R.A. Bates

Evaluation of frequency and time-frequency spectral analysis of heart rate variability as a diagnostic marker of the sleep apnoea syndrome

The sleep apnoea/hypopnoea syndrome (SAHS) elicits a unique heart rate rhythm that may provide the basis for an effective screening tool. The study uses the receiver operator characteristic (ROC) to assess the diagnostic potential of spectral analysis of heart rate variability (HRV) using two methods, the discrete Fourier transform (DFT) and the discrete harmonic wavelet transform (DHWT). These two methods are compared over different sleep stages and spectral frequency bands. The HRV results are subsequently compared with those of the current screening method of oximetry. For both the DFT and the DHWT, the most diagnostically accurate frequency range for HRV spectral power calculations is found to be 0.019–0.036 Hz (denoted by AB2). Using AB2, 15 min sections of non-REM sleep data in 40 subjects produce ROC areas, for the DFT, DHWT and oximetry, of 0.94, 0.97 and 0.67, respectively. In REM sleep, ROC areas are 0.78, 0.79 and 0.71, respectively. In non-REM sleep, spectral analysis of HRV appears to be a significantly better indicator of the SAHS than the current screening method of oximetry, and, in REM sleep, it is comparable with oximetry. The advantage of the DHWT over the DFT is that it produces a greater time resolution and is computationally more efficient. The DHWT does not require the precondition of stationarity or interpolation of raw HRV data.

A Comparison of Six Deconvolution Techniques

We present results for the comparison of six deconvolution techniques. The methods we consider are based on Fourier transforms, system identification, constrained optimization, the use of cubic spline basis functions, maximum entropy, and a genetic algorithm. We compare the performance of these techniques by applying them to simulated noisy data, in order to extract an input function when the unit impulse response is known. The simulated data are generated by convolving the known impulse response with each of five different input functions, and then adding noise of constant coefficient of variation. Each algorithm was tested on 500 data sets, and we define error measures in order to compare the performance of the different methods.

Screening for obstructive sleep apnoea based on the electrocardiogram-the computers in cardiology challenge

The authors present a method of screening for obstructive sleep apnoea based on the electrocardiogram (ECG). The algorithm combines information from the ECG-derived respiration (EDR) signal and the RR interval tachogram. Power spectral features from the EDR signal were computed using the discrete harmonic wavelet transform, considering the power at the respiratory frequency and at frequencies below 0.1 Hz. Cycles of tachy/bradycardia (consistent with an arousal from sleep, as would be expected at the end of an episode of apnoea) were identified front the RR interval tachogram. Features were collated into minute-by-minute vectors and passed to a classifier. The algorithm correctly classified 81% of all minutes in the test database, with 29/30 patients correctly identified as apnoea or normal. Visual classification produced 92% correct classification, with all 30 patients correct.

Autonomic function assessment using analysis of heart rate variability

Abstract Analysis of changes in heart rate can be useful in determining the state of various body systems. In particular the analysis of heart rate variability (HRV) is used in the assessment of autonomic function. This paper uses the discrete harmonic wavelet transform for a time-frequency analysis of HRV data to show changes in spectral power over time. Signals representing patient heart rate are presented, and methods for spectral and time-frequency analysis are described. Three sets of patient data are then analysed using these methods. The results show the potential of time-frequency analysis in the assessment of medical disorders, such as the sleep apnoea syndrome, where transient alterations in autonomic function occur.

The Catastrophe of Measurement Error in Heart Rate Variability

Abstract Guidelines for the spectral analysis of electrocardiographic R-R intervals have recently been published. However, little infomation is given on the effect of the electrocardiogram sampling frequency with respect to spectral analysis results. Developing modelled R-R data, using the Integral Pulse Frequency Modulated model at differing sampling frequencies, we have produced a methodology for determining if a recorded R-R signal has a high enough signal to noise ratio to perform accurate spectral analysis

A new application for heart rate variability: diagnosing the sleep apnoea syndrome

The unique heart rate rhythm in the sleep apnoea/hypopnoea syndrome (SAHS) may provide the basis for an effective screening tool. This paper assesses the variability (HRV), using the Discrete Fourier Transform (DHWT). Spectral HRV results are compared to those of the current screening method of oximetry. Diagnostic indicator of the SAHS in both NREM and REM sleep. The DHWT offers advantages over the DFT in that it supplies greater time resolution and is computationally more efficient.

Heart rate variability: measurement error or chaos

Guidelines published for the spectral analysis of heart rate variability (HRV) offer little insight into the handling of a fundamental error source, namely sampling frequency (f/sub s/) measurement error. Using artificial heart rate data we systematically examined the effect that f/sub s/ and modulating amplitudes have on the power spectrum of HRV data. The derived topological information forms the basis for developing criteria which determine whether a recorded R-R signal has a high enough signal to noise ratio to perform spectral analysis within a confidence interval of /spl plusmn/15%. These criteria are tested using R-R interval data obtained with f/sub s/ values ranging between 31.25-500 Hz in four patients with differing HRV profiles. This paper presents the results of both model simulations and actual data, showing how the effect of f/sub s/ measurement error can be described in terms of nonlinear dynamics.

Modelling the Nonlinear Pharmacokinetics of Tissue-Type Plasminogen Activator in Three Animal Species

Abstract This study is concerned with modelling plasma concentrations of tissuetype plasminogen activator (t-PA) in three different animal species and at several different dose levels. Several cornpartrnental model structures are examined, and it is found that a model with parallel linear and nonlinear (Michaelis Menten) elimination from the central compartment provides the best fit to the data. For two of the animal species (rats and rabbits), a three-compartment model gives significantly better fits than a two-compartment one, while for the third species (marmosets), the difference is not statistically significant.

Autonomic Function Assessment using Analysis of Heart Rate Variability

Abstract Analysis of changes in heart rate can be useful in determining the state of various body systems. In particular the analysis of heart rate variability (HRV) is used in the assessment of autonomic function. We present a method of time-frequency analysis of HRV data using the discrete harmonic wavelet transform to show changes in spectral power resolution over time. Signals representing patient heart rate are presented and methods for spectral and time-frequency analysis are described. Three sets of patient data are then analysed using these methods. The results show the potential of time-frequency analysis in the assessment of medical disorders, such as the sleep apnoea syndrome, where transient alterations in autonomic function occur.