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.

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.

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.

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.

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.

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.

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.

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

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.

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.

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.

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.

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.

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.

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

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.

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.

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.

Methodological cornerstone comparing algorithms and emphasizing validation, data quality, and common evaluation methods. Important antidote to overfitting algorithms to one clean dataset.

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.

The best single review for breathing-rate estimation from ECG and PPG. Lays out preprocessing, respiratory-signal extraction, rate estimation, fusion, and quality assessment.

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.

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.

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.

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.

Central open-access paper for advanced respiratory extraction. Extracts RIIV, RIAV, and RIFV, estimates instantaneous RR from each, and fuses estimates.

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.

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.

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.

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.

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

Strong bridge between classical modulation extraction and neural networks. Emphasizes respiratory signal quality quantification before feeding information into models.

Uses decomposition and source-separation style processing to recover heart and respiratory rates. Useful for comparing EMD/ICA approaches with simpler bandpass methods.

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.

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.

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.

Corrected title. Recent open-access paper explicitly focused on RR from PPG; useful contemporary comparator and bridge to older RIAV/RIIV/RIFV methods.

Relevant for low-power and embedded ambitions. Spiking neural networks may eventually map well to efficient hardware, even if not immediately Arduino-friendly.

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.

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.

Corrected full title. Motion-contaminated PPG is the normal wearable case; supports adding accelerometers and explicit motion labels to the platform.

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.

Key open paper on PPG signal-quality indices. Respiratory extraction is only as reliable as the source waveform; supports quality gating before RR estimation.

Corrected title and application phrase. Proposes morphological features for signal quality and reliability, which can support exclusion/down-weighting of unreliable PPG windows.

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.

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.

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.