## 3 Seizure Detection Performance and Power Consumption Evaluation
### Seizure Detection Performance Evaluation  
To perform seizure detection, each EEG record was segmented into non-overlapping \(N=512\) sample (equivalent to 4 s at a sampling rate of 128 Hz) epochs. Epochs that contain solely non-seizure activity, _i.e._, falling between seizure events, are referred to as _non-seizure epochs_, whereas epochs that contain solely seizure activity, _i.e._, falling entirely within a seizure event, are referred to as _seizure epochs_. Epochs that contain both non-seizure and seizure activities are excluded from seizure detection evaluation. For every epoch, feature extraction was performed on each EEG channel individually. The features from all channels (23 channels in our case) in one epoch were concatenated to form a 23-element feature vector.  
To determine whether a newly observed feature vector is representative of seizure or non-seizure activity, a binary classifier was used. The classifier used in this study was the support vector machine (SVM). SVM has been applied in many seizure detection applications [20], due to its robustness and reliability [21]. Furthermore, SVM does not require a balanced number of positive and negative training samples, as in the case of neural networks. This property is particularly attractive for seizure detection, as the number of negative (non-seizure) samples is typically much larger than the number of positive (seizure) samples. In this study, a second-order polynomial kernel SVM is used in the classification of all feature types.  
The classification performance of the considered seizure detection methods was assessed on a per-subject basis using a leave-one-record-out cross-validation scheme, which was previously proposed in [20]. The cross-validation works as follows. In each round, the feature vectors from all, but one, of the tested subject's records were used as the training set to train the classifier. The feature vectors from the withheld record of the subject were then used to test the classifier. This was repeated, until every record of the tested subject was withheld once. This test provides an idea of the seizure detection algorithm's ability to generalize from the training set and to classify unseen data.  
The classification results were reported in terms of:  
* _Seizure Sensitivity_, which measures the percentage of expert-labeled seizure events during which at least one or more epochs are correctly labeled by the classifier as being a seizure. As mentioned above, a seizure event varied between six and 752 s and the segments of the data belonging to seizure events were marked by medical experts.  
* _Epoch Sensitivity_, which measures the percentage of seizure epochs that are correctly classified as seizure epochs by the classifier.
* _Specificity_, which measures the percentage of expert-labeled non-seizure epochs that are correctly labeled as non-seizure epochs.
* _False Positive Rate (FPR)_, which measures the number of non-seizure epochs that are incorrectly labeled as seizure epochs within a one-hour period.
* _Detection Latency_, which measures the delay (in seconds) between the onset of a seizure event and the end of the first seizure epoch within the event that is correctly labeled as a seizure.