Principal Component Analysis as a Tool for Analyzing Beat-to-Beat Changes in ECG Features: Application to ECG-Derived Respiration

An algorithm for analyzing changes in ECG morphology based on principal component analysis (PCA) is presented and applied to the derivation of surrogate respiratory signals from single-lead ECGs. The respiratory-induced variability of ECG features, P waves, QRS complexes, and T waves are described b...

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Bibliographic Details
Published in:IEEE transactions on biomedical engineering Vol. 57; no. 4; pp. 821 - 829
Main Authors: Langley, Philip, Bowers, Emma J., Murray, Alan
Format: Journal Article
Language:English
Published: New York, NY IEEE 01.04.2010
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adult
Algorithm design and analysis
Algorithms
Atrial Premature Complexes - physiopathology
Biological and medical sciences
Councils
ECG-derived respiration (EDR)
Electrocardiography
Electrocardiography - methods
Electrocardiography. Vectocardiography
Electrodiagnosis. Electric activity recording
Female
Heart
Hospitals
Humans
Investigative techniques of respiratory function
Investigative techniques, diagnostic techniques (general aspects)
Male
Medical sciences
Middle Aged
Morphology
Patient monitoring
Physics
Principal Component Analysis
principal component analysis (PCA)
Principal components analysis
Respiration
Respiratory Rate - physiology
Signal analysis
Signal Processing, Computer-Assisted
Statistics, Nonparametric
Studies
Human
principal component analysis (PCA)
Electrophysiology
Exploration
Method
Algorithm
ECG-derived respiration (EDR)
Electrocardiography
Respiratory frequency
Signal processing
Physiology
Respiration
Comparative study
Principal component analysis
Biomedical engineering
ISSN:0018-9294, 1558-2531, 1558-2531
Online Access:Get full text
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Description
Summary:An algorithm for analyzing changes in ECG morphology based on principal component analysis (PCA) is presented and applied to the derivation of surrogate respiratory signals from single-lead ECGs. The respiratory-induced variability of ECG features, P waves, QRS complexes, and T waves are described by the PCA. We assessed which ECG features and which principal components yielded the best surrogate for the respiratory signal. Twenty subjects performed controlled breathing for 180 s at 4, 6, 8, 10, 12, and 14 breaths per minute and normal breathing. ECG and breathing signals were recorded. Respiration was derived from the ECG by three algorithms: the PCA-based algorithm and two established algorithms, based on RR intervals and QRS amplitudes. ECG-derived respiration was compared to the recorded breathing signal by magnitude squared coherence and cross-correlation. The top ranking algorithm for both coherence and correlation was the PCA algorithm applied to QRS complexes. Coherence and correlation were significantly larger for this algorithm than the RR algorithm(p < 0.05 and p < 0.0001, respectively) but were not significantly different from the amplitude algorithm. PCA provides a novel algorithm for analysis of both respiratory and nonrespiratory related beat-to-beat changes in different ECG features.
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ISSN:0018-9294
1558-2531
1558-2531
DOI:10.1109/TBME.2009.2018297