# Automatic Detection of Electrodermal Activity Events during Sleep
## 5 Conclusions  
We presented an algorithm that automatically scores EDA events and EDA storms. To our knowledge, a scoring algorithm for EDA recordings obtained by the endosomatic method has not been developed previously. Including EDA in sleep studies is not the norm because of its relative novelty in sleep research and because scoring it is tedious and has not been properly standardised. We believe that the tool presented in this work provides valuable means for inclusion by allowing for more accurate and faster manual scorings. After its employment, sleep technologists can import automatic scoring into the scoring software and briefly check whether wrongly detected EDA events need to be removed. The next step is to make the algorithm clearly highlight areas of no interest and grey zones in the signal, where there is significant activity that might qualify as an event and needs the attention of an expert technologist. Moreover, due to its nature, the endosomatic method is much more suitable to be included in wearables, as it does require externally applying a voltage. Indeed, a few commercial devices already measure EDA. However, due to its complex nature, the signal has not been used for assessing sleep, yet. We believe that our work is the first stepping stone in this direction. ML models are the current state-of-the-art computational methods for sleep research. However, research on selecting training variables that effectively reflect physiological phenomena during sleep is scarce, particularly, in the area of EDA. Given the difficulty of and ambiguity in scoring endosomatic EDA sleep signals, the training dataset might not be sufficiently correct to guarantee a correct learning process. For this reason, we abstained from using ML and rather obtained the measures used in this work from the literature. Feature engineering is a pivotal step in optimising ML models. It has been shown that simpler and more interpretable ML models outperform complex models when accompanied by ad hoc feature engineering. Our algorithm detects EDA events and EDA storms, phenomena known to be associated with physiological processes during sleep, which can be used for feature engineering in the future. Our algorithm has been already successfully used to detect OSA. Thus, it opens the possibility of not only detecting but also monitoring the progression of OSA without invasive measurements. Moreover, we believe that better integrating EDA in sleep research will allow us to gain more physiological insight, for instance, by means of network physiology, which investigates the interactions between different physiological systems. Although sleep studies record many different physiological signals, network physiology has been rather neglected. Investigating changes in the topology of the physiological network when EDAevents and EDA storms occur will help to better characterise the processes underlying thermoregulation and understand its interaction with other physiological systems. Thus, in the future, we believe that automatic scoring algorithms, such as the one presented in this work, will much aid the work of manual scorers, ultimately taking over this tedious task. Their use will also allow for more thorough standardisation of scoring and, thus, better interpretability of scoring results from different studies. Finally, they will also be used to optimise ML algorithms for better determining sleep stages and disease severity, while allowing for a better understanding of underlying physiological processes by facilitating the analysis of the correlation between different physiological signals relevant to sleep. We therefore see the work presented in this paper as a first stepping stone in this direction. Conceptualisation, J.P., E.A., S.L.N.A.H. and E.S.A. ; methodology, J.P., E.A. and S.L.N.A.H. ; validation, J.P. and S.L.N.A.H. ; software, J.P., E.A. and S.L.N.A.H. ; formal analysis, J.P., E.A. and S.L.N.A.H. ; investigation, J.P., E.A., S.L.N.A.H and E.S.A. ; data curation, J.P. and T.S.