[home] [Personal Program] [Help]

---

Session: Poster session II
Session starts: Thursday 24 January, 16:00

---

Kaat Vandecasteele ()
Thomas De Cooman ()
Ying Gu ()
Steven Vandeput ()
Evy Cleeren ()
Kasper Claes ()
Jonathan Dan ()
Wim Van Paesschen ()
Sabine Van Huffel ()
Borbála Hunyadi ()

Abstract:
Seizure diaries kept by the patients are subjective and often unreliable. In order to obtain an objective seizure diary outside the hospital, an automated wearable seizure detection device is required. To detect non-convulsive seizures, electroencephalography (EEG) and electrocardiography (ECG) are interesting biomedical signals. Instead of using the full scalp EEG, only behind-the-ear EEG channels are used here, which can be recorded with a wearable device outside the hospital. This research proposes a patient-specific automated seizure detection algorithm based on behind-the-ear EEG and HR. The proposed algorithm combined 4 behind-the-ear EEG channels (two behind each ear) with the heart rate. These signals were recorded with the hospital hardware. The algorithm consisted of two sequential steps. Firstly, a Support Vector Machine-based algorithm generated alarms based on the EEG data only. Secondly, on the data segments responsible for generating these alarms, a k-nearest neighbors-based algorithm, using EEG and ECG data, was run. A key element in this algorithm is the alignment in time of the ictal manifestation in EEG and ECG. This algorithm was evaluated on a dataset of consecutive patients recorded in the University Hospitals Leuven between with the following inclusion criteria (1) at least 2 seizures with EEG correlates (2) at least an average ictal HR increase of 30 beats per minute. The dataset consists of 1897 hours of data originating from 18 patients including 80 focal impaired awareness seizures, arising from (fronto-) temporal (78) and frontal-parietal (2) lobe. The average Sensitivity (Sens), False Positives per hour (FP/h) and Positive Predictive Value (PPV) were calculated for the EEG-based and the EEG/ECG-based seizure detection algorithm. The EEG-based algorithm resulted in a Sens of 78.36%, 1.09 FP/h and a PPV of 0.13, whereas the EEG/ECG-based algorithm resulted in a Sens of 65.73%, 0.15 FP/h and a PPV of 0.31.