Deep Learning for PPG Heart Rate Estimation: CNN, LSTM & Hybrid Architectures

Deep learning has fundamentally changed the landscape of PPG heart rate estimation, achieving accuracy levels that were unreachable with traditional signal processing just five years ago. Convolutional Neural Networks (CNNs) automatically learn optimal spectral and morphological features from raw PPG data, while Long Short-Term Memory (LSTM) networks capture the temporal dynamics of heart rate evolution. Hybrid CNN-LSTM architectures combine both capabilities to achieve state-of-the-art results on every major benchmark dataset.

This guide provides a comprehensive technical overview of deep learning architectures for PPG-based heart rate estimation, covering input representation, 1D and 2D CNN designs, recurrent architectures, hybrid models, training strategies, and practical deployment considerations. For context on the traditional signal processing methods that deep learning aims to supersede, see our guides on motion artifact removal and Kalman filter-based heart rate tracking.

Why Deep Learning for PPG Heart Rate?

Traditional PPG heart rate estimation follows a sequential pipeline: preprocessing, motion artifact removal, spectral estimation, peak selection, and temporal tracking. Each stage involves design decisions and hand-tuned parameters that are optimized independently. The result is a brittle pipeline where errors in early stages propagate and amplify through subsequent stages.

Deep learning offers an alternative paradigm: end-to-end learning from raw (or minimally preprocessed) PPG and accelerometer signals directly to heart rate output. The network learns to jointly handle noise removal, feature extraction, and temporal tracking in a single optimized system. This joint optimization avoids the suboptimal parameter choices that arise from stage-by-stage design and enables the model to discover signal features that human engineers may not have considered.

The empirical evidence strongly supports this approach. On the IEEE Signal Processing Cup 2015 dataset, which has become the de facto benchmark for PPG heart rate estimation during exercise, deep learning methods occupy 8 of the top 10 positions on published leaderboards. The gap between deep learning and traditional methods widens as motion intensity increases, precisely the regime where hand-designed algorithms struggle most.

However, deep learning is not a universal improvement. Traditional methods maintain advantages in interpretability (understanding why a particular estimate was produced), data efficiency (working with limited training data), computational efficiency (running on low-power processors), and generalization to out-of-distribution scenarios (demographics, sensor types, or activities not represented in training data). The choice between approaches depends on application requirements, available data, and deployment constraints.

1D Convolutional Neural Networks for PPG

One-dimensional CNNs process the raw PPG time series directly, applying learned convolutional filters that detect temporal patterns at multiple scales. The hierarchical feature extraction capability of CNNs naturally maps to the multi-scale structure of PPG signals: short filters capture individual pulse morphology, medium filters capture inter-beat dynamics, and long filters (or deep networks with large receptive fields) capture respiratory and autonomic modulations.

DeepPPG Architecture

Reiss et al. (2019) proposed DeepPPG, one of the first systematic deep learning approaches to PPG heart rate estimation. The architecture processes 8-second windows of 4-channel input (PPG + 3-axis accelerometer) at 64 Hz through a series of 1D convolutional layers:

• Input: 512 samples x 4 channels
• 4 convolutional blocks, each containing: Conv1D (kernel size 5) -> BatchNorm -> ReLU -> MaxPool(2)
• Channel sizes: 64, 128, 256, 256
• 2 fully connected layers: 512, 128
• Output: single heart rate value (regression)

DeepPPG achieved heart rate MAE of 3.42 BPM on the PPG-DaLiA dataset (15 subjects performing activities of daily living including walking, cycling, driving, and working at a desk), outperforming classical methods including TROIKA (MAE: 8.01 BPM) and a spectral peak tracking baseline (MAE: 12.87 BPM) on the same dataset (doi:10.3390/s19143079).

A critical design choice in DeepPPG is the inclusion of accelerometer channels alongside the PPG signal. By providing the raw motion data as input, the network learns to implicitly perform motion artifact removal as part of its feature extraction, eliminating the need for a separate preprocessing stage. Ablation studies showed that removing the accelerometer channels increased MAE from 3.42 to 5.78 BPM, confirming the importance of motion reference information.

Spectrogram-Based 2D CNN

An alternative to processing raw time-domain PPG is to first compute a time-frequency representation (spectrogram) and process it with a 2D CNN. This approach is motivated by the observation that heart rate tracking is fundamentally a time-frequency problem: the goal is to identify and follow a specific frequency component (the cardiac fundamental) across time in the presence of interfering components (motion harmonics).

CorNET (Biswas et al., 2019, IEEE Transactions on Biomedical Circuits and Systems, doi:10.1109/TBCAS.2019.2892297) took a different, hybrid route rather than a spectrogram front end: two convolutional layers followed by two LSTM layers and a dense output layer, operating on a single-channel wrist PPG signal. On the 22-record TROIKA dataset (IEEE SP Cup 2015) it reported an MAE of 1.47 ± 3.37 BPM for heart rate estimation, alongside 96% accuracy for biometric identification on 20 subjects.

Subsequent work, including that of Burrello and colleagues, has explored deploying compact deep networks for PPG heart rate estimation on ultra-low-power microcontrollers, showing that deep learning for this task can fit within the constraints of wearable hardware.

Recurrent Neural Networks: LSTM and GRU

While CNNs excel at extracting spatial features from fixed-length input windows, they do not inherently model the sequential dependency between consecutive heart rate estimates. Recurrent Neural Networks (RNNs), particularly Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures, are designed for exactly this purpose.

LSTM for Heart Rate Tracking

LSTM networks maintain a cell state that can store information over long time spans, controlled by input, forget, and output gates. For PPG heart rate tracking, the LSTM learns to maintain a running estimate of heart rate and update it based on each new PPG observation, functionally equivalent to an adaptive Kalman filter but with learned (rather than hand-designed) dynamics.

The key advantage of the LSTM over the Kalman filter approaches described in our Kalman filter guide is that the LSTM learns its state dynamics from data rather than requiring manual specification of a state-space model. This makes it more adaptable to complex, nonlinear heart rate dynamics and removes the need for noise parameter tuning. However, the LSTM requires substantially more training data and computation.

GRU as a Lightweight Alternative

The GRU architecture simplifies the LSTM by combining the input and forget gates into a single update gate, reducing the parameter count by approximately 25%. For PPG heart rate estimation, where the temporal dynamics are relatively simple (smooth heart rate evolution), the GRU achieves comparable accuracy to the LSTM at lower computational cost.

Hybrid CNN-LSTM Architectures

The most successful deep learning models for PPG heart rate estimation combine the spatial feature extraction of CNNs with the temporal modeling of LSTMs. The CNN processes each PPG window independently to extract feature vectors, and the LSTM processes the sequence of feature vectors to track heart rate over time.

Architecture Design Principles

The typical CNN-LSTM hybrid follows this structure:

1. CNN encoder: Processes each PPG/accelerometer window (5-8 seconds) through convolutional layers to produce a fixed-length feature vector (64-256 dimensions)
2. Sequence modeling: LSTM or GRU processes a sequence of feature vectors (10-30 consecutive windows, covering 20-60 seconds of history) to capture temporal context
3. Output head: Fully connected layer maps the LSTM hidden state to a heart rate estimate

This architecture separates concerns: the CNN handles the "what" (extracting cardiac and motion features from each window), while the LSTM handles the "when" (tracking how heart rate evolves over time and resolving temporal ambiguities).

Shyam et al. (2019, doi:10.1109/EMBC.2019.8856989) proposed PPGnet, an end-to-end deep learning model operating on 8-second PPG segments. Without patient-specific training it achieved a mean absolute error of 3.36 ± 4.1 BPM on the 12 subjects of the IEEE SP Cup 2015 dataset, and the authors explored transfer learning with sparse retraining to adapt the model across PPG devices with different hardware.

Multi-Task Learning

Several authors have demonstrated that training the CNN-LSTM to predict auxiliary outputs alongside heart rate improves overall accuracy through multi-task regularization.