Detecting Chronic Diseases from Sleep-Wake Behaviour and Clinical Features

Abstract

Many chronic diseases show evidence of correlations with sleep-wake behaviour, and there is an increasing interest in making use of such correlations for early warning systems. This research presents an approach towards early chronic disease detection by mining sleep-wake measurements using deep learning. Specifically, a Long-Short-Term-Memory network is applied on actigraph data enriched with clinical history of patients. Experiments and analysis are performed targeting detection at an early and advanced disease stage based on different clinical data features. The results show for disease detection an averaged accuracy of 0.62, 0.73, 0.81, 0.77 for hypertension, diabetes, sleep apnea and chronic kidney disease, respectively. Early detection performs with an averaged accuracy of 0.49 for sleep apnea and 0.56 for diabetes. Nevertheless, compared to existing work, our approach shows an improvement in performance and demonstrates that predicting chronic diseases from sleep-wake behavior is feasible, though further investigation will be needed for early prediction.

Computational Sleep Behavior Analysis: A Survey

“…These parameters have proven useful in investigating abnormal sleep behavior. Consequently, abnormal behavior can classify sleep disorders and is related to certain chronic diseases [25].…”

“…Sleep apnea is diagnosed using AHI, which represents the apnea and hypopnea events per hour [28]; see table 3. An AHI of less than 5 is interpreted as healthy, whereas an AHI between 5 and 15 is classified as mild obstructive sleep apnea, an AHI between 15 to 30 is classified as moderate sleep apnea, and an AHI higher than 30 is classified as severe sleep apnea [25]. For insomnia, the Insomnia Severity Index and Bergen Insomnia Scale can be used for the assessment [2].…”

“…For insomnia, the Insomnia Severity Index and Bergen Insomnia Scale can be used for the assessment [2]. Medical history and physical exams can be used in combination to quickly and accurately make diagnoses, such as explored for sleep apnea diagnosis in [25]. Sleep disorders are generally related to poor sleep quality.…”

“…(2) clinical history, including symptoms, and risk factors; or (3) sensor-based data, e.g., with blood pressure measurements [25]. Current research investigates early disease detection by marker-based clinical analysis or sensor-based behavioral analysis [25].…”

“…(2) clinical history, including symptoms, and risk factors; or (3) sensor-based data, e.g., with blood pressure measurements [25]. Current research investigates early disease detection by marker-based clinical analysis or sensor-based behavioral analysis [25]. Sensor-based analysis uses data mining on sensory data, such as actigraphy, to investigate Alzheimer's disease [32], Parkinson's disease [33], diabetes, hypertension, and chronic kidney disease (CKD) [25], [27].…”