Influence of recognition errors of computerised analysis of 24-hour electrocardiograms on the measurement of spectral components of heart rate variability.
Spectral methods for the assessment of heart rate variability (HRV) in 24-h electrocardiograms (ECG) are believed to require visual verification and manual editing of the computerised recognition of the ECG. This study investigated the effect of the recognition errors of computerised ECG recognition on two methods providing spectral HRV indices: (a) Fast Fourier Transformation (FFT); and (b) peak-to-trough analysis (PTA). Both methods were used to measure HRV spectra in 24-h ECGs recorded in 557 survivors of acute myocardial infarction. Each ECG was analysed using the Marquette 8000 Holter system and spectral HRV analyses were performed both prior to and after manual verification of the automatic ECG analysis. The FFT and PTA methods were used to calculate the low (0.04-0.15 Hz), medium (0.15-0.40 Hz) and high (0.40-1.00 Hz) HRV spectral components. For each method and for each spectral component, the rank correlations between the results obtained from unedited and edited ECG recognition were calculated. The correlations between the corresponding spectral components provided by the FFT and PTA methods applied to the edited recognitions were also calculated. Both methods were substantially affected by recognition errors. The FFT method was more sensitive to the misrecognition than the PTA method. The inter-method correlations were higher for the high and medium spectral components than for the low spectral component. The study suggests that spectral HRV analysis should be performed only on carefully verified and manually corrected recognitions of long-term electrocardiograms.
Xia
R
R
Department of Cardiological Sciences, St. George's Hospital Medical School, London.
Odemuyiwa
O
O
Gill
J
J
Malik
M
M
Camm
A J
AJ
D000328:Adult; D005117:Cardiac Complexes, Premature / Q000503:physiopathology; D016757:Death, Sudden, Cardiac; D015716:Electrocardiography, Ambulatory / Q000145:classification* / Q000706:statistics & numerical data; D005583:Fourier Analysis; D006339:Heart Rate / Q000502:physiology*; D006801:Humans; D009203:Myocardial Infarction / Q000503:physiopathology; D010363:Pattern Recognition, Automated; D012307:Risk Factors; D012815:Signal Processing, Computer-Assisted