Enhancing Epilepsy Seizure Detection Through Advanced EEG Preprocessing Techniques and Peak-to-Peak Amplitude Fluctuation Analysis
<b>Objectives:</b> Naturally, there are several challenges, such as muscular artifacts, ocular movements and electrical interferences that depend on precise diagnosis and classification, which hamper exact epileptic seizure detection. This study has been conducted to improve seizure dete...
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2024-11-01
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| author | Muawiyah A. Bahhah Eyad Talal Attar |
| author_facet | Muawiyah A. Bahhah Eyad Talal Attar |
| author_sort | Muawiyah A. Bahhah |
| collection | DOAJ |
| description | <b>Objectives:</b> Naturally, there are several challenges, such as muscular artifacts, ocular movements and electrical interferences that depend on precise diagnosis and classification, which hamper exact epileptic seizure detection. This study has been conducted to improve seizure detection accuracy in epilepsy patients using an advanced preprocessing technique that could remove such noxious artifacts. <b>Methods:</b> In the frame of this paper, the core tool in the area of epilepsy, EEG, will be applied to record and analyze the electrical patterns of the brain. The dataset includes recordings of seven epilepsy patients taken by the Unit of Neurology and Neurophysiology, University of Siena. The preprocessing techniques employed include advanced artifact removal and signal enhancement methods. We introduced Peak-to-Peak Amplitude Fluctuation (PPAF) to assess amplitude variability within Event-Related Potential (ERP) waveforms. This approach was applied to data from patients experiencing 3–5 seizures, categorized into three distinct groups. <b>Results:</b> The results indicated that the frontal and parietal regions, particularly the electrode areas Cz, Pz and Fp2, are the main contributors to epileptic seizures. Additionally, the implementation of the PPAF metric enhanced the effectiveness of seizure detection and classification algorithms, achieving accuracy rates of 99%, 98% and 95% for datasets with three, four and five seizures, respectively. <b>Conclusions:</b> The present research extends the epilepsy diagnosis with clues on brain activity during seizures and further demonstrates the effectiveness of advanced preprocessing techniques. The introduction of PPAF as a metric could have promising potential in improving both the accuracy and reliability of epilepsy seizure detection algorithms. These observations provide important implications for control and treatment both in focal and in generalized epilepsy. |
| format | Article |
| id | doaj-art-53667466b21e4d3ab73b7b663c361a33 |
| institution | Kabale University |
| issn | 2075-4418 |
| language | English |
| publishDate | 2024-11-01 |
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| series | Diagnostics |
| spelling | doaj-art-53667466b21e4d3ab73b7b663c361a332024-11-26T17:59:45ZengMDPI AGDiagnostics2075-44182024-11-011422252510.3390/diagnostics14222525Enhancing Epilepsy Seizure Detection Through Advanced EEG Preprocessing Techniques and Peak-to-Peak Amplitude Fluctuation AnalysisMuawiyah A. Bahhah0Eyad Talal Attar1Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi ArabiaDepartment of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia<b>Objectives:</b> Naturally, there are several challenges, such as muscular artifacts, ocular movements and electrical interferences that depend on precise diagnosis and classification, which hamper exact epileptic seizure detection. This study has been conducted to improve seizure detection accuracy in epilepsy patients using an advanced preprocessing technique that could remove such noxious artifacts. <b>Methods:</b> In the frame of this paper, the core tool in the area of epilepsy, EEG, will be applied to record and analyze the electrical patterns of the brain. The dataset includes recordings of seven epilepsy patients taken by the Unit of Neurology and Neurophysiology, University of Siena. The preprocessing techniques employed include advanced artifact removal and signal enhancement methods. We introduced Peak-to-Peak Amplitude Fluctuation (PPAF) to assess amplitude variability within Event-Related Potential (ERP) waveforms. This approach was applied to data from patients experiencing 3–5 seizures, categorized into three distinct groups. <b>Results:</b> The results indicated that the frontal and parietal regions, particularly the electrode areas Cz, Pz and Fp2, are the main contributors to epileptic seizures. Additionally, the implementation of the PPAF metric enhanced the effectiveness of seizure detection and classification algorithms, achieving accuracy rates of 99%, 98% and 95% for datasets with three, four and five seizures, respectively. <b>Conclusions:</b> The present research extends the epilepsy diagnosis with clues on brain activity during seizures and further demonstrates the effectiveness of advanced preprocessing techniques. The introduction of PPAF as a metric could have promising potential in improving both the accuracy and reliability of epilepsy seizure detection algorithms. These observations provide important implications for control and treatment both in focal and in generalized epilepsy.https://www.mdpi.com/2075-4418/14/22/2525epilepsyseizurefocal epilepsybrainindependent component analysis (ICA)epoch extraction |
| spellingShingle | Muawiyah A. Bahhah Eyad Talal Attar Enhancing Epilepsy Seizure Detection Through Advanced EEG Preprocessing Techniques and Peak-to-Peak Amplitude Fluctuation Analysis Diagnostics epilepsy seizure focal epilepsy brain independent component analysis (ICA) epoch extraction |
| title | Enhancing Epilepsy Seizure Detection Through Advanced EEG Preprocessing Techniques and Peak-to-Peak Amplitude Fluctuation Analysis |
| title_full | Enhancing Epilepsy Seizure Detection Through Advanced EEG Preprocessing Techniques and Peak-to-Peak Amplitude Fluctuation Analysis |
| title_fullStr | Enhancing Epilepsy Seizure Detection Through Advanced EEG Preprocessing Techniques and Peak-to-Peak Amplitude Fluctuation Analysis |
| title_full_unstemmed | Enhancing Epilepsy Seizure Detection Through Advanced EEG Preprocessing Techniques and Peak-to-Peak Amplitude Fluctuation Analysis |
| title_short | Enhancing Epilepsy Seizure Detection Through Advanced EEG Preprocessing Techniques and Peak-to-Peak Amplitude Fluctuation Analysis |
| title_sort | enhancing epilepsy seizure detection through advanced eeg preprocessing techniques and peak to peak amplitude fluctuation analysis |
| topic | epilepsy seizure focal epilepsy brain independent component analysis (ICA) epoch extraction |
| url | https://www.mdpi.com/2075-4418/14/22/2525 |
| work_keys_str_mv | AT muawiyahabahhah enhancingepilepsyseizuredetectionthroughadvancedeegpreprocessingtechniquesandpeaktopeakamplitudefluctuationanalysis AT eyadtalalattar enhancingepilepsyseizuredetectionthroughadvancedeegpreprocessingtechniquesandpeaktopeakamplitudefluctuationanalysis |