Pulse oximetry needs at least two wavelengths—typically red and infrared—to separate oxygenated from deoxygenated hemoglobin. PulseSensor is single-wavelength green. Nothing in this bibliography changes that, and a project should not claim SpO₂ from a single green channel.
PulseSensor Research G3 — The Science Under the Sensor
PulseSensor Research · G3 review
The science under the sensor
A working reading list for anyone pushing PPG past beats-per-minute—respiration, signal quality, placement, multi-sensor timing, and the algorithms that get you there.
The maker world is rich in mash-ups of technology and technique, and a little thin on the underlying science. This is our attempt to fix that. Not a product page, and not a claim about what PulseSensor measures—just the literature we read, the papers we hand to people who ask how would I actually do that?, and direct links so you can read them yourself.
The familiar PulseSensor starting point
From reflected light to heartbeat, BPM and HRV
Before looking for respiration, it helps to follow the traditional PulseSensor signal chain. PulseSensor is a reflectance photoplethysmograph: its green sensing LED shines into the tissue, and a photodetector measures the changing amount of light reflected back as each arterial pulse changes the local blood volume. The electronics turn that optical change into a continuously varying analog voltage—the raw PPG waveform.
The PulseSensor Playground code samples that waveform at 500 Hz. It follows the rising edge and changing amplitude of the pulse wave to identify qualified beats, measures the milliseconds between beats as the inter-beat interval (IBI), and derives beats per minute (BPM). A built-in or external feedback LED—including the red LED on the PulseSensor CyberDeck—can then flash or fade on each qualified beat.
1 · IlluminateGreen LEDLight enters fingertip or earlobe tissue.
2 · DetectReflected lightBlood-volume changes modulate the returned light.
3 · ObserveRaw PPG waveAn analog voltage traces pulse shape and morphology.
4 · MeasureBeat · IBI · BPMQualified beat timing produces intervals and heart rate.
5 · ExploreHRV and beyondIBI sequences support pulse-rate variability, PTT, and respiratory research.
A useful precision: when variability is measured from PPG pulse-to-pulse intervals, “pulse rate variability” is the most exact term. It is often used as an accessible approximation to ECG-derived heart-rate variability, but the two are not identical—especially during motion or changing vascular conditions.
Read the PulseSensor PPG and beat-detection explanation · Explore the open-source PulseSensor Playground library
From Dr. D. John Doyle
This annotated scientific bibliography offers a focused, practical roadmap for researchers and developers engaged in advanced signal processing of the photoplethysmograph (PPG) signal, with particular emphasis on the extraction of respiratory information. PPG, a non-invasive optical technique widely embedded in pulse oximeters and wearable devices, captures volumetric changes in blood flow and serves as a rich cardiovascular carrier signal subtly modulated by respiration.
The bibliography curates key literature—prioritizing PubMed-indexed and open-access sources—essential for building experimental platforms using low-cost hardware like Arduino or ESP32. It addresses the full pipeline: multi-channel PPG acquisition, respiratory-rate estimation, derivation of respiratory waveforms, signal-quality assessment, artifact rejection, and validation against reference signals such as capnography or impedance pneumography.
At its core, the collection recognizes respiration as a multifaceted modulator of the PPG waveform. Rather than relying on a single estimator, it advocates storing raw waveforms and metadata so several respiratory surrogates can be processed and combined in parallel.
RIAVRespiratory-induced amplitude variation
RIIVRespiratory-induced intensity or baseline variation
RIFVRespiratory-induced frequency variation
Read this before you build
Honest limits
PulseSensor hands your board an analog voltage. Sample rate, ADC bit depth, timing regularity, noise, and reference stability are yours to get right. Several methods below quietly fall apart without a clean, evenly timed sample stream.
These are educational and experimental instruments. Clinical papers describe results obtained with clinical methods and equipment; they are not instructions for measuring anyone’s health. PulseSensor is not a medical device and is not for diagnosis, treatment, monitoring, or emergency use.
Applied PPG research with PulseSensor
Change the sensor, the person, the place and the motion
The waveform is not produced by the heart alone. It is the combined result of physiology, body site, optical wavelength, skin and tissue, sensor pressure, ambient light, movement, electronics, and the algorithm. Those variables are not merely problems to remove: when recorded carefully, they become the experiment.
What one to four PulseSensors can investigate
1One clean referenceRaw morphology, beat timing, IBI, BPM, pulse-rate variability, RIAV, RIIV, RIFV, respiration, pressure and motion tests at one site.
2Compare two sitesLeft hand vs. right hand, ear vs. finger, or two points along one limb. Compare amplitude, shape, respiratory content and relative pulse-arrival delay.
3Add a controlKeep one stable reference while comparing two sites, two pressures or two placements. A third channel can also carry a respiratory reference or motion signal.
4Build a body mapRecord bilateral fingers and ears—or another carefully documented set—to study site, symmetry, timing, motion and signal quality together.
Body position and placement
Finger, forehead, earlobe, upper wrist, underside of wrist and arm do not produce interchangeable PPG signals. Paper 13 found that the accuracy of PPG-derived respiratory frequency depended on both measurement site and breathing pattern; forehead performed best during normal breathing and finger during deep breathing in that study.
With two sensors—one on each hand—record side, finger, orientation, strap pressure and arm position. Compare signal quality, pulse shape, amplitude, respiratory modulation and relative arrival time rather than assuming that a difference is physiological.
Skin pigmentation and optical bias
Melanin, tissue thickness, perfusion, sensor geometry and wavelength can change how much light reaches the detector. Paper 4 treats skin tone as one of several interacting sources of PPG inaccuracy alongside contact pressure, ambient light, motion, sensor position and physiology.
An inclusive experiment should record a participant’s self-described skin tone or a documented scale, preserve raw signal amplitude and quality, and report failures—not silently discard difficult recordings. Do not treat a software threshold that works for one participant as universal.
Movement and contact noise
Motion can look like a pulse, shift the baseline, change pressure, and disturb every respiratory surrogate. Paper 41 uses simultaneous acceleration to help reconstruct motion-contaminated PPG. Papers 43–45 support explicit signal-quality measures before reporting results.
Useful tests include stillness, tapping, hand rotation, walking and deliberate pressure changes. Record accelerometer data and experiment markers on the same clock as the PPG whenever possible.
Multiple colors and optical channels
Green, red and infrared light penetrate and scatter differently. Papers 42 and 47 show how multiple wavelengths and channels can help separate useful pulsatile information from motion. A normal PulseSensor provides one green optical channel; genuine multi-wavelength research requires additional red/infrared optical hardware, not merely multiple green PulseSensors.
Multiple ordinary PulseSensors are still valuable for spatial comparisons and redundancy. Label wavelength, LED drive, detector geometry and site for every channel.
Pulse arrival time and pulse transit time
Two PulseSensors placed at different distances from the heart can measure a relative pulse-arrival delay between two PPG waveforms. PulseSensor’s existing experiment places one sensor near the ear and one on a fingertip. If the distance between sites is measured and sampling is truly synchronized, the delay can support pulse-wave-travel experiments.
Important distinction: conventional pulse transit time usually begins at the heart’s electrical R-wave measured by ECG and ends at a peripheral PPG pulse. Two PPG sensors alone do not supply that ECG starting point; they measure the difference in arrival time between two peripheral sites. Changes may reflect vascular state, but they should not be presented as blood-pressure measurements or diagnoses without proper calibration and validation.
Explore the existing two-PulseSensor transit-time tutorial · See the open-source two-sensor example
Minimum record for every experiment
Save the raw waveform from every channel plus: exact sample timestamps, sensor and wavelength, body site and side, distance between sites when timing matters, attachment method and pressure, posture, breathing instruction, motion/accelerometer data, ambient-light notes, participant descriptors relevant to optical performance, signal-quality score, and the reference signal used for validation. That metadata is what lets another person reproduce—or challenge—the result.
Practical companions
Already on this site
We have been publishing hands-on signal work for years. These pages turn parts of the reading list into experiments you can run now.
Heart-rate variability
Multi-sensor experiments
Open science, ready to fork
Build on it
Why this reading list belongs on this site and not in a folder somewhere: the ideas point directly toward code and documentation we already give away. PulseSensor Playground is MIT-licensed, and it sits inside a decade of Arduino libraries, Processing and p5.js sketches, worked examples, and open documentation.
Connecting the bibliography to open-source code
Where the PulseSensor Open Science library can go next
The existing PulseSensor Playground already supplies the essential real-time foundation: raw samples, beat detection, pulse amplitude, IBI, BPM, 500 Hz timing, multiple-sensor support, and visual or physical feedback. The older PulseSensor HRV collection also demonstrates time-domain, frequency-domain, Poincaré-plot, and biofeedback explorations.
Six practical additions suggested by Dr. Doyle’s bibliography
- Research-ready waveform recorder. Export raw samples with exact timestamps, sensor location, sample rate, contact notes, and experiment markers. This creates the clean foundation repeatedly called for in papers 4, 7, 8, and 13.
- Modern pulse-rate variability example. Calculate and explain IBI series, RMSSD, SDNN, frequency-domain summaries, and Poincaré plots while clearly distinguishing PPG-derived pulse-rate variability from ECG HRV. This could refresh the existing HRV sketches inside the main library.
- “Find the breath” starter example. Derive RIAV from pulse amplitude, RIIV from the baseline, and RIFV from beat timing; then display the three candidate respiratory waves together. Papers 18, 20, 21, and 26 provide the scientific path.
- Embedded respiration spectrum. Add bandpass filtering plus Welch/FFT peak detection as a transparent first algorithm, with an output confidence score and a reference comparison channel. Papers 23, 29, and 30 are especially practical.
- Signal-quality gate. Label each analysis window “usable,” “questionable,” or “reject,” using pulse morphology and timing consistency before reporting BPM, HRV, or respiration. Start from papers 43, 44, and 45.
- Motion and multi-sensor research examples. Synchronize multiple PulseSensors and optional accelerometer data, then preserve every channel for offline comparison. Papers 41, 42, and 47 show why this matters.
Recommended first milestone: combine the waveform recorder, signal-quality gate, and three-channel RIAV/RIIV/RIFV display. That would turn the current library’s dependable heartbeat engine into a reproducible PPG research starter kit without hiding the underlying science.
Start here
Dr. Doyle’s suggested reading order
- Charlton et al. 2018 — the organizing framework
- Pimentel et al. 2017 — quality-aware fusion
- Karlen et al. 2013 — RIAV, RIIV and RIFV
- Dehkordi et al. 2018 — instantaneous respiratory rate
- Hartmann et al. 2019 — measurement-site effects
- Signal quality and motion — essential for wearables
- Modern algorithms and machine learning
- Wearable PPG roadmaps — where the field is going
Dr. Doyle’s complete reading list
Browse all 47 papers
Authors, dates, titles, and access status stay visible in the collapsed list. Open an entry for Dr. Doyle’s annotation, full citation, and paper link.
Open access
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Browse all 47 papers
Choose a topic
Every citation, author list, year, access label, direct paper link, and Dr. Doyle’s commentary appears below on this page.
Foundations and roadmaps8 papers · physiology, instrumentation and wearable research
Respiratory physiology7 papers · how breathing changes the PPG waveform
Extraction algorithms16 papers · filtering, spectra, fusion and tracking
ML, quality and robustness16 papers · motion, SQIs, multiple wavelengths and AI
01 Photoplethysmography and its application in clinical physiological measurement
Allen J. Photoplethysmography and its application in clinical physiological measurement. Physiological Measurement. 2007;28(3):R1-R39. doi:10.1088/0967-3334/28/3/R01. PMID:17322588.
Why Dr. Doyle included it
Open PubMed ↗
A classic entry point for PPG physiology and instrumentation. It remains useful because respiratory extraction depends on understanding the arterial, venous, autonomic, optical-coupling, and site-related sources of waveform variation.
02 A review on wearable photoplethysmography sensors and their potential future applications in health care
Castaneda D, Esparza A, Ghamari M, Soltanpur C, Nazeran H. A review on wearable photoplethysmography sensors and their potential future applications in health care. International Journal of Biosensors & Bioelectronics. 2018;4(4):195-202. doi:10.15406/ijbsbe.2018.04.00125.
Why Dr. Doyle included it
Open PMC ↗
Broad wearable-PPG review emphasizing sensor configurations, motion artifact, measurement site, and applications. Useful for choosing between finger, ear, forehead, wrist, and reflectance/transmission modes.
03 Current progress of photoplethysmography and SpO2 for health monitoring
Tamura T. Current progress of photoplethysmography and SpO2 for health monitoring. Biomedical Engineering Letters. 2019;9(1):21-36. doi:10.1007/s13534-019-00097-w. PMID:30956885.
Why Dr. Doyle included it
Open PMC ↗
Concise review of PPG and pulse-oximetry technology, including motion artifacts and health-monitoring applications. A useful bridge between clinical pulse oximetry and consumer/wearable PPG.
04 Sources of Inaccuracy in Photoplethysmography for Continuous Cardiovascular Monitoring
Fine J, Branan KL, Rodriguez AJ, Boonya-ananta T, Ajmal, Ramella-Roman JC, McShane MJ, Cote GL. Sources of Inaccuracy in Photoplethysmography for Continuous Cardiovascular Monitoring. Biosensors (Basel). 2021;11(4):126. doi:10.3390/bios11040126. PMID:33923469.
Why Dr. Doyle included it
Open PMC ↗
Highly relevant to experimental design. It catalogs sources of error including skin tone, motion, contact pressure, ambient light, sensor position, and physiology; all of these can corrupt respiratory-induced modulation.
05 Photoplethysmography Signal Processing and Synthesis. In: Kyriacou PA, Allen J, editors. Photoplethysmography: Technology, Signal Analysis and Applications. Academic Press; 2022.
Mejia-Mejia E, Budidha K, Abay TY, May JM, Kyriacou PA. Photoplethysmography Signal Processing and Synthesis. In: Kyriacou PA, Allen J, editors. Photoplethysmography: Technology, Signal Analysis and Applications. Academic Press; 2022.
Why Dr. Doyle included it
Open Author PDF ↗
Practical technical chapter on PPG preprocessing, time-domain analysis, frequency-domain analysis, machine learning, and synthetic signal generation. Valuable for moving from pulse counting to respiratory modulation extraction and simulator-driven testing.
06 Photoplethysmogram Analysis and Applications: An Integrative Review
Park J, Seok HS, Kim SS, Shin H. Photoplethysmogram Analysis and Applications: An Integrative Review. Frontiers in Physiology. 2022;12:808451. doi:10.3389/fphys.2021.808451. PMID:35222266.
Why Dr. Doyle included it
Open PMC ↗
Broad modern review of PPG analysis, physiology, and applications. It helps place respiratory extraction alongside HRV, blood pressure estimation, vascular assessment, autonomic analysis, and wearable sensing.
07 Wearable Photoplethysmography for Cardiovascular Monitoring
Charlton PH, Kyriacou PA, Mant J, Marozas V, Chowienczyk P, Alastruey J. Wearable Photoplethysmography for Cardiovascular Monitoring. Proceedings of the IEEE. 2022;110(3):355-381. doi:10.1109/JPROC.2022.3149785. PMID:35356509.
Why Dr. Doyle included it
Open PMC ↗
High-value review for wearable and low-cost PPG systems. Covers sensor design, preprocessing, signal quality, datasets, and clinical use; essential background for respiratory algorithms in wearable conditions.
08 The 2023 wearable photoplethysmography roadmap
Charlton PH, et al. The 2023 wearable photoplethysmography roadmap. Physiological Measurement. 2023;44(11):111001. doi:10.1088/1361-6579/acead2. PMID:37494945.
Why Dr. Doyle included it
Open PMC ↗
Strategic roadmap for wearable PPG research: sensor design, signal processing, clinical translation, datasets, bias, and open science. Strong reference for building an open Arduino/ESP32 PPG platform. Respiratory physiology and PPG modulation
09 Photoplethysmographic derivation of respiratory rate: a review of relevant physiology
Meredith DJ, Clifton D, Charlton PH, Brooks J, Pugh CW, Tarassenko L. Photoplethysmographic derivation of respiratory rate: a review of relevant physiology. Journal of Medical Engineering & Technology. 2012;36(1):1-7. doi:10.3109/03091902.2011.638965. PMID:22185462.
Why Dr. Doyle included it
Open Author PDF ↗
Foundational physiological review explaining how breathing modulates PPG through venous return, intrathoracic pressure, autonomic effects, and respiratory sinus arrhythmia. Justifies parallel extraction of RIIV, RIAV, and RIFV.
10 Respiration signals from photoplethysmography
Nilsson LM. Respiration signals from photoplethysmography. Anesthesia & Analgesia. 2013;117(4):859-865. doi:10.1213/ANE.0b013e31828098b2. PMID:23449854.
Why Dr. Doyle included it
Open PubMed ↗
Clinically oriented review from anesthesia and monitoring. Particularly relevant to OR and procedural settings where pulse oximetry is already present and respiration monitoring may be desirable without extra sensors.
11 Comparison of respiratory-induced variations in photoplethysmographic signals
Li J, Jin J, Chen X, Sun W, Guo P. Comparison of respiratory-induced variations in photoplethysmographic signals. Physiological Measurement. 2010;31(3):415-425. doi:10.1088/0967-3334/31/3/009. PMID:20147775.
Why Dr. Doyle included it
Open PubMed ↗
Important comparison of respiratory-induced PPG variations. Supports storing raw waveform data so that multiple modulation families can be compared offline rather than relying on one respiratory surrogate.
12 Comparison of different modulations of photoplethysmography in extracting respiratory rate: from a physiological perspective
Liu H, et al. Comparison of different modulations of photoplethysmography in extracting respiratory rate: from a physiological perspective. Physiological Measurement. 2020;41(9):094001. doi:10.1088/1361-6579/abaaf0. PMID:32731213.
Why Dr. Doyle included it
Open PubMed ↗
Corrected title. Useful because it frames respiratory extraction in terms of physiological mechanisms rather than numerical performance alone. It reminds developers that amplitude, baseline, and frequency modulation may fail under different conditions.
13 Toward Accurate Extraction of Respiratory Frequency From the Photoplethysmogram: Effect of Measurement Site
Hartmann V, Liu H, Chen F, Qiu Q, Hughes S, Zheng D. Toward Accurate Extraction of Respiratory Frequency From the Photoplethysmogram: Effect of Measurement Site. Frontiers in Physiology. 2019;10:732. doi:10.3389/fphys.2019.00732. PMID:31316390.
Why Dr. Doyle included it
Open PMC ↗
Directly relevant to hardware design. Shows that PPG-derived respiratory frequency depends on measurement site and breathing pattern; strongly supports testing multiple sensor locations or optical channels.
14 Respiratory modulations in the photoplethysmogram (DPOP) as a measure of respiratory effort
Addison PS. Respiratory modulations in the photoplethysmogram (DPOP) as a measure of respiratory effort. Journal of Clinical Monitoring and Computing. 2016;30(5):595-602. doi:10.1007/s10877-015-9763-y. PMID:26377021.
Why Dr. Doyle included it
Open PMC ↗
Useful for moving beyond respiratory rate toward respiratory effort. DPOP quantifies the strength of respiratory modulation in the pleth waveform; a platform should consider modulation depth as well as rate.
15 Respiratory effort from the photoplethysmogram
Addison PS. Respiratory effort from the photoplethysmogram. Medical Engineering & Physics. 2017;41:9-18. doi:10.1016/j.medengphy.2016.12.010. PMID:28126420.
Why Dr. Doyle included it
Open PubMed ↗
Explores candidate PPG-derived parameters for respiratory effort, including amplitude, baseline, frequency, and pulse-transit-related changes. Suggests richer endpoints than respiratory rate alone. Classical and advanced algorithmic extraction of respiration from PPG
16 Monitoring of heart and respiratory rates by photoplethysmography using a digital filtering technique
Nakajima K, Tamura T, Miike H. Monitoring of heart and respiratory rates by photoplethysmography using a digital filtering technique. Medical Engineering & Physics. 1996;18(5):365-372. doi:10.1016/1350-4533(95)00066-6. PMID:8762834.
Why Dr. Doyle included it
Open PubMed ↗
Historically important early demonstration that both heart and respiratory rates can be extracted from PPG with digital filtering. Dated technologically, but conceptually useful as a simple baseline.
17 Estimation of respiratory rate from photoplethysmogram data using time-frequency spectral estimation
Chon KH, Dash S, Ju K. Estimation of respiratory rate from photoplethysmogram data using time-frequency spectral estimation. IEEE Transactions on Biomedical Engineering. 2009;56(8):2054-2063. doi:10.1109/TBME.2009.2019766. PMID:19369147.
Why Dr. Doyle included it
Open PubMed ↗
Key paper in the transition from simple filtering to time-frequency spectral estimation. Important for nonstationary breathing; a good offline benchmark against cheaper embedded algorithms.
18 Multiparameter respiratory rate estimation from the photoplethysmogram
Karlen W, Raman S, Ansermino JM, Dumont GA. Multiparameter respiratory rate estimation from the photoplethysmogram. IEEE Transactions on Biomedical Engineering. 2013;60(7):1946-1953. doi:10.1109/TBME.2013.2246160. PMID:23399950.
Why Dr. Doyle included it
Open PubMed ↗
Central paper for PPG-derived respiratory rate. It extracts respiratory-induced frequency, intensity, and amplitude variations and combines information across them; highly relevant to a multi-channel platform.
19 An assessment of algorithms to estimate respiratory rate from the electrocardiogram and photoplethysmogram
Charlton PH, Bonnici T, Tarassenko L, Clifton DA, Beale R, Watkinson PJ. An assessment of algorithms to estimate respiratory rate from the electrocardiogram and photoplethysmogram. Physiological Measurement. 2016;37(4):610-626. doi:10.1088/0967-3334/37/4/610. PMID:27027672.
Why Dr. Doyle included it
Open PMC ↗
Methodological cornerstone comparing algorithms and emphasizing validation, data quality, and common evaluation methods. Important antidote to overfitting algorithms to one clean dataset.
20 Toward a Robust Estimation of Respiratory Rate From Pulse Oximeters
Pimentel MAF, Johnson AEW, Charlton PH, Birrenkott D, Watkinson PJ, Tarassenko L, Clifton DA. Toward a Robust Estimation of Respiratory Rate From Pulse Oximeters. IEEE Transactions on Biomedical Engineering. 2017;64(8):1914-1923. doi:10.1109/TBME.2016.2613124. PMID:28113393.
Why Dr. Doyle included it
Open PMC ↗
Must-read for robust RR estimation from pulse oximetry. Uses respiratory-induced variations, quality assessment, and probabilistic fusion; one of the best algorithmic templates for a practical platform.
21 Breathing Rate Estimation From the Electrocardiogram and Photoplethysmogram: A Review
Charlton PH, Birrenkott DA, Bonnici T, Pimentel MAF, Johnson AEW, Alastruey J, Tarassenko L, Watkinson PJ, Beale R, Clifton DA. Breathing Rate Estimation From the Electrocardiogram and Photoplethysmogram: A Review. IEEE Reviews in Biomedical Engineering. 2018;11:2-20. doi:10.1109/RBME.2017.2763681. PMID:29990026.
Why Dr. Doyle included it
Open PMC ↗
The best single review for breathing-rate estimation from ECG and PPG. Lays out preprocessing, respiratory-signal extraction, rate estimation, fusion, and quality assessment.
22 Estimating respiratory and heart rates from the correntropy spectral density of the photoplethysmogram
Garde A, Karlen W, Ansermino JM, Dumont GA. Estimating respiratory and heart rates from the correntropy spectral density of the photoplethysmogram. PLOS ONE. 2014;9(1):e86427. doi:10.1371/journal.pone.0086427. PMID:24466088.
Why Dr. Doyle included it
Open PMC ↗
Corrected author list: Dehkordi was removed. Uses correntropy spectral density to improve robustness to noise and non-Gaussian artifacts; useful when simple FFT/Welch methods are unreliable.
23 Real-time estimation of respiratory rate from a photoplethysmogram using an adaptive lattice notch filter
Park C, Lee B, Lee J. Real-time estimation of respiratory rate from a photoplethysmogram using an adaptive lattice notch filter. BioMedical Engineering OnLine. 2014;13:170. doi:10.1186/1475-925X-13-170. PMID:25518918.
Why Dr. Doyle included it
Open PMC ↗
Corrected title and venue. Valuable for real-time feasibility: algorithms that work in MATLAB may be too memory- or CPU-intensive for an embedded device.
24 Respiratory rate monitoring from the photoplethysmogram via sparse signal reconstruction
Zhang X, Ding Q. Respiratory rate monitoring from the photoplethysmogram via sparse signal reconstruction. Physiological Measurement. 2016;37(7):1105-1119. doi:10.1088/0967-3334/37/7/1105. PMID:27319303.
Why Dr. Doyle included it
Open PubMed ↗
Corrected full citation. Treats respiration as a sparse spectral component embedded in a stronger cardiac carrier and noise; likely more useful as an offline benchmark than a first Arduino algorithm.
25 Estimating instantaneous respiratory rate from the photoplethysmogram
Dehkordi P, Garde A, Karlen W, Ansermino JM, Dumont GA. Estimating instantaneous respiratory rate from the photoplethysmogram. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2015;2015:6150-6153. doi:10.1109/EMBC.2015.7319796. PMID:26737696.
Why Dr. Doyle included it
Open PubMed ↗
Corrected conference citation. Introduces a synchrosqueezing-transform direction for instantaneous respiratory-rate extraction; likely too heavy for first-pass embedded use but a valuable offline standard.
26 Extracting Instantaneous Respiratory Rate From Multiple Photoplethysmogram Respiratory-Induced Variations
Dehkordi P, Garde A, Karlen W, Wensley D, Ansermino JM, Dumont GA. Extracting Instantaneous Respiratory Rate From Multiple Photoplethysmogram Respiratory-Induced Variations. Frontiers in Physiology. 2018;9:948. doi:10.3389/fphys.2018.00948. PMID:30131724.
Why Dr. Doyle included it
Open PMC ↗
Central open-access paper for advanced respiratory extraction. Extracts RIIV, RIAV, and RIFV, estimates instantaneous RR from each, and fuses estimates.
27 How nonlinear-type time-frequency analysis can help in sensing instantaneous heart rate and instantaneous respiratory rate from photoplethysmography in a reliable way
Cicone A, Wu HT. How nonlinear-type time-frequency analysis can help in sensing instantaneous heart rate and instantaneous respiratory rate from photoplethysmography in a reliable way. Frontiers in Physiology. 2017;8:701. doi:10.3389/fphys.2017.00701. PMID:29018395.
Why Dr. Doyle included it
Open PMC ↗
Mathematically advanced nonlinear time-frequency analysis for instantaneous heart and respiratory rates. Not a first embedded method, but useful as a high-end reference standard.
28 Breathing Rate Estimation Using Kalman Smoother With Electrocardiogram and Photoplethysmogram
Khreis S, Ge D, Rahman HA, Carrault G. Breathing Rate Estimation Using Kalman Smoother With Electrocardiogram and Photoplethysmogram. IEEE Transactions on Biomedical Engineering. 2020;67(3):893-904. doi:10.1109/TBME.2019.2923448. PMID:31217092.
Why Dr. Doyle included it
Open PubMed ↗
Shows the value of temporal tracking/smoothing for breath-rate estimates. A Kalman or Bayesian smoother is a natural second-stage module after raw estimator extraction.
29 Photoplethysmography-Based Respiratory Rate Estimation Algorithm for Health Monitoring Applications
Iqbal T, Elahi A, Ganly S, Wijns W, Shahzad A. Photoplethysmography-Based Respiratory Rate Estimation Algorithm for Health Monitoring Applications. Journal of Medical and Biological Engineering. 2022;42(2):242-252. doi:10.1007/s40846-022-00700-z. PMID:35535218.
Why Dr. Doyle included it
Open PMC ↗
Corrected full citation. Practical algorithm using selective windowing, preprocessing, modified Welch filtering, and postprocessing. A useful baseline because it is less exotic than synchrosqueezing but more robust than simple filtering.
30 Fast and Robust Real-Time Estimation of Respiratory Rate from Photoplethysmography
Kim H, Lee HJ, Kim JS, Whang MC. Fast and Robust Real-Time Estimation of Respiratory Rate from Photoplethysmography. Sensors (Basel). 2016;16(9):1494. doi:10.3390/s16091494. PMID:27649195.
Why Dr. Doyle included it
Open PMC ↗
Added as a useful correction/augmentation. Proposes fast real-time RR estimation from PPG, relevant to low-resource embedded implementations and comparison with Park-style adaptive filtering.
31 Acquiring Respiration Rate from Photoplethysmographic Signal by Recursive Bayesian Tracking of Intrinsic Modes in Time-Frequency Spectra
Pirhonen M, Peltokangas M, Vehkaoja A. Acquiring Respiration Rate from Photoplethysmographic Signal by Recursive Bayesian Tracking of Intrinsic Modes in Time-Frequency Spectra. Sensors (Basel). 2018;18(6):1693. doi:10.3390/s18061693. PMID:29843380.
Why Dr. Doyle included it
Open PMC ↗
Useful open-access time-frequency/Bayesian tracking paper. It broadens the algorithmic menu beyond RIAV/RIIV/RIFV and standard spectral peaks. Machine learning, deep learning, signal quality, and robustness
32 Determining respiratory rate from photoplethysmogram and electrocardiogram signals using respiratory quality indices and neural networks
Baker S, Xiang W, Atkinson I. Determining respiratory rate from photoplethysmogram and electrocardiogram signals using respiratory quality indices and neural networks. PLOS ONE. 2021;16(4):e0249843. doi:10.1371/journal.pone.0249843. PMID:33891653.
Why Dr. Doyle included it
Open PMC ↗
Strong bridge between classical modulation extraction and neural networks. Emphasizes respiratory signal quality quantification before feeding information into models.
33 Estimation of Heart Rate and Respiratory Rate from PPG Signal Based on Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise and Independent Component Analysis
Lei R, Chen C, Zhou C, Zhang Y, Yang Z, Wang X. Estimation of Heart Rate and Respiratory Rate from PPG Signal Based on Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise and Independent Component Analysis. Sensors (Basel). 2020;20(11):3237. doi:10.3390/s20113237. PMID:32517009.
Why Dr. Doyle included it
Open PMC ↗
Uses decomposition and source-separation style processing to recover heart and respiratory rates. Useful for comparing EMD/ICA approaches with simpler bandpass methods.
34 Lightweight End-to-End Deep Learning Solution for Estimating the Respiration Rate from Photoplethysmogram Signal
Chowdhury MH, Shuzan MNI, Chowdhury MEH, Reaz MBI, Mahmud S, Al Emadi N, et al. Lightweight End-to-End Deep Learning Solution for Estimating the Respiration Rate from Photoplethysmogram Signal. Bioengineering (Basel). 2022;9(10):558. doi:10.3390/bioengineering9100558.
Why Dr. Doyle included it
Open PMC ↗
Corrected title and venue. Useful as a benchmark for direct RR estimation from raw/processed PPG, even if a full deep model is not the first target for Arduino deployment.
35 Evaluation of the Photoplethysmogram-Based Deep Learning Model for Respiratory Rate Estimation
Hwang CS, et al. Evaluation of the Photoplethysmogram-Based Deep Learning Model for Respiratory Rate Estimation. Sensors (Basel). 2023;23(20):8377. doi:10.3390/s23208377. PMID:37892952.
Why Dr. Doyle included it
Open PMC ↗
Corrected to a specific Sensors article. Evaluates deep learning models for continuous RR estimation from PPG and helps identify what labeled respiratory reference signal would be needed for training or validation.
36 Machine Learning-Based Respiration Rate and Blood Oxygen Saturation Estimation Using Photoplethysmogram Signals
Shuzan MNI, Chowdhury MH, Chowdhury MEH, Murugappan M, Bhuiyan EH, Ayari MA, Khandakar A. Machine Learning-Based Respiration Rate and Blood Oxygen Saturation Estimation Using Photoplethysmogram Signals. Bioengineering (Basel). 2023;10(2):167. doi:10.3390/bioengineering10020167. PMID:36829661.
Why Dr. Doyle included it
Open PMC ↗
Machine-learning approach to estimating both RR and SpO2-related information from PPG. Suggests future multi-output models that estimate RR, signal quality, SpO2 surrogates, and artifact flags.
37 A novel respiratory rate estimation algorithm from photoplethysmogram using deep learning model
Chin WJ, Kwan BH, Lim WY, Tee YK, Darmaraju S, Liu H, Goh CH. A novel respiratory rate estimation algorithm from photoplethysmogram using deep learning model. Diagnostics (Basel). 2024;14(3):284. doi:10.3390/diagnostics14030284. PMID:38337800.
Why Dr. Doyle included it
Open PMC ↗
Corrected title. Recent open-access paper explicitly focused on RR from PPG; useful contemporary comparator and bridge to older RIAV/RIIV/RIFV methods.
38 Energy-Efficient PPG-Based Respiratory Rate Estimation Using Spiking Neural Networks
Yang G, Kang Y, Charlton PH, Kyriacou PA, Kim KK, Li L, Park C. Energy-Efficient PPG-Based Respiratory Rate Estimation Using Spiking Neural Networks. Sensors (Basel). 2024;24(12):3980. doi:10.3390/s24123980. PMID:38931569.
Why Dr. Doyle included it
Open PMC ↗
Relevant for low-power and embedded ambitions. Spiking neural networks may eventually map well to efficient hardware, even if not immediately Arduino-friendly.
39 The use of successive systolic differences in photoplethysmography for respiratory rate estimation
Arguello-Prada EJ, et al. The use of successive systolic differences in photoplethysmography for respiratory rate estimation. Heliyon. 2024;10(2):e24576. doi:10.1016/j.heliyon.2024.e24576.
Why Dr. Doyle included it
Open PMC ↗
Introduces respiratory-induced variations in successive systolic differences as an alternative to classic amplitude, intensity, and frequency families; valuable for beat-to-beat morphology-oriented experiments.
40 Comparison of Techniques for Respiratory Rate Extraction from ECG and PPG Signals
Ponsiglione AM, et al. Comparison of Techniques for Respiratory Rate Extraction from ECG and PPG Signals. Sensors (Basel). 2025;25(16):5136. doi:10.3390/s25165136. PMID:40871998.
Why Dr. Doyle included it
Open PMC ↗
Recent comparative paper evaluating extraction from ECG and PPG. Useful for sober benchmarking and for recognizing conditions where PPG-derived respiration may underperform ECG-derived features.
41 Estimation of Respiratory Rate from Motion Contaminated Photoplethysmography Signals Incorporating Accelerometry
Jarchi D, Charlton PH, Pimentel MAF, Casson AJ, Tarassenko L, Clifton DA. Estimation of Respiratory Rate from Motion Contaminated Photoplethysmography Signals Incorporating Accelerometry. Healthcare Technology Letters. 2019;6(1):19-24. doi:10.1049/htl.2018.5019.
Why Dr. Doyle included it
Open PMC ↗
Corrected full title. Motion-contaminated PPG is the normal wearable case; supports adding accelerometers and explicit motion labels to the platform.
42 Motion Artifact Reduction in Wearable Photoplethysmography Based on Multi-Channel Sensors with Multiple Wavelengths
Lee J, Kim M, Park HK, Kim IY. Motion Artifact Reduction in Wearable Photoplethysmography Based on Multi-Channel Sensors with Multiple Wavelengths. Sensors (Basel). 2020;20(5):1493. doi:10.3390/s20051493.
Why Dr. Doyle included it
Open PMC ↗
Directly relevant to hardware. Supports multi-channel and multi-wavelength PPG sensors for artifact reduction; useful for separating respiratory physiology from motion and contact artifacts.
43 Optimal Signal Quality Index for Photoplethysmogram Signals
Elgendi M. Optimal Signal Quality Index for Photoplethysmogram Signals. Bioengineering (Basel). 2016;3(4):21. doi:10.3390/bioengineering3040021. PMID:28952542.
Why Dr. Doyle included it
Open PMC ↗
Key open paper on PPG signal-quality indices. Respiratory extraction is only as reliable as the source waveform; supports quality gating before RR estimation.
44 Signal quality measure for pulsatile physiological signals using morphological features: Applications in reliability measure for pulse oximetry
Sabeti E, Reamaroon N, Mathis J, et al. Signal quality measure for pulsatile physiological signals using morphological features: Applications in reliability measure for pulse oximetry. Informatics in Medicine Unlocked. 2019;16:100222. doi:10.1016/j.imu.2019.100222. PMID:32864419.
Why Dr. Doyle included it
Open PMC ↗
Corrected title and application phrase. Proposes morphological features for signal quality and reliability, which can support exclusion/down-weighting of unreliable PPG windows.
45 Signal-quality indices for the electrocardiogram and photoplethysmogram: derivation and applications to wireless monitoring
Orphanidou C, Bonnici T, Charlton P, Clifton D, Vallance D, Tarassenko L. Signal-quality indices for the electrocardiogram and photoplethysmogram: derivation and applications to wireless monitoring. IEEE Journal of Biomedical and Health Informatics. 2015;19(3):832-838. doi:10.1109/JBHI.2014.2338351. PMID:25069129.
Why Dr. Doyle included it
Open PubMed ↗
Highly cited SQI paper in wearable monitoring. Reinforces that signal quality is task-specific: a segment may be adequate for heart rate but not for respiratory information.
46 Robust respiration detection from remote photoplethysmography
van Gastel M, Stuijk S, de Haan G. Robust respiration detection from remote photoplethysmography. Biomedical Optics Express. 2016;7(12):4941-4957. doi:10.1364/BOE.7.004941. PMID:28018717.
Why Dr. Doyle included it
Open PMC ↗
Remote/camera-based PPG paper relevant because it extracts respiration from color/intensity variations and addresses motion robustness. Broadens the project from contact PPG to optical/camera respiratory sensing.
47 Motion Artifact Reduction for Wrist-Worn Photoplethysmograph Sensors Based on Different Wavelengths
Zhang Y, Song S, Vullings R, Biswas D, Simões-Capela N, Van Helleputte N, Van Hoof C, Groenendaal W. Motion Artifact Reduction for Wrist-Worn Photoplethysmograph Sensors Based on Different Wavelengths. Sensors (Basel). 2019;19(3):673. doi:10.3390/s19030673. PMID:30764575.
Why Dr. Doyle included it
Open MDPI ↗
Useful adjunct for multi-wavelength hardware design. Although focused mainly on heart-rate recovery, the motion-removal logic matters for any respiratory estimator using wearable PPG.
Notes for a PulseSensor / Arduino / ESP32 platform
Move from reading to experimentation
Begin with bandpass filtering and Welch/FFT respiratory peak detection; extract RIAV, RIIV, and RIFV; add simple fusion or median selection; add signal-quality gating; and compare with a reference respiratory signal where available. Plan for accelerometer integration and multi-wavelength sensors to combat artifacts.
Corrected annotated bibliography revised July 8, 2026. G3 presentation assembled July 28, 2026 with AI assistance; readers should independently verify citations and licensing before reproducing figures or tables. Working page marker: research-g3.