A Technical Review of ECG and PPG Waveform Analysis for Respiratory Rate Estimation

Introduction: Why Respiratory Rate Matters in Physiological Monitoring 

Respiratory rate (RR) is a vital indicator of physiological stability—often the first parameter to shift in response to metabolic stress, infection, or hypoxia. Yet, continuous and unobtrusive respiratory monitoring remains challenging outside of controlled environments. 

With the rise of wearable and optical sensing technologies, electrocardiography (ECG) and photoplethysmography (PPG) have emerged as promising modalities for estimating respiratory rate. Single-lead ECGs in smartwatches, patches, and chest straps record the heart’s electrical activity. PPG sensors, already ubiquitous in pulse oximetry systems, capture subtle blood volume changes in peripheral tissue. Embedded within those signals are respiratory-driven modulations that can be analyzed to estimate breathing patterns—without adding hardware complexity. 

Respiratory Modulation

A typical PPG waveform reflects pulsatile changes in arterial blood volume corresponding to each cardiac cycle and breathing introduces low-frequency variations in the wave’s height and shape. Similar cues appear in ECG: thoracic motion nudges the baseline and natural respiratory sinus arrhythmia slightly alters beat-to-beat R-R intervals and changes in thoracic impedance. These variations in the signals enable wearable devices to estimate respiratory rate from both PPG and ECG and can be summarized in three modulations: 

• Amplitude Modulation (AM): Changes in intrathoracic pressure alter venous return and stroke volume, producing periodic variations in pulse amplitude. 

• Baseline Wander (BW): Respiratory movements cause slow baseline shifts in the ECG or PPG signal due to motion and pressure fluctuations at the sensor-skin interface. 

• Frequency Modulation (FM): Breathing influences heart rate via respiratory sinus arrhythmia (RSA), introducing periodicity in the inter-beat intervals. 

Each of these components carries information about respiratory rhythm.  Algorithms can estimate respiratory rate using one or more of these modulations in the signal. Isolating respiratory modulation from cardiac and motion artifacts requires careful signal processing. 

Analytical Approach to Respiratory Rate Extraction 

1) Extract Respiratory Signal - derive respiration fluctuations in ECG/PPG waveforms 

• Tracking fluctuations using peak-to-peak amplitude changes 

• Applying low-pass filters to extract baseline wander 

• Measuring respiration-induced variations in pulse interval series (R-R intervals) 

2) Combine Respiratory Signals – depending on technology, combining multiple signals strengthens true respiratory content, suppresses artifacts, and improves robustness during motion and exercise. 

3) Estimate Respiratory Rate - compute breathing rate from each signal. 

• Time-Domain Analysis: Detects individual breaths in the waveform and converts the time between them into breaths per minute.  

• Frequency-Domain Analysis: Finds the strongest breathing rhythm after transforming the signal and turns that peak into a rate.  

4) Merge Values – merge multiple respiratory rate estimations into a single value. Algorithms can use simple statistics—mean, median, or mode with outlier rejection. More advanced options weight estimates by their variance or confidence scores. 

5) Quality Assessment – optional analysis scoring the signal quality to reject imprecise results and low-quality signals 

Challenges and Future Directions 

Despite its promise, ECG and PPG-derived respiratory rate estimation faces several challenges: 

• Motion artifacts remain the most significant source of error in ambulatory environments 

• Peripheral perfusion variability can degrade waveform fidelity 

• Population diversity—skin tone, vasculature, and comorbidities —must be carefully represented in algorithm training and testing 

Validation and Regulatory Considerations 

Developers aiming to integrate respiratory rate estimation into medical devices must demonstrate accuracy, repeatability, and robustness under varied physiological and demographic conditions. 

Key validation practices include: 

• Conducting studies in controlled environments with capnography as ground truth 

• Recruiting participants across a range of age, skin tones (using the Monk Skin Tone scale), and BMI to ensure generalizable performance 

• Recruit participants whose condition may challenge the algorithm, like Chronic obstructive pulmonary disease (COPD) 

• Following relevant standards such as ISO 14155 (clinical study design) and good clinical practice (GCP) guidelines. 

At PRL, these validations are performed under CRO-managed, regulator-ready protocols—providing confidence that extracted respiratory parameters meet FDA or CE mark expectations. 

Conclusion: From Pulse to Breath 

The ability to extract respiratory rate from electrical and optical signals underscores the expanding utility of ECG and PPG waveforms in physiological monitoring. When paired with rigorous signal analysis and inclusive medical device validation, ECG- or PPG-based respiratory estimation can enhance the functionality of next-generation wearables, providing richer insights into people’s health and wellness. 

Partner with PRL to design and execute GCP/ICH-aligned studies that validate ECG and PPG respiratory rate algorithms, delivering transparent, scientifically rigorous data, and produce regulator-ready clinical endpoints—so sponsors can pursue FDA 510(k) and EU MDR/CE mark submissions with confidence. PRL delivers uncompromising data quality—backed by respiratory rate validation expertise, dedicated staff, and regulator-ready analysis and reporting. 

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