## 3 Method
### Detailed Analysis
#### 3.3.2 Machine Learning Methods  
In order to design reliable emotion recognition systems, it is particularly important to select appropriate and effective signal features. When designing affective computing systems, one of the most important considerations for application functionality are their simplicity and acceptable computational speed, thus making them suitable for real-time applications. Therefore, we use simple time-domain and frequency-domain features that do not require complex transformations and heavy computations. Most of the characteristics of the ECG signal are based on the analysis of the P, Q, R, S and T waves of the recorded signal, including several statistical features calculated from the amplitude and width of the P, Q, R, S and T wavelets. Subsequently, heart rate variability (HRV) is calculated based on the detected R peak, and further features are extracted from the resulting signal, including the mean and root-mean-square deviation from HRV. In addition, the slope of the linear regression fitted to the appearance of the R-peak was calculated IBI. Based on [46], wavelet transformed decomposition coefficients were also extracted, using 8 th order Daubechies wavelets applied to detect and align the R-peaks. Figure 2: Schematic diagram of deep convolutional neural network. For GSR, include features such as signal mean, standard deviation, kurtosis, or skewness (e.g., [47; 48]). In other cases, researchers focused on event-related features of GSR. Event-related features refer to the properties of short-term responses, such as the presence or absence of an SCR, when seconds after the presence of a stimulus (such as an image or sound). In this sense, SCR can be automatically detected and features extracted from longer time windows. Phases skin conductance response (SCR) and the sum of SCR amplitude, SCR peak count, mean SCR rise time [49; 50]. Furthermore, tonic skin conductance level (SCL). Power Spectral Density (PSD) estimation in the frequency domain using [PERSON]'s method, which is the most commonly used algorithms to obtain a frequency domain representation of the signal. Previous studies have considered the statistical aspects (variance, range, signal amplitude region, skewness, kurtosis, harmonic summation) and spectral power of the five frequency bands, as well as their minimum, maximum, and variance [51]. The physiological signal changes without a specific pattern and is highly random. Much of the information cannot be judged on the time domain, so it is also analyzed in the frequency domain. The signal frequency band is generally divided into very low frequency band (VLF = [0.0022-0.04] Hz), low frequency band (LF = [0.04-0.15] Hz) and high frequency band (HF = [0.15-0.40] Hz). The PSD method extracts the spectral power of each frequency band as the spectral characteristics of the original signal with the following equation. \[\text{Power}=\int_{f_{1}}^{f_{2}}\ PSD(f)df, \tag{3}\]  
Power calculated in the VLF, LF and HF band, total power in the entire frequency range (TP), power calculated in the power range LF band as a proportion of that calculated in the HF band (LF/HF). The proportion of power LF band calculated in the power range to that calculated in the whole band (LF/TF), LF power normalized to the sum of LF and HF power (nLF), and HF normalized to the sum of LF and HF power (nHF). The nonlinear entropy domain feature, which can reflect the complexity and uncertainty of physiological signals, and has a wide range for applications in computational studies of emotions based on physiological signals. The extracted entropy values help to quantify the regularity of the signal, which can be applied to emotion recognition. This section applies three types of entropy domain features, including information entropy, multi scale entropy, and refined composite multi scale dispersion entropy (RCMDE) [52]. The extracted features are shown in the Table 1.  
Figure 3: Feature layer fusion recognition accuracy of different feature dimensions.