# Classification of Brainwaves for Sleep Stages by High-Dimensional FFT Features from EEG Signals
## 3 Experimental Results
### Classification of Sleep-EDF Dataset Expanded (197 Recordings)  
The results of applying our method against the latest, extended version of the Sleep-EDF database, show that in contrast to the first version of the database which consisted of 61 recordings (version 1), the latest version consists of 197 recordings (version 2, released in 2018). It has been studied in many recent papers (e.g., [27; 28], however, because of its large size, it is rarely studied as a whole (many papers which classified it are using only a small subset of it). Therefore, it is hard to compare the performance on it under the same or similar conditions. Instead of comparing performances, in this \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Length of Epoch (s)** & **\# of Epochs** & **\# of Classes** & **Accuracy (\%)** \\ \hline  & & & 6 & 86.60 \\ \cline{3-5}  & & & 5 & 88.60 \\ \cline{3-5} [PERSON] et al. 2017 & 30 & 126,699 & 4 & 91.00 \\ \cline{3-5}  & & & 3 & 94.50 \\ \cline{3-5}  & & & 2 & 97.40 \\ \hline  & & & 6 & 89.43 \\ \cline{3-5} [PERSON] et al. 2019 [22] & 30 & 127,512 & 5 & 90.48 \\ \cline{3-5}  & & & 4 & 92.24 \\ \cline{3-5}  & & & 3 & 94.23 \\ \cline{3-5}  & & & 2 & 97.85 \\ \hline  & & & 6 & 90.17 \\ \cline{3-5}  & & & 5 & 91.42 \\ \cline{3-5} Our method (Pz\_Oz) & 30 & 127,663 & 4 & 92.24 \\ \cline{3-5}  & & & 3 & 94.36 \\ \cline{3-5}  & & & 2 & 97.79 \\ \hline  & & & 6 & 89.70 \\ \cline{3-5}  & & & 5 & 88.57 \\ \cline{3-5} Our method (Fpz\_Cz) & 30 & 127,663 & 4 & 90.02 \\ \cline{3-5}  & & & 3 & 92.69 \\ \cline{3-5}  & & & 2 & 97.13 \\ \hline  & & & 6 & 90.77 \\ \cline{3-5}  & & & 5 & 91.73 \\ \cline{3-5} Our method & 30 & 127,663 & 4 & 92.82 \\ \cline{3-5}  & & & 3 & 94.41 \\ \cline{3-5}  & & & 2 & 97.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison on Sleep-EDF dataset (R & K criteria, 2–6 classes). subsection we mainly analyzed the relationship between classification performance and the balance of classes. Table 4 contains the result of the classification experiment using our method. \"SC\" and \"ST\" found in the recording ID. The prefixes \"SC\" and \"ST\" stand for \"Sleep Cassette\" and \"Sleep Telemetry\", respectively. In this experiment, ten-fold cross validation was conducted for each recording. The average, highest, and lowest accuracies were 87.84%, 96.54%, and 37.03%, respectively. Since the accuracies are greatly affected by the degree of sample distribution among the classes in each recording, a large discrepancy exists between the highest and lowest accuracies. For example, in the recording SC4201, which achieved the highest accuracy, the AWA class occupies ~73% of the recording. In contrast, the lowest accuracy was achieved by ST7151 with a more even distribution between the classes (AWA:REM:S1:S2:S3:S4 = 104:143:78:304:142:126). This is even more clearly demonstrated in Figure 4, where we show the relationship between accuracy and degree of class imbalance (represented in this experiment by the standard deviation of class sizes in a recording). There appears to be an almost linear relationship (correlation coefficient was 0.9857).