ArticleSensors (Basel, Switzerland)2022
Wrist Photoplethysmography Signal Quality Assessment for Reliable Heart Rate Estimate and Morphological Analysis.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 46 citations in OpenAlex.
- Real-Time SpOSensors (Basel, Switzerland) · 2026Article
- A Comparative Study of Signal Representations Methods and Deep Learning Architectures for PPG-Based Cuffless Blood Pressure Estimation.Sensors (Basel, Switzerland) · 2026Article
- Signal Quality Assessment and Reconstruction of PPG-Derived Signals for Heart Rate and Variability Estimation in In-Vehicle Applications: A Comparative Review and Empirical Validation.Sensors (Basel, Switzerland) · 2025Review
- A lightGBM-based method for the signal quality assessment of wrist photoplethysmography.Physical and engineering sciences in medicine · 2025Article
- Determinants of photoplethysmography signal quality at the wrist.PLOS digital health · 2025Article
- Detecting cardiac states with wearable photoplethysmograms and implications for out-of-hospital cardiac arrest detection.Scientific reports · 2024Article
- Signal Quality in Continuous Transcutaneous Bilirubinometry.Sensors (Basel, Switzerland) · 2024Article
- Benchmarking of Sensor Configurations and Measurement Sites for Out-of-the-Lab Photoplethysmography.Sensors (Basel, Switzerland) · 2023Article
- A Novel Signal Restoration Method of Noisy Photoplethysmograms for Uninterrupted Health Monitoring.Sensors (Basel, Switzerland) · 2023Article
- BCG Signal Quality Assessment Based on Time-Series Imaging Methods.Sensors (Basel, Switzerland) · 2023Article
- Feasibility interventional study investigating PAIN in neurorehabilitation through wearabLE SensorS (PAINLESS): a study protocol.BMJ open · 2023Article
- PhysioKit: An Open-Source, Low-Cost Physiological Computing Toolkit for Single- and Multi-User Studies.Sensors (Basel, Switzerland) · 2023Article
- Automated Multi-Wavelength Quality Assessment of Photoplethysmography Signals Using Modulation Spectrum Shape Features.Sensors (Basel, Switzerland) · 2023Article
- Motion Artifacts Reduction for Noninvasive Hemodynamic Monitoring of Conscious Patients Using Electrical Impedance Tomography: A Preliminary Study.Sensors (Basel, Switzerland) · 2023Article
- Photoplethysmography Driven Hypertension Identification: A Pilot Study.Sensors (Basel, Switzerland) · 2023Article
- Review
- Upper-Arm Photoplethysmographic Sensor with One-Time Calibration for Long-Term Blood Pressure Monitoring.Biosensors · 2023Article
- Artificial Intelligence for Automatic Pain Assessment: Research Methods and Perspectives.Pain research & management · 2023Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Photoplethysmographic (PPG) signals are mainly employed for heart rate estimation but are also fascinating candidates in the search for cardiovascular biomarkers. However, their high susceptibility to motion artifacts can lower their morphological quality and, hence, affect the reliability of the extracted information. Low reliability is particularly relevant when signals are recorded in a real-world context, during daily life activities. We aim to develop two classifiers to identify PPG pulses suitable for heart rate estimation (Basic-quality classifier) and morphological analysis (High-quality classifier). We collected wrist PPG data from 31 participants over a 24 h period. We defined four activity ranges based on accelerometer data and randomly selected an equal number of PPG pulses from each range to train and test the classifiers. Independent raters labeled the pulses into three quality levels. Nineteen features, including nine novel features, were extracted from PPG pulses and accelerometer signals. We conducted ten-fold cross-validation on the training set (70%) to optimize hyperparameters of five machine learning algorithms and a neural network, and the remaining 30% was used to test the algorithms. Performances were evaluated using the full features and a reduced set, obtained downstream of feature selection methods. Best performances for both Basic- and High-quality classifiers were achieved using a Support Vector Machine (Acc: 0.96 and 0.97, respectively). Both classifiers outperformed comparable state-of-the-art classifiers. Implementing automatic signal quality assessment methods is essential to improve the reliability of PPG parameters and broaden their applicability in a real-world context.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.