Machine Learning Characterization of Ictal and Interictal States in EEG Aimed at Automated Seizure Detection
Background: The development of automated seizure detection methods using EEG signals could be of great importance for the diagnosis and the monitoring of patients with epilepsy. These methods are often patient-specific and require high accuracy in detecting seizures but also very low false-positive...
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| Format: | Article |
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MDPI AG
2022-06-01
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| Series: | Biomedicines |
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| Online Access: | https://www.mdpi.com/2227-9059/10/7/1491 |
| _version_ | 1797433843257442304 |
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| author | Gaetano Zazzaro Luigi Pavone |
| author_facet | Gaetano Zazzaro Luigi Pavone |
| author_sort | Gaetano Zazzaro |
| collection | DOAJ |
| description | Background: The development of automated seizure detection methods using EEG signals could be of great importance for the diagnosis and the monitoring of patients with epilepsy. These methods are often patient-specific and require high accuracy in detecting seizures but also very low false-positive rates. The aim of this study is to evaluate the performance of a seizure detection method using EEG signals by investigating its performance in correctly identifying seizures and in minimizing false alarms and to determine if it is generalizable to different patients. Methods: We tested the method on about two hours of preictal/ictal and about ten hours of interictal EEG recordings of one patient from the Freiburg Seizure Prediction EEG database using machine learning techniques for data mining. Then, we tested the obtained model on six other patients of the same database. Results: The method achieved very high performance in detecting seizures (close to 100% of correctly classified positive elements) with a very low false-positive rate when tested on one patient. Furthermore, the model portability or transfer analysis revealed that the method achieved good performance in one out of six patients from the same dataset. Conclusions: This result suggests a strategy to discover clusters of similar patients, for which it would be possible to train a general-purpose model for seizure detection. |
| first_indexed | 2024-03-09T10:22:39Z |
| format | Article |
| id | doaj.art-d685499e1ec740e38b7dae173116343a |
| institution | Directory Open Access Journal |
| issn | 2227-9059 |
| language | English |
| last_indexed | 2024-03-09T10:22:39Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomedicines |
| spelling | doaj.art-d685499e1ec740e38b7dae173116343a2023-12-01T21:55:02ZengMDPI AGBiomedicines2227-90592022-06-01107149110.3390/biomedicines10071491Machine Learning Characterization of Ictal and Interictal States in EEG Aimed at Automated Seizure DetectionGaetano Zazzaro0Luigi Pavone1C.I.R.A.—Italian Aerospace Research Centre, Via Maiorise s.n.c., 81043 Capua, ItalyI.R.C.C.S. Neuromed, Via Atinense, 18, 86077 Pozzilli, ItalyBackground: The development of automated seizure detection methods using EEG signals could be of great importance for the diagnosis and the monitoring of patients with epilepsy. These methods are often patient-specific and require high accuracy in detecting seizures but also very low false-positive rates. The aim of this study is to evaluate the performance of a seizure detection method using EEG signals by investigating its performance in correctly identifying seizures and in minimizing false alarms and to determine if it is generalizable to different patients. Methods: We tested the method on about two hours of preictal/ictal and about ten hours of interictal EEG recordings of one patient from the Freiburg Seizure Prediction EEG database using machine learning techniques for data mining. Then, we tested the obtained model on six other patients of the same database. Results: The method achieved very high performance in detecting seizures (close to 100% of correctly classified positive elements) with a very low false-positive rate when tested on one patient. Furthermore, the model portability or transfer analysis revealed that the method achieved good performance in one out of six patients from the same dataset. Conclusions: This result suggests a strategy to discover clusters of similar patients, for which it would be possible to train a general-purpose model for seizure detection.https://www.mdpi.com/2227-9059/10/7/1491data miningelectroencephalogramepilepsyfalse-alarm rateintracranial EEG<i>k</i>-nearest neighbor |
| spellingShingle | Gaetano Zazzaro Luigi Pavone Machine Learning Characterization of Ictal and Interictal States in EEG Aimed at Automated Seizure Detection Biomedicines data mining electroencephalogram epilepsy false-alarm rate intracranial EEG <i>k</i>-nearest neighbor |
| title | Machine Learning Characterization of Ictal and Interictal States in EEG Aimed at Automated Seizure Detection |
| title_full | Machine Learning Characterization of Ictal and Interictal States in EEG Aimed at Automated Seizure Detection |
| title_fullStr | Machine Learning Characterization of Ictal and Interictal States in EEG Aimed at Automated Seizure Detection |
| title_full_unstemmed | Machine Learning Characterization of Ictal and Interictal States in EEG Aimed at Automated Seizure Detection |
| title_short | Machine Learning Characterization of Ictal and Interictal States in EEG Aimed at Automated Seizure Detection |
| title_sort | machine learning characterization of ictal and interictal states in eeg aimed at automated seizure detection |
| topic | data mining electroencephalogram epilepsy false-alarm rate intracranial EEG <i>k</i>-nearest neighbor |
| url | https://www.mdpi.com/2227-9059/10/7/1491 |
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