Visual inspection of Polysomnography (PSG) recordings by sleep experts, based on established guidelines, has been the gold standard in sleep stage classification. This approach is expensive, time-consuming, and mostly limited to experimental research and clinical cases of major sleep disorders. Various automatic approaches to sleep scoring have been emerging in the past years and are opening the way to a quick computational assessment of sleep architecture that may find its way to the clinics. With the hope to make sleep scoring a fully automated process in the clinics, we report here an ensemble algorithm that aims at not only predicting sleep stages but of doing so with an optimized minimal number of EEG channels. For that, we combine a genetic algorithm-based optimization with a classification framework that minimizes the number of channels used by the machine learning algorithm to quantify sleep stages. This resulted in a sleep scoring with an F1 score of 0.793 for the fully automated model and 0.82 for the model trained on 10 percent of the unseen subject, both with only 3 EEG channels. The ensemble algorithm is based on a combination of extremely randomized trees and MiniRocket classifiers. The algorithm was trained, validated, and tested on night sleep PSG data collected from 7 subjects. Our approach's novelty lies in using the minimum information needed for automated sleep scoring, based on a systematic search that concurrently selects the optimal-minimum number of EEG channels and the best-performing features for the machine learning classifier. The optimization framework presented in this work may enable new flexible designs for sleep scoring devices suited to studies in the comfort of homes, easily and inexpensively. In this way facilitate experimental and clinical studies in large populations.

Visual inspection of Polysomnography (PSG) recordings by sleep experts based on established guidelines has been the gold standard in sleep stage classification. This approach is expensive, time consuming and mostly limited to experimental research and clinical cases of major sleep disorders. Various automatic approaches to sleep scoring have been emerging in the past years and are opening the way to a quick computational assessment of sleep architecture that may find its way to the clinics. With the hope to make sleep scoring a fully automated process in the clinics, we report here an ensemble algorithm that aims at not only predicting sleep stages but of doing so with an optimized minimal number of EEG channels. For that, we combine a genetic algorithm based optimization with a classification framework that minimizes the number of channels used by the machine learning algorithm to quantify sleep stages. This resulted in a scoring with an F1 score of 0.793 for the fully automatic model and 0.806 for the model trained on 10 percent of the unseen subject, both with only 3 EEG channels. The ensemble algorithm is based on a combination of extremely randomized trees and MiniRocket classifiers. The algorithm was trained, validated and tested on night sleep PSG data collected from 7 subjects. The novelty of our approach lies on the use of the minimum information needed for automated sleep scoring, based on a systematic search that concurrently selects the optimal-minimum number of EEG channels and the best performing features for the machine learning classifier. The optimization framework presented in this work may enable new designs for sleep scoring devices suited to studies in the comfort of the homes, easily and inexpensively and in this way facilitate experimental and clinical studies in large populations.