Quantified Sleep: Machine learning techniques for observational n-of-1 studies

• URL: http://arxiv.org/abs/2105.06811v1
• Date: Fri, 14 May 2021 13:13:17 GMT
• Title: Quantified Sleep: Machine learning techniques for observational n-of-1 studies
• Authors: Gianluca Truda
• Abstract summary: This paper applies statistical learning techniques to an observational Quantified-Self study to build a descriptive model of sleep quality. Sleep quality is one of the most difficult modelling targets in QS research, due to high noise and a large number of weakly-contributing factors.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper applies statistical learning techniques to an observational Quantified-Self (QS) study to build a descriptive model of sleep quality. A total of 472 days of my sleep data was collected with an Oura ring and combined with lifestyle, environmental, and psychological data. Such n-of-1 QS projects pose a number of challenges: heterogeneous data sources; missing values; high dimensionality; dynamic feedback loops; human biases. This paper directly addresses these challenges with an end-to-end QS pipeline that produces robust descriptive models. Sleep quality is one of the most difficult modelling targets in QS research, due to high noise and a large number of weakly-contributing factors. Sleep quality was selected so that approaches from this paper would generalise to most other n-of-1 QS projects. Techniques are presented for combining and engineering features for the different classes of data types, sample frequencies, and schema - including event logs, weather, and geo-spatial data. Statistical analyses for outliers, normality, (auto)correlation, stationarity, and missing data are detailed, along with a proposed method for hierarchical clustering to identify correlated groups of features. The missing data was overcome using a combination of knowledge-based and statistical techniques, including several multivariate imputation algorithms. "Markov unfolding" is presented for collapsing the time series into a collection of independent observations, whilst incorporating historical information. The final model was interpreted in two ways: by inspecting the internal $\beta$-parameters, and using the SHAP framework. These two interpretation techniques were combined to produce a list of the 16 most-predictive features, demonstrating that an observational study can greatly narrow down the number of features that need to be considered when designing interventional QS studies.

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