## 3 Databases and Results
### Databases
#### 3.1.2 CHB-MIT Database  
CHB-MIT database published by [PERSON] [37] contains long-term multi-channel EEG data of 24 epilepsy patients (1.5-22 years old) collected at the Children's Hospital of Boston-Massachusetts Institute of Technology, with a 256 sampling rate and a 16-bit resolution. Data in CHB-MIT database were acquired continuously during long-term EEG monitoring with no signal processing performed after acquisition. The raw EEG data can better simulate a practical application scenario. The subset of each patient contains a varying number (between 9 and 42, 27.25 \(\pm\) 9.68 on average) of.edf files. Generally, the digitized recordings in most files are one-hour long. The beginning and end time of each seizure ictal case has been labeled based on expert judgments. We divided the continuously recorded data into 30 s EEG segments. There are totally 198 seizure ictal cases in the database. Due to the non-uniform electrode distribution during data acquisition, 181 out of 198 seizures which shared common 23 channels were utilized for method validation. For each seizure ictal case, only one 30 s segment was used in our experiment to avoid the overestimation of algorithm performance due to the similarity among several segments in one seizure ictal case. Because of the scarcity of epilepsy ictal segments (181 segments), we randomly downsampled interictal EEG data to rebalance the ratio between ictal and interictal data (a final ratio of 1:3 in our validation experiment). To take the signal variability over time into consideration, the segmented interictal data were evenly distributed over different periods of time with no overlap. More details can be fetched in [37].  
In preprocessing procedure, the EEG signal in each channel was normalized by Z-score. The normalized signals with zero-mean and one-standard deviation were prepared for subsequent feature extraction process.  
In feature extraction procedure, MMSE features were first extracted from each coefficient set in each EEG channel, constructing a 1610-dimensional MMSE feature vector (14 coefficient sets \(\times\) 5 entropy scales \(\times\) 23 EEG channels). As for energy features, the variance and mean absolute value were extracted from each wavelet coefficient set, constructing a 644-dimensional energy feature vector (14 coefficient sets \(\times\) 2 indicators \(\times\) 23 EEG channels). In summary, a 2254-dimensional feature vector was constructed for each EEG segment.  
In the feature selection and classification procedure, a specific classification model was developed for each patient. The \"leave-one-out\" cross-validation strategy was also applied to validate the proposed RR-DTDWT algorithm. We firstly held out one EEG segment as test data and all remaining EEG segments of all patients were allocated to the training set. Although a total number of 181 cases of seizure ictal EEG segments were contained in the experiment database, for each patient, very few cases of ictal seizures were available, which could not contribute to a precise feature correlation used for feature score calculation. Therefore, the AGRM-based feature score was pre-calculated using all EEG data in training set. The whole procedure was independent of the testing set. Due to the high dimensionality of original feature space, only the leading 50 features were selected. As for classification, due to the huge individual difference of EEG characteristics, the training data of the same patient as test data were used for SVM model training. The model trained in our work can be viewed as a patient-specific model, which is consistent with most automated seizure detection studies in the literature [7, 38, 39]. Similarly, we employed sensitivity, specificity, precision, accuracy and F1 score as classification evaluation metrics.