## 4 Results  
### Seizure Detection Performance  
The seizure detection performance of the considered classification features was evaluated on the CHB-MIT dataset using the leave-one-record-out cross-validation scheme described in Section 3.2. The mean values of seizure sensitivity, epoch sensitivity, specificity, latency and FPR were computed and are shown in Table 1. The epoch size was set to \(N=512\), which is equivalent to 4 s of EEG recording at a sampling rate of 128 Hz. It can be seen that in spite of their simplicity, the time domain-based features were able to achieve high seizure sensitivity. The nonlinear autocorrelation, in particular, achieves a mean seizure sensitivity of 94.91% (median 100%), with a mean FPR of 1.53 per hour (median 0.24). These results are comparable to those of other seizure detection studies using the same EEG dataset [20,24]. In [20], [PERSON] and [PERSON] explored the temporal evolution of spectral and spatial properties of the signals and reported a mean seizure sensitivity of 96% and a median FPR of 0.083 per hour. In [24], [PERSON] _et al._ explored different methods to combine time- and frequency-domain features of the multichannel EEG. The proposed algorithm achieved a mean seizure sensitivity of 80% and a mean FPR of 0.41 per hour. Note that in Table 1, the seizure sensitivity is higher than the epoch sensitivity (as expected). This is due to the fact that a seizure event is labeled as detected when there is at least one epoch being labeled as a seizure epoch. Furthermore, we also consider the effect of different epoch sizes on the system performance. The choice of the epoch size not only affects the temporal resolution of the features, but also directly determines how frequent the features are transmitted to the data server for classification. A large epoch size can reduce the number of data transmissions within a given period of time, but may potentially increase the detection latency. To evaluate the sensitivity of the detection performance to the epoch size, we varied the epoch size from \(N=384\) (3 s) to \(N=896\) (7 s), and the results are shown in Figure 2. In general, an increase in \(N\) results in an improvement in all performance metrics, except for seizure sensitivity and detection latency. The degradation in seizure sensitivity was mainly due to the reduced number of epochs during one seizure event and also partly due to the reduced number of training samples available to train the classifier. Table 2 shows the effect of the compression ratio (CR) on the detection performance when using the nonlinear autocorrelation as the feature type. It can be seen that as we increase the CR from 1:1 (uncompressed) to 20:1, the detection performance generally worsens in all considered metrics. At a CR of 20:1, we observe mild drops in the seizure sensitivity and specificity by 6% and 3%, respectively, relative to the uncompressed results. More alarmingly, we see a drastic increase in the FPR from 1.53 to 29.64 per hour. Such degradation in seizure detection performance is expected, since more and more information in the EEG is lost as we increase the CR. The effect of an imperfect wireless channel on the seizure detection performance of different data reduction approaches was assessed. The results are shown in Figure 3 as a function of the BER, which ranges from \(10^{-5}\) to \(10^{-2}\). Nonlinear autocorrelation was used as the classification features for all three approaches. For the EEG compression approach, a CR of 4:1 was used. Among the three approaches, the