Detrended fluctuation analysis: a suitable method for studying fetal heart rate variability?

We evaluate the suitability of an enhanced detrended fluctuation analysis for studying fetal heart rate series involving imperfect quality of information. Our results indicate that to explore persistent long-range correlations, or fractality, the collection requirements of the data can be relaxed by allowing the possibility of using averaged fetal heart rate series. In addition, it also appears feasible to employ, without producing major alterations in the long-range scaling behaviour, fragmented fetal heart rate series involving up to 50% of random missing values, or up to 50 min of consecutive missing samples in recordings of approximately equal to 8 h length. These are crucial advantages to overcome the often variable quality of fetal data. Consequently, these findings may open the possibility of obtaining information concerning the development of neural processes from fetal heart rate series, despite their non-stationary and fragmented nature.

Application of region-based segmentation and neural network edge detection to skin lesions.

This paper proposes two approaches to the skin lesion image segmentation problem. The first is a mainly region-based segmentation method where an optimal threshold is determined iteratively by an isodata algorithm. The second method proposed is based on neural network edge detection and a rational Gaussian curve that fits an approximate closed elastic curve between the recognized neural network edge patterns. A quantitative comparison of the techniques is enabled by the use of synthetic lesions to which Gaussian noise is added. The proposed techniques are also compared with an established automatic skin segmentation method. It is demonstrated that for lesions with a range of different border irregularity properties the iterative thresholding method provides the best performance over a range of signal to noise ratios. Iterative thresholding technique is also demonstrated to have similar performance when tested on real skin lesions.

Interpretation of heart rate variability via detrended fluctuation analysis and alphabeta filter.

Detrended fluctuation analysis (DFA), suitable for the analysis of nonstationary time series, has confirmed the existence of persistent long-range correlations in healthy heart rate variability data. In this paper, we present the incorporation of the alphabeta filter to DFA to determine patterns in the power-law behavior that can be found in these correlations. Well-known simulated scenarios and real data involving normal and pathological circumstances were used to evaluate this process. The results presented here suggest the existence of evolving patterns, not always following a uniform power-law behavior, that cannot be described by scaling exponents estimated using a linear procedure over two predefined ranges. Instead, the power law is observed to have a continuous variation with segment length. We also show that the study of these patterns, avoiding initial assumptions about the nature of the data, may confer advantages to DFA by revealing more clearly abnormal physiological conditions detected in congestive heart failure patients related to the existence of dominant characteristic scales.

Application of empirical mode decomposition to heart rate variability analysis.

The analysis of heart rate variability, involving changes in the autonomic modulation conditions, demands specific capabilities not provided by either parametric or non-parametric spectral estimation methods. Moreover, these methods produce time-averaged power estimates over the entire length of the record. Recently, empirical mode decomposition and the associated Hilbert spectra have been proposed for non-linear and non-stationary time series. The application of these techniques to real and simulated short-term heart rate variability data under stationary and non-stationary conditions is presented. The results demonstrate the ability of empirical mode decomposition to isolate the two main components of one chirp series and three signals simulated by the integral pulse frequency modulation model, and consistently to isolate at least four main components localised in the autonomic bands of 14 real signals under controlled breathing manoeuvres. In addition, within the short time-frequency range that is recognised for heart rate variability phenomena, the Hilbert amplitude component ratio and the instantaneous frequency representation are assessed for their suitability and accuracy in time-tracking changes in amplitude and frequency in the presence of non-stationary and non-linear conditions. The frequency tracking error is found to be less than 0.22% for two simulated signals and one chirp series.

Detection of fetal electrocardiogram signals using matched filters with adaptive normalisation.

The authors discuss the application of matched litters to the detection of R-waves in fetal electrocardiogram (FECG) data, recorded during labour using a scalp electrode. When using the basic matched filter, one correlates a template representing the clean signal with the noisy signal. This method is optimal when the underlying noise is white in nature. However, it is known that false detection of R-waves can occur in the presence of extraneous peaks which have a similar shape to the fetal R-wave. It is proposed to switch between two different normalisations of the impulse response of the matched filter to alleviate this problem. When the signal-to-noise ratio is lower than a predetermined threshold, then normalisation to the geometric mean of the template and noisy data energies is carried out, otherwise only normalisation to the template energy is made. In the former case, the background noise and spikes that are larger than the underlying FECG are attenuated, hence increasing the probability of detection of the R-waves. In the latter case, noise, which has a lower amplitude than the underlying R-wave, is reduced. The effectiveness of this method is demonstrated by application to scalp electrode data.

Antenatal assessment using the FECG obtained via abdominal electrodes.

During the past decade a variety of intrapartum fetal monitors have been constructed that process the entire fetal electrocardiogram (FECG), obtained via a scalp electrode. They therefore differ from conventional monitors in aiming to extract relevant timing and magnitude information from the morphology of the FECG rather than simply the RR interval and hence heart rate. An intrapartum monitor such as this has been successfully developed by ourselves. This paper describes the early results obtained whilst attempting to extend this form of monitoring forward into the antenatal period. In order to achieve this the FECG must be acquired via surface electrodes placed on the maternal abdomen, which yields a signal containing the FECG amidst a number of noise sources. Our investigations into the feasibility of "antenatal abdominal FECG analysis" have been on two fronts. The first has been to produce a bedside monitor similar in function to our intrapartum device, whilst the second has been to address the possibility of performing such monitoring in ambulant subjects. At present the antenatal bedside monitor has successfully extracted and processed the FECG in approximately 75% of the cases studied, with subjects ranging from 20 weeks through to term having been monitored. We also have demonstrated the feasibility of the long term monitoring of maternal and fetal heart rate using a portable instrument.

Iterative method to derive an approximate matched filter template for fetal electrocardiogram signals.

Fetal electrocardiogram (FECG) signals that are extracted from the maternal abdomen have a signal-to-noise ratio that is so low that the determination of the times of location of the R-waves can be difficult. A matched filter could, in principle, be used, but in theory this requires prior knowledge of the shape of the QRS complex of the FECG. In the work that is described in this paper, a digital low-pass filter, with an impulse response that is triangular in shape, is applied to the first M complexes of a simulated FECG signal. An average based on the detected R-wave locations is determined and this is used as an approximation to the matched filter template for the next block of M complexes. It is shown that this method can be iterated to obtain an effectiveness in detecting R-wave locations that is competitive with the corresponding performance that is obtained with the pure matched filter. The resilience of this technique to increasing noise levels is investigated.

A windows application for real-time fetal ECG analysis.

Analysis of the fetal electrocardiogram (FECG) from abdominal recordings may be used as a noninvasive technique to assess fetal well being. Consequently, interest has arisen in the development of a real-time fetal ECG monitoring system. In this paper, we present a Windows user interface developed for our antenatal FECG analysis system. The program (FECGV1), written in C++, consists of three principal modules: System Setup, Real-Time FECG Analysis, and Result Review. By adopting the Windows' graphical user interface and object-oriented programming methodology, the system software accomplished the goals of visualization, easy use, extensibility, and user adaptability. Preliminary clinical application has shown that this approach provides a strong basis for a solution to real-time antenatal FECG analysis.

The application of finite impulse response filters to the detection of fetal electrocardiogram signals.

An investigation is made into the potential application of linear phase digital filters to the detection of fetal electrocardiogram signals buried in noise. Such an assessment is made by applying both matched and linear phase filters to six computer simulated fetal signals and also to experimental data. The number of times that the R-wave locations are correctly located (N), the RMS error in R-wave location (RMS) and the correlation coefficient between the averaged and clean signals are computed. It is found that the averaged fetal complexes computed using these two types of filter are almost identical. However, for three of the signals, the values for N and RMS obtained using the linear phase filter are inferior to the corresponding results obtained with the matched filter. It is suggested that the averaged complex obtained using the linear phase filter could be used as an approximation to the matched filter template; it is found that this procedure results in an effectiveness of detecting R-waves that is, for the most part, comparable with the performance of a matched filter based on the QRS complex.

Wavelet transform as a potential tool for ECG analysis and compression.

The recently introduced wavelet transform is a member of the class of time-frequency representations which include the Gabor short-time Fourier transform and Wigner-Ville distribution. Such techniques are of significance because of their ability to display the spectral content of a signal as time elapses. The value of the wavelet transform as a signal analysis tool has been demonstrated by its successful application to the study of turbulence and processing of speech and music. Since, in common with these subjects, both the time and frequency content of physiological signals are often of interest (the ECG being an obvious example), the wavelet transform represents a particularly relevant means of analysis. Following a brief introduction to the wavelet transform and its implementation, this paper describes a preliminary investigation into its application to the study of both ECG and heart rate variability data. In addition, the wavelet transform can be used to perform multiresolution signal decomposition. Since this process can be considered as a sub-band coding technique, it offers the opportunity for data compression, which can be implemented using efficient pyramidal algorithms. Results of the compression and reconstruction of ECG data are given which suggest that the wavelet transform is well suited to this task.

Study of cardiac arrhythmia using the Kalman filter.

It has been known for some time that the variability of the R-R intervals in the electrocardiogram signal yields valuable information concerning the various types of arrhythmia that might be present. It has recently been suggested that the identification of cardiac arrhythmia might be possible by applying spectral analysis techniques to the data. An investigation is made into the possible application of the Kalman filter identifier in the calculation of time varying spectra of the data, with a view to studying the onset of arrhythmia and also short bursts of arrhythmia. To this end, data from the MIT-BIH database are analysed; in particular, cases of bigenimy, trigenimy, second degree block and ventricular flutter have been looked at. It is found that this technique can, in many cases, detect the onset of arrhythmia and sometimes actually identify the arrhythmia that is present. It is suggested that the Kalman filter identifier could have a general application in studying both the normal and arrhythmic segments of data to yield valuable medical information concerning the subject under study.

Signal processing of the fetal electrocardiogram.

There has been considerable interest in recent years in the monitoring of the well-being of the fetus by the analysis of changes in morphology of the fetal electrocardiogram (FECG) signal. In this article, a brief review is first made of the use of the scalp electrode method to obtain an enhanced FECG complex. Subsequently, the problems in extracting the fetal signal from measurements taken from the abdomen of the mother are discussed. A comparison is made between two existing algorithms to extract the fetal signal: (1) the method of Akselrod; and (2) the adaptive filtering algorithm of Widrow. These algorithms are evaluated by considering the errors that could occur in locating the fetal R wave in the presence of noise.

Study of cardiac arrhythmia using zero-crossing analysis.

It has been known for some time that the variability of the R-R intervals in the electrocardiogram yields valuable information concerning the types of arrhythmia which might be present. In this paper, an investigation is made into the application of zero-crossing analysis to the study of such variability. The number of times the R-R interval crosses its mean value over a specified interval of time is counted, and may be associated with a particular characteristic frequency, related to the dominant frequency components of the power spectrum of R-R intervals. Higher order crossing counts may be computed by taking combinations of sum and difference operations on the original time series. The advantage of using zero-crossing analysis over spectral analysis is the computational simplicity of the former. It is demonstrated, by analysing data taken from the MIT-BIH Arrhythmia database, that zero crossing analysis can sometimes be used t distinguish between different arrhythmias, but forethought concerning the number of sum and difference operations to be taken on the original data set is required when computing the higher order crossing counts.