# Asian Affective and Emotional State (A2 ES) Dataset of ECG and PPG for Affective Computing Research
## 2 Related Works  
ECG and PPG are popular modalities for ERS. Many studies using these modalities have achieved promising results in representing human emotions. [PERSON] et al. [22] utilised ECG signals to recognise positive and negative valence states in children with autism spectrum disorder (ASD). The proposed system successfully obtained an accuracy of 81%. Meanwhile, a PPG-based ERS with a convolutional neural network (CNN) is proposed in [38] for the fast emotional recognition of valence and arousal. The system achieved a 75.3% and 76.2% valence and arousal accuracy within 1.1 s for short-term emotion recognition. In 2021, [PERSON] et al. developed a real-time ERS to automate a music selection system using emotion recognized based on PPG signals [39]. An accuracy of 91.81% was achieved utilising features extracted from phase-space geometry ([PERSON]'s analysis). For binary classification and multiclass classification, maximum accuracy rates of 96.67% and 91.11% were achieved, respectively. [PERSON] et al. evaluated the performance of an ECG-based ERS with the features extracted using two distinct feature extraction toolboxes, TEAP and AUBT, and achieved an accuracy of up to 65% [17]. ECG and PPG are also commonly integrated with other physiological signals as a strategy to improve ERS performance. In [40], ECG was used together with temperature (TEMP), galvanic skin response (GSR), electromyography (EMG), respiration (RESP), accelerometer signals, and facial expressions to recognise dimensional emotional states (high arousal and high valence (HAHV), high arousal and low valence (HALV), low arousal and high valence (LAHV), and low arousal and low valence (LALV)), arousal, and valence. The accuracy obtained was in the range of 40 to 70%. [PERSON] et al. [41] proposed stress detection using ECG and GSR signals and categorised them using two approaches: machine learning and deep learning. Their work successfully achieved the best accuracy of 95%. [PERSON] et al. proposed a multimodal fusion ERS that includes EEG and ECG [42]. The fusion ERS was better than the single-modality ERS, with an accuracy for valence of 85.38% and for arousal of 77.52%. In [43], another emotion-based music recommendation engine system was built using a combination of PPG and GSR signals from wearables. The emotional information from PPG and GSR was fed to a collaborative and content-based recommendation engine, and the best accuracy rate obtained exceeded 70%. [PERSON] et al. [44] also proposed an ERS using PPG and GSR from wearable devices. The ERS recognises three emotions: amusement, sadness, and neutral. The system successfully recognised all three emotions, with a testing accuracy of up to 100%. In [45], a deep physiological affect network, which is a robust physiological model that recognises human emotions using PPG and EEG signals, is presented. The proposed system achieved 78.72% and 79.03% overall accuracy for recognising valence and arousal emotions, respectively. Although both ECG and PPG signals can be used independently or integrated with other physiological signals, they can also be fused together to upgrade the robustness and improve an ERS's performance. For example, [PERSON] et al. [46] proposed a group-based individual response specificity (IRS) to improve the emotion recognition performance by fusing the statistical features from ECG and PPG with GSR. The highest performance achieved was 78.06% using the MLP classifier. The authors of [47] also proposed an automatic ERS with the fusion of the ECG and PPG features and successfully achieved the best performance of 85.70%. Additionally, the fusion of the ECG and PPG features was also used in [48]. They classified three emotions, positive, neutral, and negative, using a CNN and achieved an accuracy of 75.40%. In affective computing, existing datasets that collect data from either a single modality or a multimodality using physiological and physical signals are important for the advancement of this field.