Automatic detection of respiration rate from ambulatory single-lead ECG

Justin Boyle, Niranjan Bidargaddi, Antti Sarela, Mohan Karunanithi

Research output: Contribution to journalArticlepeer-review

76 Citations (Scopus)

Abstract

Ambulatory electrocardiography is increasingly being used in clinical practice to detect abnormal electrical behavior of the heart during ordin ry daily activities. The utility of this monitoring can be improved by deriving respiration, which previously has been based on overnight apnea studies where patients are stationary, or the use of multilead ECG systems for stress testing. We compared six respiratory measures derived from a single-lead portable ECG monitor with simultaneously measured respiration air flow obtained from an ambulatory nasal cannula respiratory monitor. Ten controlled 1-h recordings were performed covering activities of daily living (lying, sitting, standing, walking, jogging, running, and stair climbing) and six overnight studies. The best method was an average of a 0.20.8 Hz bandpass filter and RR technique based on lengthening and shortening of the RR interval. Mean error rates with the reference gold standard were $\pm$4 breaths per minute (bpm) (all activities), $\pm$2 bpm (lying and sitting), and $\pm$1 breath per minute (overnight studies). Statistically similar results were obtained using heart rate information alone (RR technique) compared to the best technique derived from the full ECG waveform that simplifies data collection procedures. The study shows that respiration can be derived under dynamic activities from a single-lead ECG without significant differences from traditional methods.

Original languageEnglish
Article number5256153
Pages (from-to)890-896
Number of pages7
JournalIEEE Transactions on Information Technology in Biomedicine
Volume13
Issue number6
DOIs
Publication statusPublished or Issued - Nov 2009
Externally publishedYes

Keywords

  • Cardiovascular system
  • Electrocardiography
  • Exercise
  • Respiratory system

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering

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