Literature DB >> 25118665

Electrocardiogram derived respiratory rate from QRS slopes and R-wave angle.

Jesús Lázaro1, Alejandro Alcaine, Daniel Romero, Eduardo Gil, Pablo Laguna, Esther Pueyo, Raquel Bailón.   

Abstract

A method for estimating respiratory rate from electrocardiogram (ECG) signals is presented. It is based on QRS slopes and R-wave angle, which reflect respiration-induced beat morphology variations. The 12 standard leads, 3 leads from vectorcardiogram (VCG), and 2 additional non-standard leads derived from VCG loops were analyzed. The following series were studied as ECG derived respiration (EDR) signals: slope between the peak of Q and R waves, slope between the peak of R and S waves, and the R-wave angle. Information from several EDR signals was combined in order to increase the robustness of estimation. Evaluation is performed over two databases containing ECG and respiratory signals simultaneously recorded during two clinical tests with different characteristics: tilt test, representing abrupt cardiovascular changes, and stress test representing a highly non-stationary and noisy environment. A combination of QRS slopes and R-wave angle series derived from VCG leads obtained a respiratory rate estimation relative error of 0.50 ± 4.11% (measuring 99.84% of the time) for tilt test and 0.52 ± 8.99% (measuring 96.09% of the time) for stress test. These results outperform those obtained by other reported methods, both in tilt and stress testing.

Entities:  

Mesh:

Year:  2014        PMID: 25118665     DOI: 10.1007/s10439-014-1073-x

Source DB:  PubMed          Journal:  Ann Biomed Eng        ISSN: 0090-6964            Impact factor:   3.934


  12 in total

1.  Reproducibility of Heart Rate Variability Characteristics Measured on Random 10-second ECG using Joint Symbolic Dynamics.

Authors:  Muammar M Kabir; Golriz Sedaghat; Jason Thomas; Larisa G Tereshchenko
Journal:  Comput Cardiol (2010)       Date:  2017-03-02

2.  Respiratory Rate Estimation Using U-Net-Based Cascaded Framework From Electrocardiogram and Seismocardiogram Signals.

Authors:  Michael Chan; Venu G Ganti; Omer T Inan
Journal:  IEEE J Biomed Health Inform       Date:  2022-06-03       Impact factor: 7.021

3.  Assessment of Joint Interactions between Respiration and Baroreflex Activity using Joint Symbolic Dynamics in Heart Failure Patients.

Authors:  Muammar M Kabir; Elyar Ghafoori; Larisa G Tereshchenko
Journal:  Comput Cardiol (2010)       Date:  2015-09

4.  Beat-to-beat spatiotemporal variability in the T vector is associated with sudden cardiac death in participants without left ventricular hypertrophy: the Atherosclerosis Risk in Communities (ARIC) Study.

Authors:  Jonathan W Waks; Elsayed Z Soliman; Charles A Henrikson; Nona Sotoodehnia; Lichy Han; Sunil K Agarwal; Dan E Arking; David S Siscovick; Scott D Solomon; Wendy S Post; Mark E Josephson; Josef Coresh; Larisa G Tereshchenko
Journal:  J Am Heart Assoc       Date:  2015-01-19       Impact factor: 5.501

Review 5.  Guidelines for Reporting Articles on Psychiatry and Heart rate variability (GRAPH): recommendations to advance research communication.

Authors:  D S Quintana; G A Alvares; J A J Heathers
Journal:  Transl Psychiatry       Date:  2016-05-10       Impact factor: 6.222

6.  The Ultrasonic Directional Tidal Breathing Pattern Sensor: Equitable Design Realization Based on Phase Information.

Authors:  Arijit Sinharay; Raj Rakshit; Anwesha Khasnobish; Tapas Chakravarty; Deb Ghosh; Arpan Pal
Journal:  Sensors (Basel)       Date:  2017-08-11       Impact factor: 3.576

7.  Nano-copper enhanced flexible device for simultaneous measurement of human respiratory and electro-cardiac activities.

Authors:  Li Wang; Feng Zhang; Kechao Lu; Mohammed Abdulaziz; Chao Li; Chongyu Zhang; Jun Chen; Yunlun Li
Journal:  J Nanobiotechnology       Date:  2020-05-29       Impact factor: 10.435

8.  Detection of Abnormal Respiration from Multiple-Input Respiratory Signals.

Authors:  Ju O Kim; Deokwoo Lee
Journal:  Sensors (Basel)       Date:  2020-05-24       Impact factor: 3.576

Review 9.  Breathing Rate Estimation From the Electrocardiogram and Photoplethysmogram: A Review.

Authors:  Peter H Charlton; Drew A Birrenkott; Timothy Bonnici; Marco A F Pimentel; Alistair E W Johnson; Jordi Alastruey; Lionel Tarassenko; Peter J Watkinson; Richard Beale; David A Clifton
Journal:  IEEE Rev Biomed Eng       Date:  2017-10-24

10.  Contribution of Different Subbands of ECG in Sleep Apnea Detection Evaluated Using Filter Bank Decomposition and a Convolutional Neural Network.

Authors:  Cheng-Yu Yeh; Hung-Yu Chang; Jiy-Yao Hu; Chun-Cheng Lin
Journal:  Sensors (Basel)       Date:  2022-01-10       Impact factor: 3.576

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.