In-Home Sleep Apnea Severity Classification using Contact-free Load Cells and an AdaBoosted Decision Tree Algorithm

Abstract

We present a method for automated diagnosis and classification of severity of sleep apnea using an array of non-contact pressure-sensitive sensors placed underneath a mattress as an alternative to conventional obtrusive sensors. Our algorithm comprises two stages: i) A decision tree classifier that identifies patients with sleep apnea, and ii) a subsequent linear regression model that estimates the Apnea-Hypopnea Index (AHI), which is used to determine the severity of sleep disordered breathing. We tested our algorithm on a cohort of 14 patients who underwent overnight home sleep apnea test. The machine learning algorithm was trained and performance was evaluated using leave-one-patient-out cross-validation. The accuracy of the proposed approach in detecting sleep apnea is 86.96%, with sensitivity and specificity of 81.82% and 91.67%, respectively. Moreover, classification of severity of the sleep disorder was correctly assigned in 11 out of 14 cases, and the mean absolute error in the AHI estimation was calculated to be 3.83 events/hr.

Fig. 1

Data acquisition system’s installation schema.

Fig. 2

Two-minute signal segments from load cell array, airflow, and abdomen belt sensors, with annotated respiratory events.

Fig. 3

Flow diagram of the classification system. Dashed links are activated only if sleep apnea is detected.

Fig. 4

Distribution of features extracted from relevant frequency sub-bands grouped by disease severity.

Fig. 5

Structure of the dominant decision tree in the classification ensemble.