With a large amount of data collected from studies of sleep quality and based on the physiological signals (PS) that are collected, it is possible to use mechanisms that intelligently detect sleep disorders such as arousals (ARS). In this detection, the triggers can be present in any of the PS or can occur from their combinations. Thus, with the characterization of the PS and with a considerable number of examples, it is possible to generate a model that recognizes ARS zones in new samples. In this way, by segmenting the signals and decomposing them into variable frequency bands, thanks to the application of discrete wavelet transform (DWT), it is possible to characterize the contributions of each PS in time and frequency. The features that are extracted give information about the contributions in frequency and time of each PS. Then these characteristics feed a neural network model that iteratively learns the best non-linear function that approximates the input to its corresponding label. Once the methodology was tested, with less than 3% of the training data, it was possible to reach an Area Under Precision-Recall Curve (AUPRC) of 0.261.