## 3 Results
### Normalization Used for Napcc and Optimum Number of Napcc  
In this section, the performance of the proposed method was evaluated by changing the number of dimensions of NapCC presented to MLP-ANN to investigate how the number of dimensions of NapCC obtained based on the use of these normalization methods influenced the automatic extraction performance of SBEs. Figure 3 shows the relationship between the mean F1 score and the number of NapCC dimensions when sigmoid or mean normalizations were used in Exp-1. Figure 4 shows the respective results for Exp-2. It can be observed from Figure 3 that the F1 score of sigmoid normalization was approximately 0.5% and 1% higher than that of mean normalization in Exp-1 and Exp-2, respectively. There were no large differences in the standard deviation between the sigmoid and the mean normalizations. Given that sigmoid normalization seems to perform better based on the results obtained in these experiments, we employed the sigmoid normalization in the NAPCC extraction process in this study. According to the results associated with the use of sigmoid normalization in Figure 3, it was confirmed that the maximum score in 13 dimensions was obtained when the mean F1 scores in each dimension obtained based on Exp-1 and Exp-2 were multiplied. The 13-dimensional NAPCC was determined as the optimal characteristic vector used in the proposed method in this study. In the following sections, this characteristic vector was used for the evaluation of the performance of the proposed method and for the comparison of the proposed method with the previous method. Figure 4 shows (as examples) (a) 20 s of recorded data, (b) NAP output obtained by analyzing the recorded data, (c) trained MLP-ANN output results, and (d) labeling results by three annotators (1 for SBE sections, 0 for non-SBE sections). It can be confirmed from these figures that the NAP output and the trained MLP-ANN output reflected even the information of SBEs that appeared to be buried in background noise. Evaluation of the Performance of the Proposed Method and Comparison of the Proposed Method with Our Previous Method  
We developed an SBE/non-SBE classification technique based on MLP-ANN, which was used as a time-series classifier [12]. When this MLP-ANN was used as a subject-independent classifier, we showed that our previous method can classify low-intensity SBEs and low-intensity non-SBEs from sleep sounds recorded in noisy environments with an average accuracy of 75.10%. The performance of our previous technique was compared with that of other recent techniques. Even though we focused on the detection of low  
Figure 4: SBE detection results based on the proposed method: (**a**) 20 s of recorded data, (**b**) NAP output obtained by analyzing the recorded data, (**c**) trained MLP-ANN output results, (**d**) results of labeling by three annotators (1 for SBE sections, 0 for non-SBE sections). intensity SBEs in sleep sounds, the classification accuracy was as good as that attained with recent techniques. In this study, we compared the performance of the proposed method with that of this method. For this purpose, Exp-1 and Exp-2 were performed using the same parameters adopted in our previous work. Tables 3 and 4 show the performance evaluation results of the proposed and the conventional methods for Exp-1 and Exp-2, respectively. As mentioned in Section 3.1, in consideration of the initial value dependence of MLP-ANN, leave-one-out cross-validation was conducted 10 times, and performance evaluations of the proposed method and previous method were conducted based on trial results that maximized the F1 score. According to Table 4, in the case of Exp-1, we found that the mean value was larger than that of our previous method, and the standard deviation of the proposed method was smaller than that of our previous method. As shown in Table 5, the results of Exp-2 yield the same trend as that of Exp-1.