# PPGnet: Deep Network for Device Independent Heart Rate Estimation from Photoplethysmogram

arXiv: https://arxiv.org/abs/1903.08912 · HTML: https://mlsanity.com/paper/1903.08912/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Mar 2019.

## Built on by (most cited first, 11 of 11)

- [WildPPG: A Real-World PPG Dataset of Long Continuous Recordings](https://mlsanity.com/paper/2412.17540/) (2024)
- [Embedding Temporal Convolutional Networks for Energy-Efficient PPG-Based Heart Rate Monitoring](https://mlsanity.com/paper/2203.04396/) (2022)
- [RRWaveNet: A Compact End-to-End Multi-Scale Residual CNN for Robust PPG Respiratory Rate Estimation](https://mlsanity.com/paper/2208.08672/) (2022)
- [BeliefPPG: Uncertainty-aware Heart Rate Estimation from PPG signals via Belief Propagation](https://mlsanity.com/paper/2306.07730/) (2023)
- [Efficient Deep Learning-based Estimation of the Vital Signs on Smartphones](https://mlsanity.com/paper/2204.08989/) (2022)
- [A Self-Supervised Algorithm for Denoising Photoplethysmography Signals for Heart Rate Estimation from Wearables](https://mlsanity.com/paper/2307.05339/) (2023)
- [SiamQuality: A ConvNet-Based Foundation Model for Imperfect Physiological Signals](https://mlsanity.com/paper/2404.17667/) (2024)
- [Model-driven Heart Rate Estimation and Heart Murmur Detection based on Phonocardiogram](https://mlsanity.com/paper/2407.18424/) (2024)
- [Temporal Cardiovascular Dynamics for Improved PPG-Based Heart Rate Estimation](https://mlsanity.com/paper/2510.27297/) (2025)
- [Energy-efficient Wearable-to-Mobile Offload of ML Inference for PPG-based Heart-Rate Estimation](https://mlsanity.com/paper/2306.06129/) (2023)
- [TAU: Modeling Temporal Consistency Through Temporal Attentive U-Net for PPG Peak Detection](https://mlsanity.com/paper/2503.10733/) (2025)

## Abstract (arXiv)

Photoplethysmogram (PPG) is increasingly used to provide monitoring of the cardiovascular system under ambulatory conditions. Wearable devices like smartwatches use PPG to allow long term unobtrusive monitoring of heart rate in free living conditions. PPG based heart rate measurement is unfortunately highly susceptible to motion artifacts, particularly when measured from the wrist. Traditional machine learning and deep learning approaches rely on tri-axial accelerometer data along with PPG to perform heart rate estimation. The conventional learning based approaches have not addressed the need for device-specific modeling due to differences in hardware design among PPG devices. In this paper, we propose a novel end to end deep learning model to perform heart rate estimation using 8 second length input PPG signal. We evaluate the proposed model on the IEEE SPC 2015 dataset, achieving a mean absolute error of 3.36+-4.1BPM for HR estimation on 12 subjects without requiring patient specific training. We also studied the feasibility of applying transfer learning along with sparse retraining from a comprehensive in house PPG dataset for heart rate estimation across PPG devices with different hardware design.