Towards a user-friendly sleep staging system for polysomnography part I: Automatic classification based on medical knowledge

Abstract

Abstract Manual sleep scoring is a time-consuming task that requires a high level of medical expertise. For this reason, a number of automatic sleep scoring algorithms have recently been implemented. However, their use by physicians remains limited for various reasons: a lack of transparency of the approach used, insufficient heterogeneity among the patients used for testing, or a lack of practicality. This paper presents a system for facilitated sleep scoring that will overcome these limitations. The proposed system, a user-friendly tool based on electrophysiological channels, was trained and tested on large datasets of 300 and 100 distinct recordings from patients with various sleep disorders. The method replicates the manual sleep scoring process, in accordance with the American Academy of Sleep Medicine (AASM) guidelines and generates patient-dependent sleep scoring (using the SATUD system). For an improved level of precision and confidence with regard to scoring, our approach also provides a table that gives indications about the confidence level of the algorithm when scoring sleep. In contrast to recent deep learning approaches, the algorithms used were chosen for their resilience and as they are easy to understand. Medical knowledge was included in the process as much as possible. Results showed that the system is consistent with manual scoring (mean Cohen's Kappa of 0.69 and accuracy rate of 77.8%). It proves that a facilitated interpretation of the model, crucial in such fields as sleep diagnosis, can be provided when using automatic tools. This new system thereby generates sleep scoring decision support tools, which should easily contribute to significant time-saving and help sleep specialists to perform sleep diagnosis.