PulseSensor Research (H2)
H2 maintained library · PPG science and experiments
The science under the sensor
Learn what is hiding inside a pulse waveform—from heartbeat timing to respiration-related variations—and how to explore it carefully with PulseSensor.
This page connects Dr. D. John Doyle’s annotated 47-paper bibliography to practical, reproducible experiments. Start with the signal, choose a build path, or go straight to the papers.
Every paper record below was checked against persistent identifiers and its destination link on 28 Jul 2026. We prefer DOI, PubMed, and legitimate full-text copies in PubMed Central. PulseSensor.com does not copy or host the papers themselves.
How H2 is maintained
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- H2 corrects three mismatched DOI records, twelve incorrect PMIDs, and five missing PMIDs inherited from the earlier iteration.
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PPG in 60 seconds
PulseSensor is a reflectance photoplethysmograph (PPG). It turns small changes in reflected green light into an analog waveform that your development board can sample.
A useful precision: variability calculated from PPG pulse-to-pulse intervals is most accurately called pulse-rate variability. It is related to ECG-derived heart-rate variability, but the two are not identical—especially during motion or changing vascular conditions.
What the current PulseSensor can—and cannot—do
Available now
- One green reflectance PPG channel per sensor
- Raw analog waveform for your board’s ADC
- PulseSensor Playground beat, IBI, BPM, amplitude, and raw-sample access
- 500 Hz library timing and support for multiple PulseSensor instances
- Open-source examples for one sensor, multiple sensors, WebSerial, and timing experiments
Not a product claim
- Not a pulse oximeter and not an SpO₂ sensor
- Not a validated respiration-rate monitor
- Not a blood-pressure monitor
- Not medical equipment or a safety-critical monitor
- Research papers made with clinical hardware do not automatically validate a maker build
Inspect the open-source PulseSensor Playground library ↗
Choose an experiment
Start with the fewest channels that can answer your question. Every additional sensor adds useful comparisons—and new synchronization, placement, pressure, and motion variables.
One sensor
- Raw waveform and morphology
- Beat events, IBI, BPM, and pulse-rate variability
- Explore RIAV, RIIV, and RIFV as candidate respiration-related variations
- Test placement, pressure, light, and motion
Two sensors
- Compare finger, ear, left/right, or proximal/distal sites
- Compare amplitude, shape, signal quality, and respiratory content
- Measure synchronized relative pulse-arrival delay
- Separate shared from site-specific changes cautiously
Fusion and controls
- Keep a stable PPG reference while changing placement or pressure
- Add an accelerometer or respiratory reference channel
- Compare quality-weighted candidate estimators
- Build carefully documented spatial experiments
A reproducible PPG workflow
Build now, then extend the library
Build with today’s code
Research directions suggested by the bibliography
- A research-ready raw waveform recorder with metadata
- A transparent RIAV/RIIV/RIFV starter example
- A signal-quality gate before reporting estimates
- Optional accelerometer and reference-channel synchronization
- Modern pulse-rate variability examples with clear terminology
Dr. Doyle’s advanced PPG reading guide
An annotated bibliography, with emphasis on extracting respiration from the PPG signal
The short version
The literature treats the PPG as a cardiovascular carrier signal modulated by respiration. Respiratory information appears as respiratory-induced amplitude variation (RIAV), intensity or baseline variation (RIIV), frequency variation through respiratory sinus arrhythmia (RIFV), pulse-width and morphology changes, slow baseline shifts, and site-dependent venous/arterial interactions.
The practical consequence for anyone building on an Arduino or ESP32: store raw waveforms and metadata, and support several parallel respiratory channels rather than one estimator. Pick a single method too early and you will spend months tuning a number that a second channel would have told you not to trust.
Where to start
Forty-seven papers is a lot. In this order, each one makes the next easier.
- Charlton et al. 2018Organizing framework for the entire field of ECG/PPG breathing-rate estimation.
- Pimentel et al. 2017Robust pulse-oximeter RR estimation with quality assessment and fusion.
- Karlen et al. 2013Classic multiparameter RIAV/RIIV/RIFV estimator.
- Dehkordi et al. 2018Instantaneous RR from multiple respiratory-induced PPG variations.
- Hartmann et al. 2019Measurement-site effects; directly informs sensor placement.
- Elgendi 2016; Orphanidou 2015; Jarchi et al. 2019Signal quality and motion contamination.
- Baker et al. 2021; Iqbal et al. 2022; Chin et al. 2024Modern algorithmic and machine-learning extensions.
- Charlton et al. 2022 and 2023Roadmap-level guidance for open wearable PPG systems.
Browse the 47-paper bibliography
Commentary is Dr. Doyle’s. Citation metadata and source links are maintained against DOI, PubMed, and PMC records. The colour shows whether a legitimate full-text copy is linked.
Foundations
8Physiology, instrumentation, error sources, and where the wearable field is headed.
Allen J. Photoplethysmography and its application in clinical physiological measurement. Physiol Meas. 2007;28(3):R1-39. doi:10.1088/0967-3334/28/3/R01. PMID:17322588.
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.
Castaneda D, Esparza A, Ghamari M, Soltanpur C, Nazeran H. A review on wearable photoplethysmography sensors and their potential future applications in health care. Int J Biosens Bioelectron. 2018;4(4):195-202. doi:10.15406/ijbsbe.2018.04.00125. PMID:30906922.
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.
Tamura T. Current progress of photoplethysmography and SPO(2) for health monitoring. Biomed Eng Lett. 2019;9(1):21-36. doi:10.1007/s13534-019-00097-w. PMID:30956878.
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.
Fine J, Branan KL, Rodriguez AJ, Boonya-Ananta T, Ajmal, Ramella-Roman JC, McShane MJ, Coté GL. Sources of Inaccuracy in Photoplethysmography for Continuous Cardiovascular Monitoring. Biosensors (Basel). 2021;11(4):126. doi:10.3390/bios11040126. PMID:33923469.
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.
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.
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.
Park J, Seok HS, Kim SS, Shin H. Photoplethysmogram Analysis and Applications: An Integrative Review. Front Physiol. 2022;12:808451. doi:10.3389/fphys.2021.808451. PMID:35300400.
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.
Charlton PH, Kyriaco PA, Mant J, Marozas V, Chowienczyk P, Alastruey J. Wearable Photoplethysmography for Cardiovascular Monitoring. Proc IEEE Inst Electr Electron Eng. 2022;110(3):355-381. doi:10.1109/JPROC.2022.3149785. PMID:35356509.
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.
Charlton PH, Allen J, Bailón R, Baker S, Behar JA, Chen F, Clifford GD, Clifton DA, Davies HJ, Ding C, Ding X, Dunn J, Elgendi M, Ferdoushi M, Franklin D, Gil E, Hassan MF, Hernesniemi J, Hu X, Ji N, Khan Y, Kontaxis S, Korhonen I, Kyriacou PA, Laguna P, Lázaro J, Lee C, Levy J, Li Y, Liu C, Liu J, Lu L, Mandic DP, Marozas V, Mejía-Mejía E, Mukkamala R, Nitzan M, Pereira T, Poon CCY, Ramella-Roman JC, Saarinen H, Shandhi MMH, Shin H, Stansby G, Tamura T, Vehkaoja A, Wang WK, Zhang YT, Zhao N, Zheng D, Zhu T. The 2023 wearable photoplethysmography roadmap. Physiol Meas. 2023;44(11):111001. doi:10.1088/1361-6579/acead2. PMID:37494945.
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 modulation
7Why breathing shows up in a heartbeat signal at all.
Meredith DJ, Clifton D, Charlton P, Brooks J, Pugh CW, Tarassenko L. Photoplethysmographic derivation of respiratory rate: a review of relevant physiology. J Med Eng Technol. 2012;36(1):1-7. doi:10.3109/03091902.2011.638965. PMID:22185462.
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.
Nilsson LM. Respiration signals from photoplethysmography. Anesth Analg. 2013;117(4):859-865. doi:10.1213/ANE.0b013e31828098b2. PMID:23449854.
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.
Li J, Jin J, Chen X, Sun W, Guo P. Comparison of respiratory-induced variations in photoplethysmographic signals. Physiol Meas. 2010;31(3):415-25. doi:10.1088/0967-3334/31/3/009. PMID:20147775.
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.
Liu H, Chen F, Hartmann V, Khalid SG, Hughes S, Zheng D. Comparison of different modulations of photoplethysmography in extracting respiratory rate: from a physiological perspective. Physiol Meas. 2020;41(9):094001. doi:10.1088/1361-6579/abaaf0. PMID:32731213.
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.
Hartmann V, Liu H, Chen F, Hong W, Hughes S, Zheng D. Toward Accurate Extraction of Respiratory Frequency From the Photoplethysmogram: Effect of Measurement Site. Front Physiol. 2019;10:732. doi:10.3389/fphys.2019.00732. PMID:31316390.
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.
Addison PS. Respiratory modulations in the photoplethysmogram (DPOP) as a measure of respiratory effort. J Clin Monit Comput. 2016;30(5):595-602. doi:10.1007/s10877-015-9763-y. PMID:26377021.
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.
Addison PS. Respiratory effort from the photoplethysmogram. Med Eng Phys. 2017;41:9-18. doi:10.1016/j.medengphy.2016.12.010. PMID:28126420.
Explores candidate PPG-derived parameters for respiratory effort, including amplitude, baseline, frequency, and pulse-transit-related changes. Suggests richer endpoints than respiratory rate alone.
Algorithms
16Filtering, time-frequency analysis, synchrosqueezing, probabilistic fusion.
Degueurce C, Dietrich G, Pourcelot P, Denoix JM, Geiger D. Three-dimensional kinematic technique for evaluation of horse locomotion in outdoor conditions. Med Biol Eng Comput. 1996;34(3):249-52. doi:10.1016/1350-4533(95)00066-6. PMID:8762834.
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.
Chon KH, Dash S, Ju K. Estimation of respiratory rate from photoplethysmogram data using time-frequency spectral estimation. IEEE Trans Biomed Eng. 2009;56(8):2054-63. doi:10.1109/TBME.2009.2019766. PMID:19369147.
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.
Karlen W, Raman S, Ansermino JM, Dumont GA. Multiparameter respiratory rate estimation from the photoplethysmogram. IEEE Trans Biomed Eng. 2013;60(7):1946-53. doi:10.1109/TBME.2013.2246160. PMID:23399950.
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.
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. Physiol Meas. 2016;37(4):610-26. doi:10.1088/0967-3334/37/4/610. PMID:27027672.
Methodological cornerstone comparing algorithms and emphasizing validation, data quality, and common evaluation methods. Important antidote to overfitting algorithms to one clean dataset.
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 Trans Biomed Eng. 2017;64(8):1914-1923. doi:10.1109/TBME.2016.2613124. PMID:27875128.
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.
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 Rev Biomed Eng. 2018;11:2-20. doi:10.1109/RBME.2017.2763681. PMID:29990026.
The best single review for breathing-rate estimation from ECG and PPG. Lays out preprocessing, respiratory-signal extraction, rate estimation, fusion, and quality assessment.
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.
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.
Park C, Lee B. Real-time estimation of respiratory rate from a photoplethysmogram using an adaptive lattice notch filter. Biomed Eng Online. 2014;13:170. doi:10.1186/1475-925X-13-170. PMID:25518918.
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.
Zhang X, Ding Q. Respiratory rate monitoring from the photoplethysmogram via sparse signal reconstruction. Physiol Meas. 2016;37(7):1105-19. doi:10.1088/0967-3334/37/7/1105. PMID:27319303.
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.
Dehkordi P, Garde A, Molavi B, Petersen CL, Ansermino JM, Dumont GA. Estimating instantaneous respiratory rate from the photoplethysmogram. Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:6150-3. doi:10.1109/EMBC.2015.7319796. PMID:26737696.
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.
Dehkordi P, Garde A, Molavi B, Ansermino JM, Dumont GA. Extracting Instantaneous Respiratory Rate From Multiple Photoplethysmogram Respiratory-Induced Variations. Front Physiol. 2018;9:948. doi:10.3389/fphys.2018.00948. PMID:30072918.
Central open-access paper for advanced respiratory extraction. Extracts RIIV, RIAV, and RIFV, estimates instantaneous RR from each, and fuses estimates.
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. Front Physiol. 2017;8:701. doi:10.3389/fphys.2017.00701. PMID:29018352.
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.
Khreis S, Ge D, Rahman HA, Carrault G. Breathing Rate Estimation Using Kalman Smoother With Electrocardiogram and Photoplethysmogram. IEEE Trans Biomed Eng. 2020;67(3):893-904. doi:10.1109/TBME.2019.2923448. PMID:31217092.
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.
Iqbal T, Elahi A, Ganly S, Wijns W, Shahzad A. Photoplethysmography-Based Respiratory Rate Estimation Algorithm for Health Monitoring Applications. J Med Biol Eng. 2022;42(2):242-252. doi:10.1007/s40846-022-00700-z. PMID:35535218.
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.
Kim H, Kim JY, Im CH. Fast and Robust Real-Time Estimation of Respiratory Rate from Photoplethysmography. Sensors (Basel). 2016;16(9):1494. doi:10.3390/s16091494. PMID:27649182.
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.
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:29795007.
Useful open-access time-frequency/Bayesian tracking paper. It broadens the algorithmic menu beyond RIAV/RIIV/RIFV and standard spectral peaks.
Quality and robustness
16Machine learning, signal-quality indices, and staying honest with noisy data.
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:33831075.
Strong bridge between classical modulation extraction and neural networks. Emphasizes respiratory signal quality quantification before feeding information into models.
Lei R, Ling BW, Feng P, Chen J. Estimation of Heart Rate and Respiratory Rate from PPG Signal Using Complementary Ensemble Empirical Mode Decomposition with both Independent Component Analysis and Non-Negative Matrix Factorization. Sensors (Basel). 2020;20(11):3238. doi:10.3390/s20113238. PMID:32517226.
Uses decomposition and source-separation style processing to recover heart and respiratory rates. Useful for comparing EMD/ICA approaches with simpler bandpass methods.
Chowdhury MH, Shuzan MNI, Chowdhury MEH, Reaz MBI, Mahmud S, Al Emadi N, Ayari MA, Ali SHM, Bakar AAA, Rahman SM, Khandakar A. 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. PMID:36290527.
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.
Hwang CS, Kim YH, Hyun JK, Kim JH, Lee SR, Kim CM, Nam JW, Kim EY. Evaluation of the Photoplethysmogram-Based Deep Learning Model for Continuous Respiratory Rate Estimation in Surgical Intensive Care Unit. Bioengineering (Basel). 2023;10(10):1222. doi:10.3390/bioengineering10101222. PMID:37892952.
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.
Shuzan MNI, Chowdhury MH, Chowdhury MEH, Murugappan M, Hoque Bhuiyan E, Arslane Ayari M, 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.
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.
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.
Corrected title. Recent open-access paper explicitly focused on RR from PPG; useful contemporary comparator and bridge to older RIAV/RIIV/RIFV methods.
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:38931763.
Relevant for low-power and embedded ambitions. Spiking neural networks may eventually map well to efficient hardware, even if not immediately Arduino-friendly.
Argüello-Prada EJ, Marcillo Ibarra KD, Díaz Jiménez KL. The use of successive systolic differences in photoplethysmographic (PPG) signals for respiratory rate estimation. Heliyon. 2024;10(4):e26036. doi:10.1016/j.heliyon.2024.e26036. PMID:38370197.
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.
Ponsiglione AM, Russo M, Petrellese MG, Letizia A, Tufano V, Ricciardi C, Tedesco A, Amato F, Romano M. Comparison of Techniques for Respiratory Rate Extraction from Electrocardiogram and Photoplethysmogram. Sensors (Basel). 2025;25(16):5136. doi:10.3390/s25165136. PMID:40871998.
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.
Jarchi D, Charlton P, Pimentel M, Casson A, Tarassenko L, Clifton DA. Estimation of respiratory rate from motion contaminated photoplethysmography signals incorporating accelerometry. Healthc Technol Lett. 2019;6(1):19-26. doi:10.1049/htl.2018.5019. PMID:30881695.
Corrected full title. Motion-contaminated PPG is the normal wearable case; supports adding accelerometers and explicit motion labels to the platform.
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. PMID:32182772.
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.
Elgendi M. Optimal Signal Quality Index for Photoplethysmogram Signals. Bioengineering (Basel). 2016;3(4):21. doi:10.3390/bioengineering3040021. PMID:28952584.
Key open paper on PPG signal-quality indices. Respiratory extraction is only as reliable as the source waveform; supports quality gating before RR estimation.
Sabeti E, Reamaroon N, Mathis M, Gryak J, Sjoding M, Najarian K. Signal quality measure for pulsatile physiological signals using morphological features: Applications in reliability measure for pulse oximetry. Inform Med Unlocked. 2019;16:100222. doi:10.1016/j.imu.2019.100222. PMID:32864419.
Corrected title and application phrase. Proposes morphological features for signal quality and reliability, which can support exclusion/down-weighting of unreliable PPG windows.
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 J Biomed Health Inform. 2015;19(3):832-8. doi:10.1109/JBHI.2014.2338351. PMID:25069129.
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.
van Gastel M, Stuijk S, de Haan G. Robust respiration detection from remote photoplethysmography. Biomed Opt Express. 2016;7(12):4941-4957. doi:10.1364/BOE.7.004941. PMID:28018717.
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.
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:30736395.
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.
Keep reading, building, and questioning
The bibliography is a map, not a product specification. The most useful next step is a small experiment with raw data, explicit metadata, a signal-quality check, and a result another person can reproduce or challenge.
About full text and reuse: This page links to papers; it does not republish them. Copyright and license terms remain with each publisher or author. A PMC link can be free to read without granting redistribution rights. License badges report the PMC record checked on 28 July 2026.
Annotated bibliography compiled by D. John Doyle for World Famous Electronics. H2 citation metadata and links were normalized with DOI and NCBI records, with AI assistance, for creative, educational, and experimental use. PulseSensor is not a medical device and is not for diagnosis, treatment, patient monitoring, emergency use, or safety-critical use. ♥