ArticleBiomedical signal processing and control2021
An impedance pneumography signal quality index: Design, assessment and application to respiratory rate monitoring.
Article in Biomedical signal processing and control, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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The trial behind it
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Who cites it
22 citing papers in PubMed.
- Respiratory rate measurement by pressure variation in the high flow nasal cannula-system in healthy volunteers.Journal of clinical monitoring and computing · 2024Trial
- MIMIC-III-Ext-PPG, a PPG-based Benchmark Dataset for Cardiovascular and Respiratory Signal Analysis.Scientific data · 2026Article
- Evaluation of physiology-based models for noninvasive blood pressure estimation using pulse arrival time.Scientific reports · 2026Article
- Monitoring Respiratory Health in Children With Acute Asthma Using Wearable Electrical Bioimpedance and Breath Sounds: Observational Case-Control Study.JMIR mHealth and uHealth · 2026Observational
- Radar-based inspiratory-to-expiratory time ratio estimation: a validation study.Scientific reports · 2026Article
- Cardiorespiratory phase synchronization maturational trajectory: biomarker of autonomic nervous system development in preterm infants.Pediatric research · 2026Article
- Comparison of Techniques for Respiratory Rate Extraction from Electrocardiogram and Photoplethysmogram.Sensors (Basel, Switzerland) · 2025Article
- Emerging Wearable Acoustic Sensing Technologies.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- A Novel Respiratory Rate Estimation Algorithm from Photoplethysmogram Using Deep Learning Model.Diagnostics (Basel, Switzerland) · 2024Article
- Video-Based Respiratory Rate Estimation for Infants in the NICU.IEEE journal of translational engineering in health and medicine · 2024Article
- Enabling Continuous Breathing-Phase Contextualization via Wearable-Based Impedance Pneumography and Lung Sounds: A Feasibility Study.IEEE journal of biomedical and health informatics · 2023Article
- Laser spectroscopic method for remote sensing of respiratory rate.Physical and engineering sciences in medicine · 2023Article
- Impedance-Based Ventilation Detection and Signal Quality Control During Out-of-Hospital Cardiopulmonary Resuscitation.IEEE journal of biomedical and health informatics · 2023Article
- PreEpiSeizures: description and outcomes of physiological data acquisition using wearable devices during video-EEG monitoring in people with epilepsy.Frontiers in physiology · 2023Article
- Towards Estimation of Tidal Volume and Respiratory Timings via Wearable-Patch-Based Impedance Pneumography in Ambulatory Settings.IEEE transactions on bio-medical engineering · 2022Article
- A Wearable Multimodal Sensing System for Tracking Changes in Pulmonary Fluid Status, Lung Sounds, and Respiratory Markers.Sensors (Basel, Switzerland) · 2022Article
- A Real-Time Respiration Monitoring and Classification System Using a Depth Camera and Radars.Frontiers in physiology · 2022Article
- Data Augmentation and Transfer Learning for Data Quality Assessment in Respiratory Monitoring.Frontiers in bioengineering and biotechnology · 2022Article
- Assessing Electrocardiogram and Respiratory Signal Quality of a Wearable Device (SensEcho): Semisupervised Machine Learning-Based Validation Study.JMIR mHealth and uHealth · 2021Article
- Wearable Bioimpedance Monitoring: Viewpoint for Application in Chronic Conditions.JMIR biomedical engineering · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Impedance pneumography (ImP) is widely used for respiratory rate (RR) monitoring. However, ImP-derived RRs can be imprecise. The aim of this study was to develop a signal quality index (SQI) for the ImP signal, and couple it with a RR algorithm, to improve RR monitoring. An SQI was designed which identifies candidate breaths and assesses signal quality using: the variation in detected breath durations, how well peaks and troughs are defined, and the similarity of breath morphologies. The SQI categorises 32 s signal segments as either high or low quality. Its performance was evaluated using two critical care datasets. RRs were estimated from high-quality segments using a RR algorithm, and compared with reference RRs derived from manual annotations. The SQI had a sensitivity of 77.7 %, and specificity of 82.3 %. RRs estimated from segments classified as high quality were accurate and precise, with mean absolute errors of 0.21 and 0.40 breaths per minute (bpm) on the two datasets. Clinical monitor RRs were significantly less precise. The SQI classified 34.9 % of real-world data as high quality. In conclusion, the proposed SQI accurately identifies high-quality segments, and RRs estimated from those segments are precise enough for clinical decision making. This SQI may improve RR monitoring in critical care. Further work should assess it with wearable sensor data.
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