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In Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine

STUDY OBJECTIVES : Nocturnal blood pressure (BP) profile shows characteristic abnormalities in obstructive sleep apnea (OSA), namely acute post-apnea BP surges and non-dipping BP. These abnormal BP profiles provide prognostic clues indicating increased cardiovascular disease (CVD) risk. We developed a deep neural network model to perform computerized analysis of polysomnography data and predict nocturnal BP profile.

METHODS : We analyzed concurrently performed polysomnography and non-invasive beat-to-beat BP measurement with a deep neural network model to predict nocturnal BP profiles from polysomnography data in thirteen patients with severe obstructive sleep apnea.

RESULTS : A good correlation was noted between measured and predicted post-apnea systolic and diastolic BP (Pearson's r ≥ 0.75). Moreover, Bland Altman analyses showed good agreement between the two values. Continuous systolic and diastolic BP prediction by the DNN model was also well-correlated with measured BP values (Pearson's r ≥ 0.83).

CONCLUSIONS : We developed a deep neural network model to predict nocturnal BP profile from clinical polysomnography signals and provide a potential prognostic tool in OSA. Validation of the model in larger samples and examination of its utility in predicting CVD risk in future studies is warranted.

Prasad Bharati, Agarwal Chirag, Schonfeld Elan, Schonfeld Dan, Mokhlesi Babak


blood pressure, deep neural network, obstructive sleep apnea