Performance analysis of remote photoplethysmography deep filtering using long short-term memory neural network.

ABSTRACT:

Background

Remote photoplethysmography (rPPG) is a technique developed to estimate heart rate using standard video cameras and ambient light. Due to the multiple sources of noise that deteriorate the quality of the signal, conventional filters such as the bandpass and wavelet-based filters are commonly used. However, after using conventional filters, some alterations remain, but interestingly an experienced eye can easily identify them.

Results

We studied a long short-term memory (LSTM) network in the rPPG filtering task to identify these alterations using many-to-one and many-to-many approaches. We used three public databases in intra-dataset and cross-dataset scenarios, along with different protocols to analyze the performance of the method. We demonstrate how the network

SUBMITTER: Botina-Monsalve D 

PROVIDER: S-EPMC9487135 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature