Equanimity - dynamic physiology

Abstract

Disclosed is a heart rate monitoring system containing a machine learning platform containing self-learning and/or personalizable algorithms capable of detecting and filtering out artifacts in signals indicative of an individual's heart rate, such signals generated from one or more electrocardiogram sensors. The self-learning and/or personalizable algorithms are updated periodically. Accordingly, the machine learning platform improves its accuracy in detecting and/or rejecting/filtering out artifacts and/or becomes more personalized as further signals indicative of an individual's heart rate are processed. The machine learning platform performs detection and filtering out of artifacts via dimension reduction algorithms. The system further contains a first algorithm for real time measurement of heart rate variability (HRV). Also disclosed is a heart rate monitoring method that implements a heart rate monitoring system disclosed herein. The heart rate monitoring system and method can partition physiological components of HRV from other non-physiological components, particularly physical activity and/or breathing.

Dependencies are inferred from the claim text.