Sleep Staging Algorithm Incorporating Multi-Threhold Neighborhood Polar Pattern Statistics

Sleep plays a vital role in human life, and its quality has a direct impact on overall health. Sleep staging is a crucial process and a key indicator used to evaluate sleep quality. This paper proposes a sleep staging method based on statistical mode of multi-threshold neighborhood extreme (SMNE). We employ the discrete wavelet transform (DWT) in conjunction with a data enhancement algorithm to preproces the EEG signals, improving their quality through a combined approach based on signal-to-noise ratio(SNR) assessment and signal overlap analysis. Then, the extremes of EEG signal are classified into 5 distinct states. Multi-threshold is applied to differences in 5-state extreme value matrix to define and extract patterns. These patterns are then statistically encoded. The extracted codes, representing the various patterns, are input into 5 weighting layers. Then, the extracted codes are fed into grey wolf optimization (GWO) to denote threshold for SNR and SMNE feature. Finally, the features derived from this process are input into a Random Forest (RF) classifier. The SleepEDFx dataset shows accuracy of 94.6%, kappa coefficient of 0.92 and F1-score of 89.3%. For the SleepEDF-20 dataset, the corresponding metrics are accuracy of 96.3%, kappa of 0.94 and F1-score of 94.2%. Meanwhile, the ISRUC-Sleep dataset achieves accuracy of 88.5%, kappa value of 0.83 and F1-score of 86.5%.