# Asian Affective and Emotional State (A2 ES) Dataset of ECG and PPG for Affective Computing Research
## 1 Introduction
Several databases are composed of data collected using nonportable devices and expensive technology; meanwhile, the databases for emotion recognition through EEG and ECG (DREAMER) [14], wearable stress and affect detection (WESAD) [29], and emotion recognition smartwatches (ERS) [30] are compilations of signals collected from wireless, low-cost, and off-the-shelf devices. These databases have been utilised in studies by researchers with different levels of success [13; 18; 20]. In past research, the issues of racial inequities and bias toward wearable technology, particularly for those with darker skin tones, have been raised [31; 32]. Those with darker skin tones, tattoos, or arm hair have lower accuracy when using wearable devices that track their health activity or monitor their heart conditions. [PERSON] et al. [32] recommended that researchers should be aware of racial bias and disseminate study results across demographic subgroups to minimise bias. To the best of our knowledge, there are no existing physiological affective datasets collected from wearable devices that look at this issue and include multi-Asian ethnicities. For example, the DEAP dataset comprises data from European participants [13], while the MPED dataset consists of data from Chinese participants only. Thus, this paper introduces the Asian Affective and Emotional State (A2 ES) Database consisting of ECG and PPG recordings of 47 participants from various Asian ethnicities. Both ECG and PPG recordings have been reported to be affected by skin colour [31; 33]. An ECG is used to detect the heart's electrical activity, which starts from the sinoatrial node to contract the heart muscles for continuing the blood pumping action in the body [34]. As illustrated in Figure 1, the ECG comprises three primary components: P wave, QRS wave, and T wave. On the other hand, PPG is a low-cost and noninvasive way to measure blood volume changes in a human during heart activity. PPG has two main components: incoherent light source and photoreceiver [35]. A typical PPG signal element is shown in Figure 2, complete with the systolic period associated with blood in-rush, the diastolic period associated with relaxation, and the dicrotic notch associated with pulse reflection [36]. The subjects that participated in the data collection were exposed to 25 audio-visual stimuli to elicit specific emotions. The self-assessment ratings from the participants and the list of the 25 stimuli are also presented here. The applicability of the A2 ES's ECG and PPG data for building an ERS was tested using machine learning and deep learning approaches. Five machine learning algorithms, namely, support vector machine (SVM), naive Bayes (NB), K-nearest neighbours (KNN), decision tree classifier (DT), and random forest (RF), were applied. The ECG-based ERS built using SVM and the PPG-based ERS built using RF were found to be the best. The small data size did not suit deep learning, and poor performances were reported. The rest of this paper is organised as follows. In Section 2, related works, including ECG- and PPG-based ERS, as well as ECG- and PPG-based databases, are described. The  
Figure 1: The P, QRS, and T waves in a single cycle of a standard ECG reading. Figure 2: Typical transmission of a PPG signal with the systolic period. Reproduced with permission from [37]. experiment protocol is covered in Section 3, which includes the stimuli selection procedure, participants' details, and data collection setting and protocol. Section 4 describes the data preprocessing and feature extraction process. In Section 5, an evaluation of the ECG- and PPG-based ERS performances are presented. A concluding discussion and future work directions are provided in Section 6.