PPG Respiratory Rate Estimation: Extracting Breathing Rate from Photoplethysmography
**Respiratory rate is the vital sign most sensitive to clinical deterioration, yet it is the least frequently and least accurately recorded in routine...
- HRV, Stress & Recovery
Respiratory rate is the vital sign most sensitive to clinical deterioration, yet it is the least frequently and least accurately recorded in routine clinical practice. In a nested case-control study of 88 ward cardiac arrests matched to 352 controls, maximum respiratory rate in the preceding 48 hours was the strongest single vital-sign predictor of cardiac arrest (AUC 0.72), behind only the composite Modified Early Warning Score (AUC 0.77) (Churpek et al., 2012; doi:10.1378/chest.11-1301). The ability to extract continuous respiratory rate from PPG signals -- sensors already present in pulse oximeters on virtually every hospitalized patient and in billions of consumer wearables -- represents one of the most immediately clinically impactful applications of PPG technology.
This article provides a technical review of the three primary mechanisms by which respiration modulates the PPG waveform, the signal processing algorithms used to extract respiratory rate, fusion strategies for combining multiple respiratory estimates, and the accuracy benchmarks that define the current state of the art.
Physiological Mechanisms of Respiratory Modulation in PPG
Respiration influences the PPG waveform through three distinct physiological pathways, each producing a characteristic modulation pattern. Understanding these mechanisms is essential for designing robust extraction algorithms.
Respiratory-Induced Frequency Variation (RIFV)
Respiratory sinus arrhythmia (RSA) is the physiological variation in heart rate synchronized with breathing: heart rate increases during inspiration and decreases during expiration. This is mediated primarily by vagal modulation of the sinoatrial node and is modulated by lung stretch receptors, central respiratory drive, and baroreflex interactions.
RSA produces a frequency modulation of the PPG inter-beat interval (IBI) series. The instantaneous heart rate oscillates at the respiratory frequency, and this oscillation can be extracted by analyzing the IBI time series in the frequency domain. The respiratory frequency appears as a spectral peak in the 0.15-0.4 Hz band (corresponding to 9-24 breaths per minute) of the IBI power spectrum, which is the high-frequency (HF) component of heart rate variability.
RIFV is the most robust respiratory modulation in young, healthy individuals at rest, where RSA amplitude can reach 10-20 BPM peak-to-peak. However, RSA amplitude decreases markedly with age, at high heart rates, in patients with autonomic neuropathy (common in diabetes), and with certain medications (atropine, beta-blockers). In elderly or critically ill patients, RIFV may be too weak to detect reliably.
Respiratory-Induced Amplitude Variation (RIAV)
Changes in intrathoracic pressure during the respiratory cycle alter venous return to the heart and consequently stroke volume. During spontaneous (negative-pressure) inspiration, intrathoracic pressure decreases, increasing venous return to the right heart but initially decreasing left ventricular filling due to pulmonary vascular pooling. This produces beat-to-beat variation in stroke volume and arterial pulse pressure, which manifests as amplitude modulation of the PPG waveform.
The RIAV envelope is extracted by measuring the peak-to-peak amplitude (or systolic peak amplitude) of each PPG pulse and analyzing the resulting amplitude time series. The respiratory frequency appears as an oscillation in this envelope. RIAV therefore provides respiratory information that complements RIFV and remains useful when RSA is attenuated.
RIAV is more prominent during mechanical ventilation (positive-pressure breathing), where the pressure-volume relationships are reversed and the stroke volume variation is typically larger. This makes RIAV particularly useful in the ICU setting. The pulse pressure variation (PPV) derived from RIAV is also a validated clinical indicator of fluid responsiveness in mechanically ventilated patients.
Respiratory-Induced Intensity Variation (RIIV)
Respiratory chest wall movement and changes in venous blood volume in the measurement site produce slow oscillations in the baseline (DC component) of the PPG signal. During inspiration, decreased intrathoracic pressure reduces venous pressure in the upper body, decreasing venous blood volume at the finger or earlobe and reducing the baseline PPG absorption. This creates a baseline intensity modulation at the respiratory frequency.
RIIV is extracted by low-pass filtering the raw PPG signal (below approximately 0.5 Hz) or by analyzing the DC component (baseline) of the PPG waveform. Nilsson et al. (2005) recorded reflection-mode forearm PPG in 12 patients and detected breaths from the respiratory-synchronous PPG component with sensitivity above 86% and specificity above 96%, unaffected by anaesthesia or positive-pressure ventilation (doi:10.1111/j.1399-6576.2005.00721.x).
RIIV is the simplest modulation to extract computationally, requiring minimal preprocessing. However, it is highly susceptible to motion artifacts, sensor repositioning, and baseline drift, which produce low-frequency variations indistinguishable from respiratory modulation.
Extraction Algorithms
Time-Domain Peak Counting
The simplest respiratory rate estimation approach identifies peaks in the respiratory modulation signal (RIAV envelope, RIIV baseline, or RIFV IBI series) and computes the breathing rate from the inter-peak intervals.
The algorithm involves:
- Bandpass filtering the modulation signal to the respiratory frequency band (0.1-0.6 Hz, corresponding to 6-36 breaths/min).
- Detecting peaks (or zero crossings) in the filtered signal.
- Computing the median inter-peak interval over a window (typically 30-60 seconds).
- Converting the interval to respiratory rate in breaths per minute.
Peak counting is computationally trivial and works well when the respiratory signal is strong and regular. It fails with irregular breathing, low signal-to-noise ratio, and when harmonic or subharmonic peaks create ambiguity.
Frequency-Domain Spectral Analysis
Spectral methods estimate respiratory rate by identifying the dominant spectral peak in the respiratory frequency band. The approach involves:
- Computing the power spectral density (PSD) of the modulation signal using FFT, Welch's method, or autoregressive (AR) modeling.
- Identifying the spectral peak with the highest power within the respiratory band (0.1-0.6 Hz).
- Reporting the peak frequency as the respiratory rate estimate.
AR spectral estimation (typically order 8-12) provides better frequency resolution than FFT for short analysis windows, which is important for respiratory rate estimation where the respiratory signal may contain only 3-6 cycles within a 30-second window. Chon et al. (2009) compared a time-frequency method (variable-frequency complex demodulation) with CWT and AR approaches in 15 healthy subjects breathing at 12-36 breaths/min, and found the time-frequency method most accurate and computationally efficient (doi:10.1109/TBME.2009.2019766).
A key challenge in spectral methods is distinguishing the true respiratory peak from cardiac harmonics. The second harmonic of the respiratory frequency can overlap with the fundamental cardiac frequency, and the subharmonic of the cardiac frequency can fall within the respiratory band. Spectral tracking algorithms that enforce temporal continuity of the respiratory frequency estimate can mitigate these ambiguities.
Advanced Signal Processing Methods
Empirical Mode Decomposition (EMD): EMD decomposes the PPG signal into intrinsic mode functions (IMFs) without assuming any particular basis. The IMF corresponding to the respiratory frequency band contains the respiratory modulation. EMD-based respiratory extraction is attractive because it adapts to non-stationary signals.
Continuous Wavelet Transform (CWT): The CWT provides time-frequency representation of the modulation signal, enabling tracking of respiratory frequency changes over time. The scalogram (magnitude of the CWT) shows the respiratory frequency as a ridge that can be tracked using ridge extraction algorithms. Addison et al. (2015) used a continuous wavelet transform-based algorithm on finger PPG from 63 general care floor patients and found a mean difference of -0.48 breaths/min (SD 1.77) against an end-tidal CO2 reference (doi:10.1007/s10877-014-9575-5).
Singular Spectrum Analysis (SSA): SSA decomposes the signal into oscillatory components based on eigendecomposition of the trajectory matrix. It can separate respiratory and cardiac components even when their frequencies are close, and it does not require predefined basis functions or frequency bands.
Smart Fusion of Multiple Respiratory Estimates
Since RIFV, RIAV, and RIIV capture respiratory information through different physiological mechanisms, they provide complementary estimates with different strengths and failure modes. Fusing multiple estimates improves robustness and accuracy.
Quality-Weighted Fusion
Karlen et al. (2013) proposed a smart fusion framework that computes respiratory rate estimates from all three modulations independently, assesses the quality of each estimate, and combines them using quality-weighted averaging (doi:10.1109/TBME.2013.2246160). Quality is assessed by:
- Signal quality index (SQI): Measures the regularity and consistency of the modulation signal. Low SQI indicates noise contamination.
- Spectral peak prominence: A sharp, isolated spectral peak indicates a reliable estimate; a broad or ambiguous peak indicates uncertainty.
- Cross-modulation agreement: When two or more modulations agree on the respiratory frequency, confidence increases.
In a study of 59 subjects, the smart fusion approach achieved MAE of 0.9 breaths/min compared to reference capnography, outperforming any single modulation source (RIFV alone: 1.4 breaths/min; RIAV alone: 1.6 breaths/min; RIIV alone: 1.8 breaths/min).
Bayesian Fusion
Probabilistic fusion frameworks maintain probability distributions over respiratory rate from each modulation source and combine them, so that a source producing an unreliable, high-variance estimate automatically has less influence. A temporal prior can also penalize large changes between consecutive windows, reflecting the fact that breathing rate normally changes slowly.
Pimentel et al. (2017) took a related robustness-focused approach, using multiple autoregressive models of different orders on the three respiratory-induced variations (doi:10.1109/TBME.2016.2613124). Across 95 eight-minute recordings from two datasets, they reported median mean absolute errors of 1.5 and 4.0 breaths/min (32-second windows) while still producing estimates for over 90% of the input data.
Performance Benchmarks and Datasets
The CapnoBase Benchmark
The CapnoBase dataset (used by Karlen et al., 2013) has become the standard benchmark for PPG respiratory rate estimation. It contains 42 recordings of 8 minutes each from pediatric and adult patients during elective surgery, with simultaneous finger PPG and capnography (reference respiratory rate). The dataset is publicly available, enabling direct comparison across methods.
Reported results are not always directly comparable, because studies use different datasets, window lengths, and error metrics:
Scroll horizontally if needed
| Study | Data | Reported error |
|---|---|---|
| Karlen et al., 2013 (Smart Fusion) | 29 children + 13 adults | Mean RMSE 3.0 breaths/min (vs 3.9-6.2 for single modulations) |
| Pimentel et al., 2017 (multiple AR models) | 95 recordings, two datasets | Median MAE 1.5 and 4.0 breaths/min; >90% of data kept |
| Bian et al., 2020 (ResNet deep learning) | Two public datasets (n = 95) | MAE 2.5 +/- 0.6 breaths/min |
Some methods abstain from low-confidence windows, so coverage (the percentage of windows that produce an estimate) should be reported alongside error.
The BIDMC Dataset
The BIDMC (Beth Israel Deaconess Medical Center) dataset provides 53 recordings from critically ill adult patients with reference respiratory rate from impedance pneumography. This dataset is more challenging than CapnoBase because ICU patients often have irregular breathing, cardiac arrhythmias, and lower signal quality.
Pimentel et al. (2017) found a clear performance gap between their two evaluation datasets (median MAE 1.5 vs 4.0 breaths/min), illustrating how much results depend on the setting.
Deep Learning Approaches
Recent work has applied deep learning to PPG respiratory rate estimation, bypassing hand-crafted feature extraction.
End-to-End CNN Models
Ravichandran et al. (2019) proposed RespNet, an end-to-end network that extracts the respiration waveform itself (rather than only the rate) from PPG, without explicit RIFV/RIAV/RIIV decomposition (doi:10.1109/EMBC.2019.8856301). On two independent datasets it reached mean squared errors of 0.262 and 0.145 and cross-correlations of 0.933 and 0.931 with the reference respiration signal, outperforming two conventional signal-processing approaches.
Residual Networks with Synthetic Training Data
Bian et al. (2020) trained a ResNet-based model end to end on time-series PPG, adding a synthetic PPG dataset to overcome limited training data, which improved performance by 34% (doi:10.1109/EMBC44109.2020.9176231). With 5-fold cross-validation on two public datasets (n = 95), the final MAE was 2.5 +/- 0.6 breaths/min, comparable to a classical method implemented for comparison.
Clinical Applications
Hospital Patient Monitoring
The most immediate clinical application is continuous respiratory rate monitoring from existing pulse oximeters. The Nellcor Respiration Rate (RRp) algorithm by Medtronic is FDA-cleared and deployed in hospital monitors, extracting respiratory rate from the pulse oximeter PPG signal without requiring additional sensors. In two validation studies (26 healthy volunteers and 53 hospitalized low-acuity patients; 23,243 paired observations), the Nellcor software's respiratory rate correlated with clinician-reviewed capnography at R = 0.92 (Bergese et al., 2017; doi:10.1213/ANE.0000000000001852).
This capability is particularly valuable for post-surgical patients on general wards, where continuous capnography is impractical but early detection of respiratory depression (from opioid analgesia) can prevent critical events.
Wearable Sleep Monitoring
Consumer wearables including Apple Watch, Fitbit, and Garmin devices now report respiratory rate during sleep. The constrained environment (minimal motion, regular breathing) makes sleep an ideal use case for PPG-based respiratory rate estimation. Longitudinal tracking of sleeping respiratory rate can identify trends associated with respiratory infections, heart failure decompensation, and sleep-disordered breathing.
For how respiratory rate estimation integrates with broader sleep staging algorithms, see our companion article. Our algorithms reference and signal processing guide provide additional implementation details for PPG-based respiratory analysis, and our article on PPG motion artifact removal covers the essential preprocessing steps for extracting clean respiratory modulations.
References
- Churpek, M. M., et al. (2012). Predicting cardiac arrest on the wards: a nested case-control study. Chest, 141(5), 1170-1176. https://doi.org/10.1378/chest.11-1301
- Nilsson, L., Johansson, A., & Kalman, S. (2005). Respiration can be monitored by photoplethysmography with high sensitivity and specificity regardless of anaesthesia and ventilatory mode. Acta Anaesthesiologica Scandinavica, 49(8), 1157-1162. https://doi.org/10.1111/j.1399-6576.2005.00721.x
- Chon, K. H., Dash, S., & Ju, K. (2009). Estimation of respiratory rate from photoplethysmogram data using time-frequency spectral estimation. IEEE Transactions on Biomedical Engineering, 56(8), 2054-2063. https://doi.org/10.1109/TBME.2009.2019766
- Addison, P. S., et al. (2015). Pulse oximetry-derived respiratory rate in general care floor patients. Journal of Clinical Monitoring and Computing, 29(1), 113-120. https://doi.org/10.1007/s10877-014-9575-5
- Karlen, W., Raman, S., Ansermino, J. M., & Dumont, G. A. (2013). Multiparameter respiratory rate estimation from the photoplethysmogram. IEEE Transactions on Biomedical Engineering, 60(7), 1946-1953. https://doi.org/10.1109/TBME.2013.2246160
- Pimentel, M. A. F., et al. (2017). Toward a robust estimation of respiratory rate from pulse oximeters. IEEE Transactions on Biomedical Engineering, 64(8), 1914-1923. https://doi.org/10.1109/TBME.2016.2613124
- Ravichandran, V., et al. (2019). RespNet: A deep learning model for extraction of respiration from photoplethysmogram. 2019 41st Annual International Conference of the IEEE EMBC, 5556-5559. https://doi.org/10.1109/EMBC.2019.8856301
- Bian, D., Mehta, P., & Selvaraj, N. (2020). Respiratory rate estimation using PPG: A deep learning approach. 2020 42nd Annual International Conference of the IEEE EMBC, 5948-5952. https://doi.org/10.1109/EMBC44109.2020.9176231
- Bergese, S. D., et al. (2017). Multicenter study validating accuracy of a continuous respiratory rate measurement derived from pulse oximetry: a comparison with capnography. Anesthesia & Analgesia, 124(4), 1153-1159. https://doi.org/10.1213/ANE.0000000000001852
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