Literature DB >> 28296645

Extraction of respiratory signals from the electrocardiogram and photoplethysmogram: technical and physiological determinants.

Peter H Charlton1, Timothy Bonnici, Lionel Tarassenko, Jordi Alastruey, David A Clifton, Richard Beale, Peter J Watkinson.   

Abstract

OBJECTIVE: Breathing rate (BR) can be estimated by extracting respiratory signals from the electrocardiogram (ECG) or photoplethysmogram (PPG). The extracted respiratory signals may be influenced by several technical and physiological factors. In this study, our aim was to determine how technical and physiological factors influence the quality of respiratory signals. APPROACH: Using a variety of techniques 15 respiratory signals were extracted from the ECG, and 11 from PPG signals collected from 57 healthy subjects. The quality of each respiratory signal was assessed by calculating its correlation with a reference oral-nasal pressure respiratory signal using Pearson's correlation coefficient. MAIN
RESULTS: Relevant results informing device design and clinical application were obtained. The results informing device design were: (i) seven out of 11 respiratory signals were of higher quality when extracted from finger PPG compared to ear PPG; (ii) laboratory equipment did not provide higher quality of respiratory signals than a clinical monitor; (iii) the ECG provided higher quality respiratory signals than the PPG; (iv) during downsampling of the ECG and PPG significant reductions in quality were first observed at sampling frequencies of  <250 Hz and  <16 Hz respectively. The results informing clinical application were: (i) frequency modulation-based respiratory signals were generally of lower quality in elderly subjects compared to young subjects; (ii) the qualities of 23 out of 26 respiratory signals were reduced at elevated BRs; (iii) there were no differences associated with gender. SIGNIFICANCE: Recommendations based on the results are provided regarding device designs for BR estimation, and clinical applications. The dataset and code used in this study are publicly available.

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Year:  2017        PMID: 28296645     DOI: 10.1088/1361-6579/aa670e

Source DB:  PubMed          Journal:  Physiol Meas        ISSN: 0967-3334            Impact factor:   2.833


  20 in total

1.  Wearable Photoplethysmography for Cardiovascular Monitoring.

Authors:  Peter H Charlton; Panicos A Kyriaco; Jonathan Mant; Vaidotas Marozas; Phil Chowienczyk; Jordi Alastruey
Journal:  Proc IEEE Inst Electr Electron Eng       Date:  2022-03-11       Impact factor: 10.961

2.  Current and Potential Applications of Wearables in Sports Cardiology.

Authors:  Prashant Rao; Dhruv R Seshadri; Jeffrey J Hsu
Journal:  Curr Treat Options Cardiovasc Med       Date:  2021-10-14

3.  Smart Multimodal Telehealth-IoT System for COVID-19 Patients.

Authors:  Lloyd E Emokpae; Roland N Emokpae; Wassila Lalouani; Mohamed Younis
Journal:  IEEE Pervasive Comput       Date:  2021-04-13       Impact factor: 1.603

4.  A comparative study of photoplethysmogram and piezoelectric plethysmogram signals.

Authors:  Qasem Qananwah; Ahmad Dagamseh; Hiam Alquran; Khalid Shaker Ibrahim; Moh'd Alodat; Oliver Hayden
Journal:  Phys Eng Sci Med       Date:  2020-08-31

5.  Estimating Heart Rate and Respiratory Rate from a Single Lead Electrocardiogram Using Ensemble Empirical Mode Decomposition and Spectral Data Fusion.

Authors:  Iau-Quen Chung; Jen-Te Yu; Wei-Chi Hu
Journal:  Sensors (Basel)       Date:  2021-02-08       Impact factor: 3.576

6.  Assessing the Quality of Heart Rate Variability Estimated from Wrist and Finger PPG: A Novel Approach Based on Cross-Mapping Method.

Authors:  Mimma Nardelli; Nicola Vanello; Guenda Galperti; Alberto Greco; Enzo Pasquale Scilingo
Journal:  Sensors (Basel)       Date:  2020-06-02       Impact factor: 3.576

7.  Design Rationale and Performance Evaluation of the Wavelet Health Wristband: Benchtop Validation of a Wrist-Worn Physiological Signal Recorder.

Authors:  Onur Dur; Reinier van Mourik; Colleen Rhoades; Man Suen Ng; Ragwa Elsayed; Maulik D Majmudar
Journal:  JMIR Mhealth Uhealth       Date:  2018-10-16       Impact factor: 4.773

8.  Ultrasound Sensors for Diaphragm Motion Tracking: An Application in Non-Invasive Respiratory Monitoring.

Authors:  Amirhossein Shahshahani; Carl Laverdiere; Sharmistha Bhadra; Zeljko Zilic
Journal:  Sensors (Basel)       Date:  2018-08-09       Impact factor: 3.576

9.  MEMS-Based Sensor for Simultaneous Measurement of Pulse Wave and Respiration Rate.

Authors:  Thanh-Vinh Nguyen; Masaaki Ichiki
Journal:  Sensors (Basel)       Date:  2019-11-13       Impact factor: 3.576

10.  Modeling arterial pulse waves in healthy aging: a database for in silico evaluation of hemodynamics and pulse wave indexes.

Authors:  Peter H Charlton; Jorge Mariscal Harana; Samuel Vennin; Ye Li; Phil Chowienczyk; Jordi Alastruey
Journal:  Am J Physiol Heart Circ Physiol       Date:  2019-08-23       Impact factor: 4.733

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