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Abstract

A dynamical model based on three coupled ordinary differential equations is introduced which is capable of generating realistic synthetic electrocardiogram (ECG) signals. The operator can specify the mean and standard deviation of the heart rate, the morphology of the PQRST cycle, and the power spectrum of the RR tachogram. In particular, both respiratory sinus arrhythmia at the high frequencies (HFs) and Mayer waves at the low frequencies (LFs) together with the LF/HF ratio are incorporated in the model. Much of the beat-to-beat variation in morphology and timing of the human ECG, including QT dispersion and R-peak amplitude modulation are shown to result. This model may be employed to assess biomedical signal processing techniques which are used to compute clinical statistics from the ECG.

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QT interval prolongation as predictor of sudden death in patients with myocardial infarction.

Michael Wolf, S Wolf (1978)

Fifty-five patients with recent myocardial infarction and 55 healthy controls, matched for age, sex, race, height, weight, education and job, had an electrocardiogram taken every two months for seven years. Twenty-eight patients and one control had a sudden cardiac death. The QTc (mean of all values recorded) was found prolonged in one control (2%), five of 27 surviving patients (18%) and in 16 of 28 patients who had sudden death (57%). The difference between surviving and sudden death patients is significant (P less than 0.01). It is interesting that the only control with a long QT was the one when died suddenly of myocardial infarction. Among patients with previous myocardial infarction a prolonged QTc constitutes a 2.16 times greater risk for sudden death. We conclude that a constant prolongation of QTc in patients with myocardial infarction may help, with other risk factors, in defining a subgroup at higher risk for sudden death.

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Power spectral density of unevenly sampled data by least-square analysis: performance and application to heart rate signals.

P. Laguna, G.B. Moody, R.G. Mark (1998)

This work studies the frequency behavior of a least-square method to estimate the power spectral density of unevenly sampled signals. When the uneven sampling can be modeled as uniform sampling plus a stationary random deviation, this spectrum results in a periodic repetition of the original continuous time spectrum at the mean Nyquist frequency, with a low-pass effect affecting upper frequency bands that depends on the sampling dispersion. If the dispersion is small compared with the mean sampling period, the estimation at the base band is unbiased with practically no dispersion. When uneven sampling is modeled by a deterministic sinusoidal variation respect to the uniform sampling the obtained results are in agreement with those obtained for small random deviation. This approximation is usually well satisfied in signals like heart rate (HR) series. The theoretically predicted performance has been tested and corroborated with simulated and real HR signals. The Lomb method has been compared with the classical power spectral density (PSD) estimators that include resampling to get uniform sampling. We have found that the Lomb method avoids the major problem of classical methods: the low-pass effect of the resampling. Also only frequencies up to the mean Nyquist frequency should be considered (lower than 0.5 Hz if the HR is lower than 60 bpm). We conclude that for PSD estimation of unevenly sampled signals the Lomb method is more suitable than fast Fourier transform or autoregressive estimate with linear or cubic interpolation. In extreme situations (low-HR or high-frequency components) the Lomb estimate still introduces high-frequency contamination that suggest further studies of superior performance interpolators. In the case of HR signals we have also marked the convenience of selecting a stationary heart rate period to carry out a heart rate variability analysis.