Optimized Signal Quality Assessment for Photoplethysmogram Signals Using Feature Selection.

ABSTRACT:

Objective

With the increasing use of wearable healthcare devices for remote patient monitoring, reliable signal quality assessment (SQA) is required to ensure the high accuracy of interpretation and diagnosis on the recorded data from patients. Photoplethysmographic (PPG) signals non-invasively measured by wearable devices are extensively used to provide information about the cardiovascular system and its associated diseases. In this study, we propose an approach to optimize the quality assessment of the PPG signals.

Methods

We used an ensemble-based feature selection scheme to enhance the prediction performance of the classification model to assess the quality of the PPG signals. Our approach for feature and subset size selection yielded the best-suited feature subset, whic

SUBMITTER: Mohagheghian F 

PROVIDER: S-EPMC9478959 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature