Abstract
Introduction:
Preoperative evaluation ‹https://www.sciencedirect.com/topics/medicine-and-dentistry/preoperative-evaluation› of patients with medically intractable epilepsies ‹https://www.sciencedirect.com/topics/medicine-and-dentistry/intractable-epilepsy› requires accurate localization of the epileptogenic zone. Intracerebral depth electrodes are used to asses regions of interest and to enclose the resection zone during presurgical planning. Often a high number of needles is needed to accurately define the resection volume. Manual evaluation of these prolonged recordings including a large number of channels which is highly time consuming. To raise efficiency we developed a computer algorithm for automatic detection of epileptic seizures in depth electrode recordings. Primary goal was to detect seizures with high sensitivity without the need to set patient specific parameters.
Methods:
The automatic seizure detection algorithm for depth electrode data was based on an existing seizure detection algorithm for surface EEG. Evaluation of the frequency and amplitude range was extended to allow detection of ictal activity of up to 25 Hz and amplitudes of up to 1 mV. To reduce the number of false detections a method to recognize loose contacts was implemented. For clinical validation, recordings of 11 patients that underwent depth electrode investigation in the Academic Centre of Epileptology, Kempenhaeghe were utilized. Recordings were evaluated manually by clinical neurophysiologists and seizures were annotated. For 10 patients the first 24 h of their depth electrode recording were analyzed, yielding in total 23 seizures detected for five of the patients studied. For one patient the complete recording was analyzed with a duration of 138 h, yielding 36 seizures. Manual seizure annotations were compared to computer detections for assessment of sensitivity and false detection rate.
Results:
The automatic seizure detection algorithm found 84% of all seizures on average. All or more than 80% of the seizures were detected in the first 24 h of 10 patients. Analysis of the complete patient recording showed suppressed seizure onset activity of less than 2 s duration followed by paroxysmal fast activity. In this patient 14 out of the 36 seizures that evolved into a rhythmic ictal pattern were detected. The average false detection rate was 15 false detections in 24 h. Validation showed that most of the false positives either point to interictal activity or to background alpha activity.
Conclusion:
We proposed a computer aided workflow for evaluation of depth electrode recordings. Our automatic seizure detection algorithm was able to detect either all or more than 7 seizures of a patient which allows deduction of important medical findings. The low false detection rate facilitates fast review of data. Our proposed approach showed that automatic evaluation of seizure activity in depth electrode recordings is feasible and will raise the overall efficiency of diagnostic.
Preoperative evaluation ‹https://www.sciencedirect.com/topics/medicine-and-dentistry/preoperative-evaluation› of patients with medically intractable epilepsies ‹https://www.sciencedirect.com/topics/medicine-and-dentistry/intractable-epilepsy› requires accurate localization of the epileptogenic zone. Intracerebral depth electrodes are used to asses regions of interest and to enclose the resection zone during presurgical planning. Often a high number of needles is needed to accurately define the resection volume. Manual evaluation of these prolonged recordings including a large number of channels which is highly time consuming. To raise efficiency we developed a computer algorithm for automatic detection of epileptic seizures in depth electrode recordings. Primary goal was to detect seizures with high sensitivity without the need to set patient specific parameters.
Methods:
The automatic seizure detection algorithm for depth electrode data was based on an existing seizure detection algorithm for surface EEG. Evaluation of the frequency and amplitude range was extended to allow detection of ictal activity of up to 25 Hz and amplitudes of up to 1 mV. To reduce the number of false detections a method to recognize loose contacts was implemented. For clinical validation, recordings of 11 patients that underwent depth electrode investigation in the Academic Centre of Epileptology, Kempenhaeghe were utilized. Recordings were evaluated manually by clinical neurophysiologists and seizures were annotated. For 10 patients the first 24 h of their depth electrode recording were analyzed, yielding in total 23 seizures detected for five of the patients studied. For one patient the complete recording was analyzed with a duration of 138 h, yielding 36 seizures. Manual seizure annotations were compared to computer detections for assessment of sensitivity and false detection rate.
Results:
The automatic seizure detection algorithm found 84% of all seizures on average. All or more than 80% of the seizures were detected in the first 24 h of 10 patients. Analysis of the complete patient recording showed suppressed seizure onset activity of less than 2 s duration followed by paroxysmal fast activity. In this patient 14 out of the 36 seizures that evolved into a rhythmic ictal pattern were detected. The average false detection rate was 15 false detections in 24 h. Validation showed that most of the false positives either point to interictal activity or to background alpha activity.
Conclusion:
We proposed a computer aided workflow for evaluation of depth electrode recordings. Our automatic seizure detection algorithm was able to detect either all or more than 7 seizures of a patient which allows deduction of important medical findings. The low false detection rate facilitates fast review of data. Our proposed approach showed that automatic evaluation of seizure activity in depth electrode recordings is feasible and will raise the overall efficiency of diagnostic.
| Original language | German |
|---|---|
| Pages | e189 |
| DOIs | |
| Publication status | Published - 10 May 2018 |
| Event | 31st International Congress of Clinical Neurophysiology (ICCN) - Marriott Wardman Park, Washington DC, United States Duration: 1 May 2018 → 6 May 2018 |
Conference
| Conference | 31st International Congress of Clinical Neurophysiology (ICCN) |
|---|---|
| Abbreviated title | ICCN 2018 |
| Country/Territory | United States |
| City | Washington DC |
| Period | 1/05/18 → 6/05/18 |
| Other | The American Clinical Neurophysiology Society (ACNS) and the Canadian Society of Clinical Neurophysiologists (CSCN) are pleased to announce the 31st International Congress of Clinical Neurophysiology (ICCN) of the International Federation of Clinical Neurophysiology (IFCN) will be held May 1-6, 2018 in Washington, DC, USA. The 31st ICCN program will include several of ACNS’s signature courses, as well as courses and workshops planned by IFCN Member Societies and other prominent societies in clinical neurophysiology from the US and around the world. Pre-Congress courses and workshops are designed to provide a solid review of the fundamentals and the latest scientific advances in both central and peripheral clinical neurophysiology. Three days of general and concurrent Congress sessions will follow including honorary lectures, symposia, platform presentations, controversy sessions, and industry-supported satellite symposia. ACNS and CSCN are honored to co-host ICCN 2018 and to welcome colleagues to Washington, DC! |
Research Field
- Exploration of Digital Health
Keywords
- Automatic seizure detection
- depth electrode recordings