Sleep modelled as a continuous and dynamic process predicts healthy ageing better than traditional sleep scoring

Abstract

BackgroundIn current clinical practice, sleep is manually scored in discrete stages of 30-s duration. We hypothesize that modelling sleep automatically as continuous and dynamic process predicts healthy ageing better than traditional scoring. MethodsSleep electroencephalography of 15 young healthy subjects (aged ≤40 years) was used to train the modelling method. Each 3-s sleep mini-epoch was modelled as a probabilistic combination of wakefulness, light and deep sleep. For 79 healthy sleepers (aged 20–77 years), 15 sleep features were derived from manual traditional scoring (manual features), 7 from the automatic modelling (automatic features) and 24 from a combination of automatic modelling with traditional scoring (combined features). Age was predicted with seven multiple linear regression models with i) manual, ii) automatic, iii) combined, iv) manual + automatic, v) manual + combined, vi) automatic + combined, and vii) manual + automatic + combined sleep features. Using the same seven sleep feature groups, two support vector machine and one random forest classifiers were used to discriminate younger (aged <47 years) from older subjects with fivefold cross-validation. Adjusted coefficients of determination (adj-R2) and average validation accuracy (ACC) were used to compare the linear models and the classifiers. ResultsThe linear model and the classifiers using only manual features achieved the lowest values of adjusted coefficient of determination and classification validation accuracy (adj-R2 = 0.295, ACC = 63.00% ± 16.22%) compared to the ones using automatic (adj-R2 = 0.354, ACC = 65.83% ± 9.39%), combined (adj-R2 = 0.321, ACC = 63.42% ± 8.78%), manual + automatic (adj-R2 = 0.416, ACC = 67.00% ± 8.60%), manual + combined (adj-R2 = 0.355, ACC = 72.17% ± 12.90%), automatic + combined (adj-R2 = 0.448, ACC = 65.92% ± 7.97%), and manual + automatic + combined sleep features (adj-R2 = 0.464, ACC = 70.92% ± 10.33%). ConclusionsContinuous and dynamic sleep modelling captures healthy ageing better than traditional sleep scoring.