# Emotion Recognition from ECG Signals Using Wavelet Scattering and Machine Learning
## 5 Conclusions and Future Work  
The extraction of features of the ECG signal by means of wavelet scattering has been shown to improve the performance of classic machine learning algorithms to classify emotions in the arousal and valence dimensions compared to features in the time and frequency domain. The wavelet scattering algorithm allows the ECG signal to be analyzed at different temporal space scales and simultaneously in the time and frequency domains. This allows increasing the separability and differentiability of the signals and patterns of two different classes. The method was validated using the AMIGOS database to classify emotions in two dimensions: arousal and valence. This study demonstrates a higher overall performance than previous works. The classifier comparison shows that the Ensemble Bagged Tree classifier can be a good choice for emotion classification from ECG signals.  
Several systems have been proposed to visualize mind states of a human subject based on EEG signals [55]. As a future work, we intend to build a system capable of discriminating emotions in real time using ECG signals. Currently, we are in the process of building a wearable device consisting of first-layer clothing with embedded electrodes for ECG signal monitoring ([[https://www.auradt.com/productos/primera-capa](https://www.auradt.com/productos/primera-capa)]([https://www.auradt.com/productos/primera-capa](https://www.auradt.com/productos/primera-capa)), accessed on 5 May 2021). For increased comfort and long-term applications, we eliminated the need of conductive gels and skin preparation by integrating dry electrodes in a first-layer t-shirt developed by the local textile manufacturer ACTAS-Grupo CLER (see Figure 7).  
The prototype is capable of measuring, storing, and streaming data, and we are planning to incorporate real-time signal processing and implement the emotion recognition algorithm in the near future. The major parts and components of the system with a ground station to process and classify the ECG can be represented in the high-level block diagram shown in Figure 8.  
Wavelet analysis has been successfully applied to other biopotential signals such as EMG [56], thus, the proposed methodology is also a promising alternative for EMG-based pattern recognition problems [57].  
Conceptualization, F.C., C.P. and M.R.-F.; formal analysis, A.S.; funding acquisition, C.P. and M.R.-F.; investigation, A.S. and F.C.; resources, C.P. and M.R.-F.; software, A.S.; supervision, F.C., C.P. and M.R.-F.; writing--original draft, A.S.; writing--review and editing, F.C., C.P. and M.R.-F. All authors have read and agreed to the published version of the manuscript. This research was funded by ATCAS-Grupo CLER and FONDECYT grant No. 1181094.  
Publicy available datasets were analyzed in this study. This data can be found here: [[http://www.eecs.qmul.ac.uk/mmv/datasets/amigos/index.html](http://www.eecs.qmul.ac.uk/mmv/datasets/amigos/index.html)]([http://www.eecs.qmul.ac.uk/mmv/datasets/amigos/index.html](http://www.eecs.qmul.ac.uk/mmv/datasets/amigos/index.html)), accessed on 6 May 2019.  
The authors declare no conflict of interest.