# TAU: Modeling Temporal Consistency Through Temporal Attentive U-Net for PPG Peak Detection

arXiv: https://arxiv.org/abs/2503.10733 · HTML: https://mlsanity.com/paper/2503.10733/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 81 references that are in the map.
- First posted on arXiv 13 Mar 2025.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [Deeply-Supervised Nets](https://mlsanity.com/paper/1409.5185/) (2014)
- [Time2Vec: Learning a Vector Representation of Time](https://mlsanity.com/paper/1907.05321/) (2019)
- [TROIKA: A General Framework for Heart Rate Monitoring Using Wrist-Type Photoplethysmographic Signals During Intensive Physical Exercise](https://mlsanity.com/paper/1409.5181/) (2014)
- [CardioGAN: Attentive Generative Adversarial Network with Dual Discriminators for Synthesis of ECG from PPG](https://mlsanity.com/paper/2010.00104/) (2020)
- [DA-Transformer: Distance-aware Transformer](https://mlsanity.com/paper/2010.06925/) (2020)
- [Photoplethysmography-Based Heart Rate Monitoring in Physical Activities via Joint Sparse Spectrum Reconstruction](https://mlsanity.com/paper/1503.00688/) (2015)
- [PPGnet: Deep Network for Device Independent Heart Rate Estimation from Photoplethysmogram](https://mlsanity.com/paper/1903.08912/) (2019)
- [An Accurate Non-accelerometer-based PPG Motion Artifact Removal Technique using CycleGAN](https://mlsanity.com/paper/2106.11512/) (2021)
- [Distance transform regression for spatially-aware deep semantic segmentation](https://mlsanity.com/paper/1909.01671/) (2019)
- [A Deep Learning & Fast Wavelet Transform-based Hybrid Approach for Denoising of PPG Signals](https://mlsanity.com/paper/2301.06549/) (2023)
- [Embedding time expressions for deep temporal ordering models](https://mlsanity.com/paper/1906.08287/) (2019)
- [Self-supervised Learning for Label Sparsity in Computational Drug Repositioning](https://mlsanity.com/paper/2206.00262/) (2022)
- [Is the PPG signal chaotic?](https://mlsanity.com/paper/2002.01054/) (2020)

## Abstract (arXiv)

Photoplethysmography (PPG) sensors have been widely used in consumer wearable devices to monitor heart rates (HR) and heart rate variability (HRV). Despite the prevalence, PPG signals can be contaminated by motion artifacts induced from daily activities. Existing approaches mainly use the amplitude information to perform PPG peak detection. However, these approaches cannot accurately identify peaks, since motion artifacts may bring random and significant amplitude variations. To improve the performance of PPG peak detection, the time information can be used. Specifically, heart rates exhibit temporal consistency that consecutive heartbeat intervals in a normal person can have limited variations. To leverage the temporal consistency, we propose the Temporal Attentive U-Net, i.e., TAU, to accurately detect peaks from PPG signals. In TAU, we design a time module that encodes temporal consistency in temporal embeddings. We integrate the amplitude information with temporal embeddings using the attention mechanism to estimate peak labels. Our experimental results show that TAU outperforms eleven baselines on heart rate estimation by more than 22.4%. Our TAU model achieves the best performance across various Signal-to-Noise Ratio (SNR) levels. Moreover, we achieve Pearson correlation coefficients higher than 0.9 (p < 0.01) on estimating HRV features from low-noise-level PPG signals.