A Long Short Term Memory Deep Neural Network for remote heart rate estimation achieved an error of 3.26 bpm, outperforming the green component method (6.49 bpm) and comparable to ICA and POS methods.
Does an LSTM Deep Neural Network improve the accuracy of remote heart rate estimation via videoplethysmography compared to existing algorithms?
An LSTM-based deep learning method provides accurate remote heart rate estimation comparable to state-of-the-art algorithms, with the added advantage of functioning beyond the visible light spectrum.
Remote monitoring of elderly people or patients in home isolation is an essential part of modern telemedicine. Videoplethysmography (VPG) is a method of noncontact assessment of heart rate and other cardiovascular parameters. Many algorithms have been developed to extract and improve the quality of the VPG signal. The main objective of this study is to design a method that replaces existing multistage algorithms and provides continuous monitoring of the user’s pulse. The article presents a method of heart rate measurement based on the Long Short Term Memory (LSTM) Deep Neural Network. The proposed method outperforms the algorithm based on the analysis of the green component (G) and provides comparable results to the state-of-the-art methods such as Independent Component Analysis (ICA) and Plane Orthogonal to the Skin (POS). The best result for G was 6.49 bpm (beats per minute), ICA = 3.02 bpm, POS = 2.61 bpm, and for the proposed method was 3.26 bpm. While maintaining the accuracy comparable to ICA and POS algorithms, the LSTM network works well also beyond the visible spectrum, e.g., with infrared lighting when the color signal is not available and is easily adaptable to telemedicine applications.
Jaromir Przybyło (Thu,) conducted a other in Remote heart rate estimation. Long Short Term Memory (LSTM) Deep Neural Network vs. Green component (G), Independent Component Analysis (ICA), and Plane Orthogonal to the Skin (POS) was evaluated on Heart rate measurement error (bpm). A Long Short Term Memory Deep Neural Network for remote heart rate estimation achieved an error of 3.26 bpm, outperforming the green component method (6.49 bpm) and comparable to ICA and POS methods.