Hidden Markov model-based heartbeat detector using electrocardiogram and arterial pressure signals.

ABSTRACT: The automatic detection of a heartbeat is commonly performed by detecting the QRS complex in the electrocardiogram (ECG), however, various noise sources and missing data can jeopardize the reliability of the ECG. Therefore, there is a growing interest in combining the information from many physiological signals to accurately detect heartbeats. To this end, hidden Markov models (HMMs) are used in this work to jointly exploit the information from ECG, arterial blood pressure (ABP) and pulmonary arterial pressure (PAP) signals in order to conceive a heartbeat detector. After preprocessing the physiological signals, a sliding window is used to extract an observation sequence to be passed through two HMMs (previously trained on a training dataset) in order to obtain the log-likelihoods of obser

SUBMITTER: Altuve M 

PROVIDER: S-EPMC8316507 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature