Feasibility of personalized nonparametric analytics for predictive monitoring of heart failure patients using continuous mobile telemetry

Abstract

Nonparametric model-based analytics personalized to the physiology of each patient are investigated for predictive monitoring of exacerbation in heart failure patients at home. Multivariate vital sign data are provided by means of continuous bio-signal acquisition with a mobile phone-based wearable sensor system worn by patients for several hours a day in the home ambulatory environment. Perturbation analysis demonstrates that individual patient physiological behavior is indeed effectively learned by the analytics, with high sensitivity to changes in physiological dynamics. Comparison of the analytics results with absence of unplanned medical events and self-reported wellness during regular patient follow-up demonstrate a very low false alert burden, suggesting this approach is efficient for remote clinical surveillance.