Breathing rate estimation from a single-lead electrocardiogram acquisition system

Nazrul Anuar Nayan, Nur Sabrina Risman, Rosmina Jaafar

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Breathing rate (BR) is highly predictive of health deterioration among vital signs measured in acutely ill hospital patients. This study proposes a single-lead electrocardiogram (ECG) acquisition system and a BR estimation algorithm from ECG. The signal quality index algorithm was validated quantitatively by using the PhysioNet/ Computing in Cardiology Challenge 2011 training data set. The BR extraction algorithm was validated using 40 MIT Physionet Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II) data set. The estimated BR showed a mean absolute error (MAE) of 1.4 compared with the reference BR. Using the proposed acquisition system, 20 ECGs of healthy subjects were recorded, and the estimated BR with MAE of 0.7 bpm was obtained. Results indicate that the proposed hardware and algorithm could replace the manually counted, uncomfortable nasal air flow sensor or chest band often used in hospitals.

Original languageEnglish
Pages (from-to)38154-38158
Number of pages5
JournalInternational Journal of Applied Engineering Research
Volume10
Issue number17
Publication statusPublished - 2015

Fingerprint

Electrocardiography
Lead
Cardiology
Deterioration
Health
Hardware
Monitoring
Sensors
Air

Keywords

  • Algorithm
  • Breathing rate
  • Critical Illness
  • e-Health sensor system
  • Single-lead ECG

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Breathing rate estimation from a single-lead electrocardiogram acquisition system. / Nayan, Nazrul Anuar; Risman, Nur Sabrina; Jaafar, Rosmina.

In: International Journal of Applied Engineering Research, Vol. 10, No. 17, 2015, p. 38154-38158.

Research output: Contribution to journalArticle

@article{6d11568faa1d475e86cf73d2a7829d79,
title = "Breathing rate estimation from a single-lead electrocardiogram acquisition system",
abstract = "Breathing rate (BR) is highly predictive of health deterioration among vital signs measured in acutely ill hospital patients. This study proposes a single-lead electrocardiogram (ECG) acquisition system and a BR estimation algorithm from ECG. The signal quality index algorithm was validated quantitatively by using the PhysioNet/ Computing in Cardiology Challenge 2011 training data set. The BR extraction algorithm was validated using 40 MIT Physionet Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II) data set. The estimated BR showed a mean absolute error (MAE) of 1.4 compared with the reference BR. Using the proposed acquisition system, 20 ECGs of healthy subjects were recorded, and the estimated BR with MAE of 0.7 bpm was obtained. Results indicate that the proposed hardware and algorithm could replace the manually counted, uncomfortable nasal air flow sensor or chest band often used in hospitals.",
keywords = "Algorithm, Breathing rate, Critical Illness, e-Health sensor system, Single-lead ECG",
author = "Nayan, {Nazrul Anuar} and Risman, {Nur Sabrina} and Rosmina Jaafar",
year = "2015",
language = "English",
volume = "10",
pages = "38154--38158",
journal = "International Journal of Applied Engineering Research",
issn = "0973-4562",
publisher = "Research India Publications",
number = "17",

}

TY - JOUR

T1 - Breathing rate estimation from a single-lead electrocardiogram acquisition system

AU - Nayan, Nazrul Anuar

AU - Risman, Nur Sabrina

AU - Jaafar, Rosmina

PY - 2015

Y1 - 2015

N2 - Breathing rate (BR) is highly predictive of health deterioration among vital signs measured in acutely ill hospital patients. This study proposes a single-lead electrocardiogram (ECG) acquisition system and a BR estimation algorithm from ECG. The signal quality index algorithm was validated quantitatively by using the PhysioNet/ Computing in Cardiology Challenge 2011 training data set. The BR extraction algorithm was validated using 40 MIT Physionet Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II) data set. The estimated BR showed a mean absolute error (MAE) of 1.4 compared with the reference BR. Using the proposed acquisition system, 20 ECGs of healthy subjects were recorded, and the estimated BR with MAE of 0.7 bpm was obtained. Results indicate that the proposed hardware and algorithm could replace the manually counted, uncomfortable nasal air flow sensor or chest band often used in hospitals.

AB - Breathing rate (BR) is highly predictive of health deterioration among vital signs measured in acutely ill hospital patients. This study proposes a single-lead electrocardiogram (ECG) acquisition system and a BR estimation algorithm from ECG. The signal quality index algorithm was validated quantitatively by using the PhysioNet/ Computing in Cardiology Challenge 2011 training data set. The BR extraction algorithm was validated using 40 MIT Physionet Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II) data set. The estimated BR showed a mean absolute error (MAE) of 1.4 compared with the reference BR. Using the proposed acquisition system, 20 ECGs of healthy subjects were recorded, and the estimated BR with MAE of 0.7 bpm was obtained. Results indicate that the proposed hardware and algorithm could replace the manually counted, uncomfortable nasal air flow sensor or chest band often used in hospitals.

KW - Algorithm

KW - Breathing rate

KW - Critical Illness

KW - e-Health sensor system

KW - Single-lead ECG

UR - http://www.scopus.com/inward/record.url?scp=84942756608&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84942756608&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:84942756608

VL - 10

SP - 38154

EP - 38158

JO - International Journal of Applied Engineering Research

JF - International Journal of Applied Engineering Research

SN - 0973-4562

IS - 17

ER -