# Automatic Detection of Electrodermal Activity Events during Sleep
###### Abstract  
Currently, there is significant interest in developing algorithms for processing electrodermal activity (EDA) signals recorded during sleep. The interest is driven by the growing popularity and increased accuracy of wearable devices capable of recording EDA signals. If properly processed and analysed, they can be used for various purposes, such as identifying sleep stages and sleep-disordered breathing, while being minimally intrusive. Due to the tedious nature of manually scoring EDA sleep signals, the development of an algorithm to automate scoring is necessary. In this paper, we present a novel scoring algorithm for the detection of EDA events and EDA storms using signal processing techniques. We apply the algorithm to EDA recordings from two different and unrelated studies that have also been manually scored and evaluate its performances in terms of precision, recall, and \(F_{1}\) score. We obtain \(F_{1}\) scores of about 69% for EDA events and of about 56% for EDA storms. In comparison to the literature values for scoring agreement between experts, we observe a strong agreement between automatic and manual scoring of EDA events and a moderate agreement between automatic and manual scoring of EDA storms. EDA events and EDA storms detected with the algorithm can be further processed and used as training variables in machine learning algorithms to classify sleep health. electrodermal activity (EDA); wavelet transform; sleep; EDA events; EDA storms +
Footnote †: journal: Medical  
by changes in skin hydration levels. They are measured indirectly either by measuring the resistance or the conductance of the skin, after externally applying a voltage (excosomatic method), or by measuring the voltage of the skin without externally applying a voltage (endosomatic method). One commonly refers to the resulting signal as the electrodermal activity (EDA) signal [7, 8]. Despite being the less commonly used method, the endosomatic method is easier to implement [9] and provides \"more\" physiological insight. We refer the reader to [8] for more details about the two methods. The first experimental observations of EDA date back to 1860s [7]. However, using the EDA signal became popular only in recent years due to the rise of wearable devices able to record EDA signals [9, 10]. During wakefulness, the EDA signal has been used to monitor students' engagement in the classroom [11], to detect epileptic seizures [12], to assess task-induced stress [13], and to classify emotions [14], to cite but few applications. While the use of the EDA signal during wakefulness is relatively well established now, the use of the EDA signal for assessing sleep is a relatively new field and most algorithms have been developed in the last two years. During sleep, EDA signals are typically used to detect sleep stage [15], to predict sleep quality [16], and to predict the presence of OSA [17]. Analysing or scoring EDA sleep recordings mainly consists of detecting EDA events and EDA storms. EDA events are oscillations in the EDA signal with well-defined amplitude and frequency [18]. These oscillations are not uniformly spread throughout the night. They are more commonly found in non-REM sleep 2, slow wave sleep (SWS), and late-night REM sleep [19, 20]. Moreover, it has been observed that abnormal EDA patterns are caused by OSA [21]. EDA storms are time intervals with a significant number of EDA events. They have been first described by [PERSON] [22] as consisting of \"a minimum of 5 galvanic skin response peaks per minute for ten consecutive minutes of sleep\". Currently, several alternative definitions, both for EDA events and EDA storms, exist; we refer the interested reader to [23].