Integrating old and new complexity measures toward automated seizure detection from long-term video EEG recordings
Summary: Automated seizure detection in long-term video-EEG recordings is far from being integrated into common clinical practice. Here, we leverage classical and state-of-the-art complexity measures to robustly and automatically detect seizures from scalp recordings. Brain activity is scored throug...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2021-01-01
|
| Series: | iScience |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589004220311949 |
| _version_ | 1818614179768041472 |
|---|---|
| author | Manuel Ruiz Marín Irene Villegas Martínez Germán Rodríguez Bermúdez Maurizio Porfiri |
| author_facet | Manuel Ruiz Marín Irene Villegas Martínez Germán Rodríguez Bermúdez Maurizio Porfiri |
| author_sort | Manuel Ruiz Marín |
| collection | DOAJ |
| description | Summary: Automated seizure detection in long-term video-EEG recordings is far from being integrated into common clinical practice. Here, we leverage classical and state-of-the-art complexity measures to robustly and automatically detect seizures from scalp recordings. Brain activity is scored through eight features, encompassing traditional time domain and novel measures of recurrence. A binary classification algorithm tailored to treat unbalanced dataset is used to determine whether a time window is ictal or non-ictal from its features. The application of the algorithm on a cohort of ten adult patients with focal refractory epilepsy indicates sensitivity, specificity, and accuracy of 90%, along with a true alarm rate of 95% and less than four false alarms per day. The proposed approach emphasizes ictal patterns against noisy background without the need of data preprocessing. Finally, we benchmark our approach against previous studies on two publicly available datasets, demonstrating the good performance of our algorithm. |
| first_indexed | 2024-12-16T16:13:54Z |
| format | Article |
| id | doaj.art-f14859c3788c4fe5ab8bf39c2de623f7 |
| institution | Directory Open Access Journal |
| issn | 2589-0042 |
| language | English |
| last_indexed | 2024-12-16T16:13:54Z |
| publishDate | 2021-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | iScience |
| spelling | doaj.art-f14859c3788c4fe5ab8bf39c2de623f72022-12-21T22:25:09ZengElsevieriScience2589-00422021-01-01241101997Integrating old and new complexity measures toward automated seizure detection from long-term video EEG recordingsManuel Ruiz Marín0Irene Villegas Martínez1Germán Rodríguez Bermúdez2Maurizio Porfiri3Department of Quantitative Methods, Law and Modern Languages, Technical University of Cartagena (UPCT), Cartagena, Murcia 30201, Spain; Bio-Health Institute (IMIB-Arrixaca), Health Science Campus, Murcia, CP 30120, Spain; Corresponding authorDepartment of Projects and Innovation, Health Service of Murcia (SMS), Murcia, Spain; Bio-Health Institute (IMIB-Arrixaca), Health Science Campus, Murcia, CP 30120, Spain; Corresponding authorUniversity Centre of Defense at the Spanish Air Force Academy, Murcia, SpainDepartment of Quantitative Methods, Law and Modern Languages, Technical University of Cartagena (UPCT), Cartagena, Murcia 30201, Spain; Department of Mechanical and Aerospace Engineering, and Department of Biomedical Engineering New York University Tandon School of Engineering (NYU), Brooklyn, NY, USASummary: Automated seizure detection in long-term video-EEG recordings is far from being integrated into common clinical practice. Here, we leverage classical and state-of-the-art complexity measures to robustly and automatically detect seizures from scalp recordings. Brain activity is scored through eight features, encompassing traditional time domain and novel measures of recurrence. A binary classification algorithm tailored to treat unbalanced dataset is used to determine whether a time window is ictal or non-ictal from its features. The application of the algorithm on a cohort of ten adult patients with focal refractory epilepsy indicates sensitivity, specificity, and accuracy of 90%, along with a true alarm rate of 95% and less than four false alarms per day. The proposed approach emphasizes ictal patterns against noisy background without the need of data preprocessing. Finally, we benchmark our approach against previous studies on two publicly available datasets, demonstrating the good performance of our algorithm.http://www.sciencedirect.com/science/article/pii/S2589004220311949Computer Application in MedicineComputer-Aided Diagnosis MethodClinical NeuroscienceTechniques in NeuroscienceAlgorithms |
| spellingShingle | Manuel Ruiz Marín Irene Villegas Martínez Germán Rodríguez Bermúdez Maurizio Porfiri Integrating old and new complexity measures toward automated seizure detection from long-term video EEG recordings iScience Computer Application in Medicine Computer-Aided Diagnosis Method Clinical Neuroscience Techniques in Neuroscience Algorithms |
| title | Integrating old and new complexity measures toward automated seizure detection from long-term video EEG recordings |
| title_full | Integrating old and new complexity measures toward automated seizure detection from long-term video EEG recordings |
| title_fullStr | Integrating old and new complexity measures toward automated seizure detection from long-term video EEG recordings |
| title_full_unstemmed | Integrating old and new complexity measures toward automated seizure detection from long-term video EEG recordings |
| title_short | Integrating old and new complexity measures toward automated seizure detection from long-term video EEG recordings |
| title_sort | integrating old and new complexity measures toward automated seizure detection from long term video eeg recordings |
| topic | Computer Application in Medicine Computer-Aided Diagnosis Method Clinical Neuroscience Techniques in Neuroscience Algorithms |
| url | http://www.sciencedirect.com/science/article/pii/S2589004220311949 |
| work_keys_str_mv | AT manuelruizmarin integratingoldandnewcomplexitymeasurestowardautomatedseizuredetectionfromlongtermvideoeegrecordings AT irenevillegasmartinez integratingoldandnewcomplexitymeasurestowardautomatedseizuredetectionfromlongtermvideoeegrecordings AT germanrodriguezbermudez integratingoldandnewcomplexitymeasurestowardautomatedseizuredetectionfromlongtermvideoeegrecordings AT maurizioporfiri integratingoldandnewcomplexitymeasurestowardautomatedseizuredetectionfromlongtermvideoeegrecordings |