## 4 Discussion
The first classifier tested was the linear classifier, which utilized linear discriminant analysis. The best accuracy was yielded at 8 min prior to SCA onset and was found to correlate with literature findings [33; 34]. However, the values obtained in this study were slightly lower than the values obtained in similarly-conducted studies (72.80% vs. 74.36%) [33]. This may be attributed to the signal quality, as the accuracy of a classifier depends on two factors; signal quality and the extracted features [35]. SVM classification was then conducted on the dataset and there was an improvement in the accuracy values obtained for both the linear and non-linear SVM classifiers, both of which yielding higher accuracy rates compared to the linear classifier (72.8% vs. 78.9% both obtained at the 8 min prior time-frame). The values obtained from the non-linear SVM classifier were found to correlate with findings obtained from a similar study conducted using the same two ECG database, although the findings obtained for accuracy (83.9% vs. 88.0%), sensitivity (91.5% vs. 92.0%), and specificity (82.5% vs. 84.0%) were slightly lower compared to the literature [34]. This slight variation in values may be due to interpatient variation, as in the literature only five patients each were used for the normal and SCA cohort, whereas the cohort numbers in this study were almost double for both the normal and SCA population. As time starts to approach the onset of SCA, the overall risk of SCA increases and, thus, the percentage at which the classifier will correctly classify a patient at risk of SCA would consequently increase [19]. Overall, the experimental results exhibited an increase in accuracy with each time window approaching SCA onset, particularly in the findings obtained from the non-linear SVM classifier. The performance of each classifier based on the isolated time-frames were then quantitatively evaluated by calculating the area under the receiver operating characteristic (ROC) curve. The higher the AUC, the better the classifier performance [36]. The highest overall AUC was obtained by the non-linear SVM classifier in the 2-min time-frame which exhibited an AUC of 0.88. However, there is an approximately 0.02 difference observed between the early time-frames (50 and 40 min prior) and the later time-frames (8 and 2 min prior), suggesting that the risk of SCA can be detected at an earlier time-frame with reasonable accuracy (See Table 3). Overall, the non-linear SVM had the best performance across all time-frames compared to its linear counterparts, which may be due to the fact that it takes into account the non-linearity of ECG signals. Across all three classifiers, the highest overall accuracy was obtained at 2 min prior to SCA onset. This is a time-frame in which a significant difference between the ECG recordings obtained from a patient at risk of SCA differing from a normal patient would be expected. This is due to the increase in the frequency of PVCs over time. Thus, more frequent PVCs at 2 min prior would result in larger compensatory pauses which, in turn, affects the HRV features of patients at risk of SCA, resulting in higher accuracies in classifying SCA and normal patients. A similar conclusion was made across various similarly conducted studies which also discovered that the 2-min time window provided the most useful information for SCA detection [19; 33; 34; 35]. However, it is important to note that the overall aim of SCA detection is to identify patients at risk as early as possible in order to prevent serious repercussions, such as irreversible neurological damage, and even death. Thus, 2 min prior to the onset of SCA is not enough time to allow a patient to respond and seek management. However, it was also observed that reasonable accuracy rates of 70-80% can be obtained across all three classifiers as early as 50 min prior to the onset of SCA.