Deep Learning-Based Detection of Epileptiform Discharges for Self-Limited Epilepsy With Centrotemporal Spikes
Centrotemporal spike-waves (CTSWs) are typical interictal epileptiform discharges (IEDs) observed in centrotemporal regions in self-limited epilepsy with centrotemporal spikes (SLECTS). This study aims to develop a deep learning-based approach for automated detection of CTSWs in scalp electroencepha...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
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| Online Access: | https://ieeexplore.ieee.org/document/9924188/ |
| _version_ | 1827926976991592448 |
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| author | Yonghoon Jeon Yoon Gi Chung Taehyun Joo Hunmin Kim Hee Hwang Ki Joong Kim |
| author_facet | Yonghoon Jeon Yoon Gi Chung Taehyun Joo Hunmin Kim Hee Hwang Ki Joong Kim |
| author_sort | Yonghoon Jeon |
| collection | DOAJ |
| description | Centrotemporal spike-waves (CTSWs) are typical interictal epileptiform discharges (IEDs) observed in centrotemporal regions in self-limited epilepsy with centrotemporal spikes (SLECTS). This study aims to develop a deep learning-based approach for automated detection of CTSWs in scalp electroencephalography (EEG) recordings of patients with SLECTS. To lower the substantial burden of IED annotation on clinicians, we simplified it by limiting IEDs to CTSWs because electroencephalographic patterns of CTSWs are known to be highly consistent. Two neurologists annotated 1672 CTSWs of 20 patients with SLECTS. Thereafter, we performed a two-level CTSW detection procedure: epoch-level and EEG-level. In the epoch-level detection, we constructed convolutional neural network-based classification models for CTSW and non-CTSW binary classification using the recordings of 20 patients and 20 controls. We then set the thresholds of the classification models for 100% specificity. In the EEG-level detection, we applied the threshold-adjusted classification models to the recordings of 50 patients and 50 controls that were not used in the epoch-level detection to distinguish between CTSW-positive (with one or more CTSWs) and CTSW-negative (with no CTSW) recordings based on the detection of CTSW presence. We obtained an average sensitivity, specificity, and accuracy of 99.8%, 98.4%, and 99.1%, respectively, with an average false detection rate of 0.19/hr for the controls. Our approach showed high detectability for CTSWs despite the simplified annotation process. We expect that the proposed CTSW detectors have potential clinical usefulness for efficiently reading EEGs and diagnosing SLECTS, and can significantly reduce the burden of IED annotation on clinicians. |
| first_indexed | 2024-03-13T05:46:25Z |
| format | Article |
| id | doaj.art-1145ac25dda24365b937419a2ef7d3a4 |
| institution | Directory Open Access Journal |
| issn | 1558-0210 |
| language | English |
| last_indexed | 2024-03-13T05:46:25Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj.art-1145ac25dda24365b937419a2ef7d3a42023-06-13T20:09:38ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1558-02102022-01-01302939294910.1109/TNSRE.2022.32155269924188Deep Learning-Based Detection of Epileptiform Discharges for Self-Limited Epilepsy With Centrotemporal SpikesYonghoon Jeon0https://orcid.org/0000-0002-0024-6890Yoon Gi Chung1https://orcid.org/0000-0002-2656-3317Taehyun Joo2Hunmin Kim3https://orcid.org/0000-0001-6689-3495Hee Hwang4Ki Joong Kim5Department of Pediatrics, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, South KoreaDepartment of Pediatrics, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, South KoreaSeoul National University College of Medicine, Seoul, South KoreaDepartment of Pediatrics, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, South KoreaDepartment of Pediatrics, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, South KoreaDepartment of Pediatrics, Seoul National University College of Medicine, Seoul National University Children’s Hospital, Seoul, South KoreaCentrotemporal spike-waves (CTSWs) are typical interictal epileptiform discharges (IEDs) observed in centrotemporal regions in self-limited epilepsy with centrotemporal spikes (SLECTS). This study aims to develop a deep learning-based approach for automated detection of CTSWs in scalp electroencephalography (EEG) recordings of patients with SLECTS. To lower the substantial burden of IED annotation on clinicians, we simplified it by limiting IEDs to CTSWs because electroencephalographic patterns of CTSWs are known to be highly consistent. Two neurologists annotated 1672 CTSWs of 20 patients with SLECTS. Thereafter, we performed a two-level CTSW detection procedure: epoch-level and EEG-level. In the epoch-level detection, we constructed convolutional neural network-based classification models for CTSW and non-CTSW binary classification using the recordings of 20 patients and 20 controls. We then set the thresholds of the classification models for 100% specificity. In the EEG-level detection, we applied the threshold-adjusted classification models to the recordings of 50 patients and 50 controls that were not used in the epoch-level detection to distinguish between CTSW-positive (with one or more CTSWs) and CTSW-negative (with no CTSW) recordings based on the detection of CTSW presence. We obtained an average sensitivity, specificity, and accuracy of 99.8%, 98.4%, and 99.1%, respectively, with an average false detection rate of 0.19/hr for the controls. Our approach showed high detectability for CTSWs despite the simplified annotation process. We expect that the proposed CTSW detectors have potential clinical usefulness for efficiently reading EEGs and diagnosing SLECTS, and can significantly reduce the burden of IED annotation on clinicians.https://ieeexplore.ieee.org/document/9924188/Deep learningelectroencephalography (EEG)interictal epileptiform discharge (IED)self-limited epilepsy with centrotemporal spikes (SLECTS)spike detection |
| spellingShingle | Yonghoon Jeon Yoon Gi Chung Taehyun Joo Hunmin Kim Hee Hwang Ki Joong Kim Deep Learning-Based Detection of Epileptiform Discharges for Self-Limited Epilepsy With Centrotemporal Spikes IEEE Transactions on Neural Systems and Rehabilitation Engineering Deep learning electroencephalography (EEG) interictal epileptiform discharge (IED) self-limited epilepsy with centrotemporal spikes (SLECTS) spike detection |
| title | Deep Learning-Based Detection of Epileptiform Discharges for Self-Limited Epilepsy With Centrotemporal Spikes |
| title_full | Deep Learning-Based Detection of Epileptiform Discharges for Self-Limited Epilepsy With Centrotemporal Spikes |
| title_fullStr | Deep Learning-Based Detection of Epileptiform Discharges for Self-Limited Epilepsy With Centrotemporal Spikes |
| title_full_unstemmed | Deep Learning-Based Detection of Epileptiform Discharges for Self-Limited Epilepsy With Centrotemporal Spikes |
| title_short | Deep Learning-Based Detection of Epileptiform Discharges for Self-Limited Epilepsy With Centrotemporal Spikes |
| title_sort | deep learning based detection of epileptiform discharges for self limited epilepsy with centrotemporal spikes |
| topic | Deep learning electroencephalography (EEG) interictal epileptiform discharge (IED) self-limited epilepsy with centrotemporal spikes (SLECTS) spike detection |
| url | https://ieeexplore.ieee.org/document/9924188/ |
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