Papers › Deep PPG: Large-Scale Heart Rate Estimation with Convolutional Neural Networks
Deep PPG: Large-Scale Heart Rate Estimation with Convolutional Neural Networks
Attila Reiss, Ina Indlekofer, Philip Schmidt, Kristof Van Laerhoven
Photoplethysmography (PPG)-based continuous heart rate monitoring is essential in a number of domains, e.g., for healthcare or fitness applications. Recently, methods based on time-frequency spectra emerged to address the challenges of motion artefact compensation. However, existing approaches are highly parametrised and optimised for specific scenarios of small, public datasets. We address this fragmentation by contributing research into the robustness and generalisation capabilities of PPG-based heart rate estimation approaches. First, we introduce a novel large-scale dataset (called PPG-DaLiA), including a wide range of activities performed under close to real-life conditions. Second, we extend a state-of-the-art algorithm, significantly improving its performance on several datasets. Third, we introduce deep learning to this domain, and investigate various convolutional neural network architectures. Our end-to-end learning approach takes the time-frequency spectra of synchronised PPG- and accelerometer-signals as input, and provides the estimated heart rate as output. Finally, we compare the novel deep learning approach to classical methods, performing evaluation on four public datasets. We show that on large datasets the deep learning model significantly outperforms other methods: The mean absolute error could be reduced by 31% on the new dataset PPG-DaLiA, and by 21% on the dataset WESAD.
Code
No code repository is listed for this paper in the archive or in Syntology's graph.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Heart rate estimation | PPG-DaLiA | CNN ensemble | MAE [bpm, session-wise] | 7.65 ± 4.2 | #2 of 6 | Archive leaderboard | report |
| Heart rate estimation | PPG-DaLiA | CNN Average | MAE [bpm, session-wise] | 8.82 ± 3.8 | #3 of 6 | Archive leaderboard | report |
| Heart rate estimation | WESAD | CNN ensemble | MAE [bpm, session-wise] | 7.47 ± 3.3 | #1 of 5 | Archive leaderboard | report |
| Heart rate estimation | WESAD | CNN average | MAE [bpm, session-wise] | 8.42 ± 3 | #2 of 5 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections