# Multimodal Classification of Anxiety Based on Physiological Signals
## 6 Conclusions  
Our study used the WESAD dataset and the combination of three physiological signals--ECG, EDA, and EMG--collected under a neutral condition to classify different anxiety levels, with particular emphasis on more severe and less frequent levels of these mental conditions. The physiological signals were processed, and relevant features were extracted for classification purposes. A supervised feature selection algorithm, seven machine learning algorithms, and four data balancing techniques were analyzed. Concerning the features selected for the classification task, we concluded that ECG-extracted features were revealed to have more importance than features extracted from EMG and EDA signals. Additionally, the balancing techniques have been shown to improve, in general, the classification results. Two main shortcomings of this study are the reduced number of participants, which restricts the data's representativeness, and the neutral condition of the data set used, whose protocol was not specifically designed for this study. Although it is already challenging to achieve a neutral condition, the fact that the participants knew they would face different stimuli after the neutral condition may lead them into an anticipation state. Additionally, the fact that half the participants were sitting down and half were standing up during the neutral condition can influence the physiological signals, especially the EMG signal. Despite the limitations, our findings allowed us to successfully classify anxiety in a neutral state, providing a deeper understanding of this condition. Through our outcomes, we concluded that it is viable to use classification models in applications connected with wearable devices (e.g., watches, smartphones), which can help detect and manage anxiety. In future work, there are some topics to be considered further in this line of research. At first, by considering a protocol specifically designed for attaining neutral conditions and engaging more participants. In addition, by addressing anxiety classification tasks from a multiclassification perspective or as ordinal classification tasks. Concerning the feature selection techniques, supervised feature selection, such as sequential feature selection or filter methods, may be an option as well. Regarding the data balancing techniques, when facing a data imbalance problem, the use of techniques that combine over/under-sampling methods and the integration of those into machine learning algorithms are also worth trying. Further tuning the balancing parameters and the classifier hyperparameters may improve the results. Conceptualization, R.S. and R.P.R. ; methodology, M.V., R.S., R.P.R. and T.S. ; software, M.V. ; data analysis, M.V. ; validation, R.S., R.P.R. and T.S. ; visualization, M.V. ; supervision, R.S., R.P.R. and T.S. ; writing--original draft preparation, M.V. ; writing--review and editing, M.V., R.S., R.P.R. and T.S. All authors have read and agreed to the published version of the manuscript. Funding: This work was funded by national funds through FCT--Fundacao para a Ciencia e a Tecnologia, LP., under the Scientific Employment Stimulus--Individual Call--CEECIND/03986/2018 (R.S. ), CEECINST/00013/2021 (R.S. ), and CEECINST/00123/2021 (T.S.). It was also supported by FCT through national funds, within the IEETA/UA R&D unit (UIDB/00127/2020) and i3S (UIDB/04293/2020), and the FEDER--Fundo Europeu de Desenvolvimento Regional funds through the COMPETE 2020--Operational Programme for Competitiveness and Internationalisation (POCI), Portugal 2020, and by the FCT project LA/P/0063/2020. Not applicable. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study [1]. Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest. Appendix A Tables Related to Extracted and Selected Features and with Results from the Classification Tasks  
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