Identifying Patterns for Neurological Disabilities by Integrating Discrete Wavelet Transform and Visualization
Neurological disabilities cause diverse health and mental challenges, impacting quality of life and imposing financial burdens on both the individuals diagnosed with these conditions and their caregivers. Abnormal brain activity, stemming from malfunctions in the human nervous system, characterizes...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/1/273 |
| _version_ | 1797359094696247296 |
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| author | Soo Yeon Ji Sampath Jayarathna Anne M. Perrotti Katrina Kardiasmenos Dong Hyun Jeong |
| author_facet | Soo Yeon Ji Sampath Jayarathna Anne M. Perrotti Katrina Kardiasmenos Dong Hyun Jeong |
| author_sort | Soo Yeon Ji |
| collection | DOAJ |
| description | Neurological disabilities cause diverse health and mental challenges, impacting quality of life and imposing financial burdens on both the individuals diagnosed with these conditions and their caregivers. Abnormal brain activity, stemming from malfunctions in the human nervous system, characterizes neurological disorders. Therefore, the early identification of these abnormalities is crucial for devising suitable treatments and interventions aimed at promoting and sustaining quality of life. Electroencephalogram (EEG), a non-invasive method for monitoring brain activity, is frequently employed to detect abnormal brain activity in neurological and mental disorders. This study introduces an approach that extends the understanding and identification of neurological disabilities by integrating feature extraction, machine learning, and visual analysis based on EEG signals collected from individuals with neurological and mental disorders. The classification performance of four feature approaches—EEG frequency band, raw data, power spectral density, and wavelet transform—is assessed using machine learning techniques to evaluate their capability to differentiate neurological disabilities in short EEG segmentations (one second and two seconds). In detail, the classification analysis is conducted under two conditions: single-channel-based classification and region-based classification. While a clear demarcation between normal (healthy) and abnormal (neurological disabilities) EEG metrics may not be evident, their similarities and distinctions are observed through visualization, employing wavelet features. Notably, the frontal brain region (frontal lobe) emerges as a crucial area for distinguishing abnormalities among different brain regions. Also, the integration of wavelet features and visual analysis proves effective in identifying and understanding neurological disabilities. |
| first_indexed | 2024-03-08T15:11:48Z |
| format | Article |
| id | doaj.art-b3fca3523cba45dc828a1aaaffef9536 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-08T15:11:48Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-b3fca3523cba45dc828a1aaaffef95362024-01-10T14:51:34ZengMDPI AGApplied Sciences2076-34172023-12-0114127310.3390/app14010273Identifying Patterns for Neurological Disabilities by Integrating Discrete Wavelet Transform and VisualizationSoo Yeon Ji0Sampath Jayarathna1Anne M. Perrotti2Katrina Kardiasmenos3Dong Hyun Jeong4Department of Computer Science, Bowie State University, Bowie, MD 20715, USADepartment of Computer Science, Old Dominion University, Norfolk, VA 23529, USADepartment of Human Movement Sciences, Old Dominion University, Norfolk, VA 23529, USADepartment of Psychology, Bowie State University, Bowie, MD 20715, USADepartment of Computer Science and Information Technology, University of the District of Columbia, Washington, DC 20008, USANeurological disabilities cause diverse health and mental challenges, impacting quality of life and imposing financial burdens on both the individuals diagnosed with these conditions and their caregivers. Abnormal brain activity, stemming from malfunctions in the human nervous system, characterizes neurological disorders. Therefore, the early identification of these abnormalities is crucial for devising suitable treatments and interventions aimed at promoting and sustaining quality of life. Electroencephalogram (EEG), a non-invasive method for monitoring brain activity, is frequently employed to detect abnormal brain activity in neurological and mental disorders. This study introduces an approach that extends the understanding and identification of neurological disabilities by integrating feature extraction, machine learning, and visual analysis based on EEG signals collected from individuals with neurological and mental disorders. The classification performance of four feature approaches—EEG frequency band, raw data, power spectral density, and wavelet transform—is assessed using machine learning techniques to evaluate their capability to differentiate neurological disabilities in short EEG segmentations (one second and two seconds). In detail, the classification analysis is conducted under two conditions: single-channel-based classification and region-based classification. While a clear demarcation between normal (healthy) and abnormal (neurological disabilities) EEG metrics may not be evident, their similarities and distinctions are observed through visualization, employing wavelet features. Notably, the frontal brain region (frontal lobe) emerges as a crucial area for distinguishing abnormalities among different brain regions. Also, the integration of wavelet features and visual analysis proves effective in identifying and understanding neurological disabilities.https://www.mdpi.com/2076-3417/14/1/273neurological disordersdiscrete wavelet transformvisual analysismachine learningfeature extraction |
| spellingShingle | Soo Yeon Ji Sampath Jayarathna Anne M. Perrotti Katrina Kardiasmenos Dong Hyun Jeong Identifying Patterns for Neurological Disabilities by Integrating Discrete Wavelet Transform and Visualization Applied Sciences neurological disorders discrete wavelet transform visual analysis machine learning feature extraction |
| title | Identifying Patterns for Neurological Disabilities by Integrating Discrete Wavelet Transform and Visualization |
| title_full | Identifying Patterns for Neurological Disabilities by Integrating Discrete Wavelet Transform and Visualization |
| title_fullStr | Identifying Patterns for Neurological Disabilities by Integrating Discrete Wavelet Transform and Visualization |
| title_full_unstemmed | Identifying Patterns for Neurological Disabilities by Integrating Discrete Wavelet Transform and Visualization |
| title_short | Identifying Patterns for Neurological Disabilities by Integrating Discrete Wavelet Transform and Visualization |
| title_sort | identifying patterns for neurological disabilities by integrating discrete wavelet transform and visualization |
| topic | neurological disorders discrete wavelet transform visual analysis machine learning feature extraction |
| url | https://www.mdpi.com/2076-3417/14/1/273 |
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