Sleep CLIP: A Multimodal Sleep Staging Model Based on Sleep Signals and Sleep Staging Labels.

Since the release of the contrastive language-image pre-training (CLIP) model designed by the OpenAI team, it has been applied in several fields owing to its high accuracy. Sleep staging is an important method of diagnosing sleep disorders, and the completion of sleep staging tasks with high accuracy has always remained the main goal of sleep staging algorithm designers. This study is aimed at designing a multimodal model based on the CLIP model that is more suitable for sleep staging tasks using sleep signals and labels. The pre-training efforts of the model involve five different training sets. Finally, the proposed method is tested on two training sets (EDF-39 and EDF-153), with accuracies of 87.3 and 85.4%, respectively.

Sleep staging classification based on a new parallel fusion method of multiple sources signals.

Objective.In the field of medical informatics, sleep staging is a challenging and time consuming task undertaken by sleep experts. The conventional method for sleep staging is to analyze Polysomnograms (PSGs) recorded in a sleep lab, but the sleep monitoring with polysomnography (PSG) severely degrades the sleep quality. Despite recent significant progress in the development of automatic sleep staging methods, building a good model still remains a big challenge for sleep studies due to the data-variability and data-inefficiency issues. Electrooculograms (EOGs) and electrocardiograms (ECGs) and are much easier to record and may offer an attractive alternative for home sleep monitoring. PSGs from the Sleep Heart Health Study database were used. This study aims to establish an new automatic sleep staging algorithm by using electrooculogram (EOG) and electrocardiogram (ECG).Approach.First, the heart rate variability (HRV) is extracted from EOG with the Weight Calculation Algorithm and an 'NRRD' RR interval detection algorithm. Second, three feature sets were extracted from HRV segments and EOG segments: time-domain features, frequency-domain features and nonlinear-domain features. The frequency domain features and nonlinear-domain features were extracted by using Discrete Wavelet Transform, Autoregressive (AR), and Power Spectral entropy, and Refined Composite Multiscale Dispersion Entropy. Third, a new 'Parallel Fusion Method' (PFM) for sleep stage classification is proposed. Three kinds of feature sets from EOG and HRV segments are fused by using PFM. Fourth, Extreme Gradient Boosting (XGBoost) is employed for sleep staging.Main results.Our experimental results show significant performance improvement on automatic sleep staging on the target domains achieved with the new sleep staging approach. The performance of the proposed method is tested by evaluating the average accuracy, Kappa coefficient. The average accuracy of sleep classification results by using XGBoost classification model with PFM is 83% and the kappa coefficient is 0.7. Experimental results show that the performance of the proposed method is competitive with the most current methods and results, and the recognition rate of S1 stage is significantly improved.Significance.As a consequence, it would enable one to improve the quality of automatic sleep staging models when the EOG and HRV signals are fused, which can be beneficial for monitor sleep quality and keep abreast of health conditions. Besides, our study provides good research ideas and methods for scholars, doctors and individuals.