Key result
A seizure detection algorithm using fused EEG and ECG features recognized 98.31% of expert-labeled seizures with a false detection rate of 11.52% across 12 records.
Why the study?
Does fusing EEG and ECG features improve real-time seizure detection?
Does fusing EEG and ECG features improve real-time seizure detection?
Fusing EEG and ECG features using Gabor functions and a k-NN classifier achieves high accuracy in real-time seizure detection.
May aid real-time epilepsy monitoring; leaves open prospective validation in larger cohorts.
Using the scalp electroencephalogram (EEG) to detect seizure onsets that are not associated with rhythmic EEG activity is challenging. In this paper, we illustrate how supplementing the extracted information from the scalp EEG with the extracted information from electrocardiogram (ECG) can improve the detection of these types of seizures. In this scheme, spectral and spatial features are extracted from EEG and ECG signals by Gabor functions. Then a k-nearest neighbour classifier is used to classify the extracted features from seizure and non-seizure EEG-ECG signals. This algorithm can automatically detect the presence of seizures which can be important advance facilitating timely medical intervention. The performance of algorithm is evaluated on 12 records and recognizes 98.31% expert-labeled seizures with a false detection rate of 11.52%.
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Nasehi et al. (2011) studied Seizures (n=12). Seizure detection algorithm based on EEG and ECG fused features was evaluated on Seizure recognition rate. A seizure detection algorithm using fused EEG and ECG features recognized 98.31% of expert-labeled seizures with a false detection rate of 11.52% across 12 records.
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