EEG Signal Processing for Medical Diagnosis, Healthcare, and Monitoring: A Comprehensive Review
<p dir="ltr">EEG is a common and safe test that uses small electrodes to record electrical signals from the brain. It has a broad range of applications in medical diagnosis, including diagnosis of epileptic seizure, Alzheimer’s, brain tumors, head injury, sleep disorders, stroke, and...
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2023
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| _version_ | 1864513527061938176 |
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| author | Nisreen Said Amer (17984077) |
| author2 | Samir Brahim Belhaouari (9427347) |
| author2_role | author |
| author_facet | Nisreen Said Amer (17984077) Samir Brahim Belhaouari (9427347) |
| author_role | author |
| dc.creator.none.fl_str_mv | Nisreen Said Amer (17984077) Samir Brahim Belhaouari (9427347) |
| dc.date.none.fl_str_mv | 2023-12-12T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2023.3341419 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/EEG_Signal_Processing_for_Medical_Diagnosis_Healthcare_and_Monitoring_A_Comprehensive_Review/25239760 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electrical engineering Electronics, sensors and digital hardware Materials engineering Electroencephalography Feature extraction Electrodes Neurons Medical diagnosis Epilepsy Classification algorithms Machine learning preprocessing |
| dc.title.none.fl_str_mv | EEG Signal Processing for Medical Diagnosis, Healthcare, and Monitoring: A Comprehensive Review |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">EEG is a common and safe test that uses small electrodes to record electrical signals from the brain. It has a broad range of applications in medical diagnosis, including diagnosis of epileptic seizure, Alzheimer’s, brain tumors, head injury, sleep disorders, stroke, and other seizure and neurological disorders. EEG can also be used to help diagnose death in people who are in a persistent coma. The use of digital signal processing and machine learning to improve EEG analysis for medical diagnosis has gained traction in recent years. This is because EEG visual analysis can be complex and time-consuming, as it mostly involves high dimensions and consists of large datasets. The development of novel sensors for EEG recording, digital signal processing algorithms, feature engineering, and detection algorithms increases the need for efficient diagnostic systems. An extensive review of the recent approaches for EEG preprocessing, extraction of features, and diagnosis of brain disorders is provided. In this paper, the main focus is to identify reliable algorithms for preprocessing, feature engineering, and classification of EEG, applied to medical healthcare and diagnosis, providing practitioners with insights into the most effective strategies, as well as potential future directions for improving accuracy of the automatic diagnostic systems. The study of reliable feature extraction and classification algorithms is crucial for a more accurate analysis of EEG signals. This paper can provide valuable information to researchers and practitioners working in the fields of EEG analysis and machine learning, as it provides a summary of recent developments and highlights key areas for future research. This paper can help researchers and clinicians to stay up-to-date on the latest developments in this field.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2023.3341419" target="_blank">https://dx.doi.org/10.1109/access.2023.3341419</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_c22e2127528cc3e6a50b92fbc226f651 |
| identifier_str_mv | 10.1109/access.2023.3341419 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25239760 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | EEG Signal Processing for Medical Diagnosis, Healthcare, and Monitoring: A Comprehensive ReviewNisreen Said Amer (17984077)Samir Brahim Belhaouari (9427347)EngineeringElectrical engineeringElectronics, sensors and digital hardwareMaterials engineeringElectroencephalographyFeature extractionElectrodesNeuronsMedical diagnosisEpilepsyClassification algorithmsMachine learningpreprocessing<p dir="ltr">EEG is a common and safe test that uses small electrodes to record electrical signals from the brain. It has a broad range of applications in medical diagnosis, including diagnosis of epileptic seizure, Alzheimer’s, brain tumors, head injury, sleep disorders, stroke, and other seizure and neurological disorders. EEG can also be used to help diagnose death in people who are in a persistent coma. The use of digital signal processing and machine learning to improve EEG analysis for medical diagnosis has gained traction in recent years. This is because EEG visual analysis can be complex and time-consuming, as it mostly involves high dimensions and consists of large datasets. The development of novel sensors for EEG recording, digital signal processing algorithms, feature engineering, and detection algorithms increases the need for efficient diagnostic systems. An extensive review of the recent approaches for EEG preprocessing, extraction of features, and diagnosis of brain disorders is provided. In this paper, the main focus is to identify reliable algorithms for preprocessing, feature engineering, and classification of EEG, applied to medical healthcare and diagnosis, providing practitioners with insights into the most effective strategies, as well as potential future directions for improving accuracy of the automatic diagnostic systems. The study of reliable feature extraction and classification algorithms is crucial for a more accurate analysis of EEG signals. This paper can provide valuable information to researchers and practitioners working in the fields of EEG analysis and machine learning, as it provides a summary of recent developments and highlights key areas for future research. This paper can help researchers and clinicians to stay up-to-date on the latest developments in this field.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2023.3341419" target="_blank">https://dx.doi.org/10.1109/access.2023.3341419</a></p>2023-12-12T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2023.3341419https://figshare.com/articles/journal_contribution/EEG_Signal_Processing_for_Medical_Diagnosis_Healthcare_and_Monitoring_A_Comprehensive_Review/25239760CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/252397602023-12-12T09:00:00Z |
| spellingShingle | EEG Signal Processing for Medical Diagnosis, Healthcare, and Monitoring: A Comprehensive Review Nisreen Said Amer (17984077) Engineering Electrical engineering Electronics, sensors and digital hardware Materials engineering Electroencephalography Feature extraction Electrodes Neurons Medical diagnosis Epilepsy Classification algorithms Machine learning preprocessing |
| status_str | publishedVersion |
| title | EEG Signal Processing for Medical Diagnosis, Healthcare, and Monitoring: A Comprehensive Review |
| title_full | EEG Signal Processing for Medical Diagnosis, Healthcare, and Monitoring: A Comprehensive Review |
| title_fullStr | EEG Signal Processing for Medical Diagnosis, Healthcare, and Monitoring: A Comprehensive Review |
| title_full_unstemmed | EEG Signal Processing for Medical Diagnosis, Healthcare, and Monitoring: A Comprehensive Review |
| title_short | EEG Signal Processing for Medical Diagnosis, Healthcare, and Monitoring: A Comprehensive Review |
| title_sort | EEG Signal Processing for Medical Diagnosis, Healthcare, and Monitoring: A Comprehensive Review |
| topic | Engineering Electrical engineering Electronics, sensors and digital hardware Materials engineering Electroencephalography Feature extraction Electrodes Neurons Medical diagnosis Epilepsy Classification algorithms Machine learning preprocessing |