder Detection Using Locations of Sophie Germain’s Primes on Ulam’s Spiral-Based Features with Electroencephalogram Signals
Anxiety, learning disabilities, and depression are the symptoms of attention deficit hyperactivity disorder (ADHD), an isogenous pattern of hyperactivity, impulsivity, and inattention. For the early diagnosis of ADHD, electroencephalogram (EEG) signals are widely used. However, the direct analysis o...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-08-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/16/7032 |
| _version_ | 1797583309716324352 |
|---|---|
| author | Orhan Atila Erkan Deniz Ali Ari Abdulkadir Sengur Subrata Chakraborty Prabal Datta Barua U. Rajendra Acharya |
| author_facet | Orhan Atila Erkan Deniz Ali Ari Abdulkadir Sengur Subrata Chakraborty Prabal Datta Barua U. Rajendra Acharya |
| author_sort | Orhan Atila |
| collection | DOAJ |
| description | Anxiety, learning disabilities, and depression are the symptoms of attention deficit hyperactivity disorder (ADHD), an isogenous pattern of hyperactivity, impulsivity, and inattention. For the early diagnosis of ADHD, electroencephalogram (EEG) signals are widely used. However, the direct analysis of an EEG is highly challenging as it is time-consuming, nonlinear, and nonstationary in nature. Thus, in this paper, a novel approach (LSGP-USFNet) is developed based on the patterns obtained from Ulam’s spiral and Sophia Germain’s prime numbers. The EEG signals are initially filtered to remove the noise and segmented with a non-overlapping sliding window of a length of 512 samples. Then, a time–frequency analysis approach, namely continuous wavelet transform, is applied to each channel of the segmented EEG signal to interpret it in the time and frequency domain. The obtained time–frequency representation is saved as a time–frequency image, and a non-overlapping <i>n</i> × <i>n</i> sliding window is applied to this image for patch extraction. An <i>n</i> × <i>n</i> Ulam’s spiral is localized on each patch, and the gray levels are acquired from this patch as features where Sophie Germain’s primes are located in Ulam’s spiral. All gray tones from all patches are concatenated to construct the features for ADHD and normal classes. A gray tone selection algorithm, namely ReliefF, is employed on the representative features to acquire the final most important gray tones. The support vector machine classifier is used with a 10-fold cross-validation criteria. Our proposed approach, LSGP-USFNet, was developed using a publicly available dataset and obtained an accuracy of 97.46% in detecting ADHD automatically. Our generated model is ready to be validated using a bigger database and it can also be used to detect other children’s neurological disorders. |
| first_indexed | 2024-03-10T23:37:05Z |
| format | Article |
| id | doaj.art-e3e68bb07b2b48d6b8768d3322b6b5c5 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T23:37:05Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-e3e68bb07b2b48d6b8768d3322b6b5c52023-11-19T02:55:50ZengMDPI AGSensors1424-82202023-08-012316703210.3390/s23167032LSGP-USFNet: Automated Attention Deficit Hyperactivity Disorder Detection Using Locations of Sophie Germain’s Primes on Ulam’s Spiral-Based Features with Electroencephalogram SignalsOrhan Atila0Erkan Deniz1Ali Ari2Abdulkadir Sengur3Subrata Chakraborty4Prabal Datta Barua5U. Rajendra Acharya6Electrical-Electronics Engineering Department, Technology Faculty, Firat University, 23119 Elazig, TurkeyElectrical-Electronics Engineering Department, Technology Faculty, Firat University, 23119 Elazig, TurkeyComputer Engineering Department, Engineering Faculty, Inonu University, 44280 Malatya, TurkeyElectrical-Electronics Engineering Department, Technology Faculty, Firat University, 23119 Elazig, TurkeyFaculty of Science, Agriculture, Business and Law, University of New England, Armidale, NSW 2351, AustraliaFaculty of Science, Agriculture, Business and Law, University of New England, Armidale, NSW 2351, AustraliaSchool of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, QLD 4300, AustraliaAnxiety, learning disabilities, and depression are the symptoms of attention deficit hyperactivity disorder (ADHD), an isogenous pattern of hyperactivity, impulsivity, and inattention. For the early diagnosis of ADHD, electroencephalogram (EEG) signals are widely used. However, the direct analysis of an EEG is highly challenging as it is time-consuming, nonlinear, and nonstationary in nature. Thus, in this paper, a novel approach (LSGP-USFNet) is developed based on the patterns obtained from Ulam’s spiral and Sophia Germain’s prime numbers. The EEG signals are initially filtered to remove the noise and segmented with a non-overlapping sliding window of a length of 512 samples. Then, a time–frequency analysis approach, namely continuous wavelet transform, is applied to each channel of the segmented EEG signal to interpret it in the time and frequency domain. The obtained time–frequency representation is saved as a time–frequency image, and a non-overlapping <i>n</i> × <i>n</i> sliding window is applied to this image for patch extraction. An <i>n</i> × <i>n</i> Ulam’s spiral is localized on each patch, and the gray levels are acquired from this patch as features where Sophie Germain’s primes are located in Ulam’s spiral. All gray tones from all patches are concatenated to construct the features for ADHD and normal classes. A gray tone selection algorithm, namely ReliefF, is employed on the representative features to acquire the final most important gray tones. The support vector machine classifier is used with a 10-fold cross-validation criteria. Our proposed approach, LSGP-USFNet, was developed using a publicly available dataset and obtained an accuracy of 97.46% in detecting ADHD automatically. Our generated model is ready to be validated using a bigger database and it can also be used to detect other children’s neurological disorders.https://www.mdpi.com/1424-8220/23/16/7032ADHD detectionEEG signalsUlam’s spiralSophie Germain’s primesSVM |
| spellingShingle | Orhan Atila Erkan Deniz Ali Ari Abdulkadir Sengur Subrata Chakraborty Prabal Datta Barua U. Rajendra Acharya LSGP-USFNet: Automated Attention Deficit Hyperactivity Disorder Detection Using Locations of Sophie Germain’s Primes on Ulam’s Spiral-Based Features with Electroencephalogram Signals Sensors ADHD detection EEG signals Ulam’s spiral Sophie Germain’s primes SVM |
| title | LSGP-USFNet: Automated Attention Deficit Hyperactivity Disorder Detection Using Locations of Sophie Germain’s Primes on Ulam’s Spiral-Based Features with Electroencephalogram Signals |
| title_full | LSGP-USFNet: Automated Attention Deficit Hyperactivity Disorder Detection Using Locations of Sophie Germain’s Primes on Ulam’s Spiral-Based Features with Electroencephalogram Signals |
| title_fullStr | LSGP-USFNet: Automated Attention Deficit Hyperactivity Disorder Detection Using Locations of Sophie Germain’s Primes on Ulam’s Spiral-Based Features with Electroencephalogram Signals |
| title_full_unstemmed | LSGP-USFNet: Automated Attention Deficit Hyperactivity Disorder Detection Using Locations of Sophie Germain’s Primes on Ulam’s Spiral-Based Features with Electroencephalogram Signals |
| title_short | LSGP-USFNet: Automated Attention Deficit Hyperactivity Disorder Detection Using Locations of Sophie Germain’s Primes on Ulam’s Spiral-Based Features with Electroencephalogram Signals |
| title_sort | lsgp usfnet automated attention deficit hyperactivity disorder detection using locations of sophie germain s primes on ulam s spiral based features with electroencephalogram signals |
| topic | ADHD detection EEG signals Ulam’s spiral Sophie Germain’s primes SVM |
| url | https://www.mdpi.com/1424-8220/23/16/7032 |
| work_keys_str_mv | AT orhanatila lsgpusfnetautomatedattentiondeficithyperactivitydisorderdetectionusinglocationsofsophiegermainsprimesonulamsspiralbasedfeatureswithelectroencephalogramsignals AT erkandeniz lsgpusfnetautomatedattentiondeficithyperactivitydisorderdetectionusinglocationsofsophiegermainsprimesonulamsspiralbasedfeatureswithelectroencephalogramsignals AT aliari lsgpusfnetautomatedattentiondeficithyperactivitydisorderdetectionusinglocationsofsophiegermainsprimesonulamsspiralbasedfeatureswithelectroencephalogramsignals AT abdulkadirsengur lsgpusfnetautomatedattentiondeficithyperactivitydisorderdetectionusinglocationsofsophiegermainsprimesonulamsspiralbasedfeatureswithelectroencephalogramsignals AT subratachakraborty lsgpusfnetautomatedattentiondeficithyperactivitydisorderdetectionusinglocationsofsophiegermainsprimesonulamsspiralbasedfeatureswithelectroencephalogramsignals AT prabaldattabarua lsgpusfnetautomatedattentiondeficithyperactivitydisorderdetectionusinglocationsofsophiegermainsprimesonulamsspiralbasedfeatureswithelectroencephalogramsignals AT urajendraacharya lsgpusfnetautomatedattentiondeficithyperactivitydisorderdetectionusinglocationsofsophiegermainsprimesonulamsspiralbasedfeatureswithelectroencephalogramsignals |