E.R. Carson

Nonlinear dynamic tools for characterizing abdominal electromyographic signals before and during labour

This paper presents evidence that electromyographic signals during healthy uterine contractions may have a fractal temporal structure. Healthy uterine contractions start from the 16th week of the human pregnancy. These contractions become more and more frequent and increase in strength up to the end of pregnancy. Here we investigate the possibility that time series generated by labour physiological control systems may be members of a special class of complex processes which are noise driven and may require more than one exponent to characterize their fractal scaling properties. We use a simplified version of the Hurst analysis algorithm to detect fractility in abdominal electromyographic signals (AEMG) and quadratic Volterra structures to estimate nonlinearity in them. We uncover a loss of chaoticity in very early segments of uterine contractions for typical cases of failure to progress in the first stage of labour and ending with surgical procedure (Caesarean section). Results are shown for representatives of the following groups: (1) healthy pregnancy, spontaneous labour and parturition, (2) full term at 40 weeks, prolonged labour (arbitrarily defined as lasting more than 12 h) and ending with Caesarean delivery, and (3) preterm spontaneous labour (less than 37 weeks).

Modelling a progressive care system using a coloured-timed Petri net

In a progressive care system, patients are transferred between different healthcare units within a healthcare facility, depending on the different stages in the patients’ care. Although a progressive care system allows for improved delivery of treatment and monitoring through the division and specialization of labour and other resources, it is also very problematic to manage effectively with high levels of interdependence between the different units and unpredictable levels of demand that are placed on resources. The solution proposed in this paper is improved scheduling of admissions through the development of a model which may be used as a basis of schedule optimization. The model which will be proposed is designed to assist a human operator to evaluate a schedule by simulating the flow of patients around a progressive care system. The modelling methodology used is coloured-timed Petri nets (CTPNs). It will be argued that the CTPN formalism is particularly suited to the problem since it allows the dynamics of the system to be sensitive to different instantiations of system variables such as processing time. That is, sensitive to different patients having different lengths of stays and admissions requirements.

Higher-order ambulatory electrocardiogram identification and motion artifact suppression with adaptive second- and third-order Volterra filters

The objective of this paper is to demonstrate how, in a few seconds, a relatively simple ECG monitor, PC and advanced signal processing algorithms could pinpoint microvolts - late potentials - result from an infarct zone in the heart and is used as an indicator in identifying patients prone to ventricular tachycardia which, if left untreated, leads to ventricular fibrillation. We will characterize recorded ECG data obtained from the standard three vector electrodes during exercise in terms of their higher-order statistical features. Essentially we use adaptive LMS- and Kalman-based second- and third-order Volterra filters to model the non- linear low-frequency P and T waves and motion artifacts which might overlap with the QRS complex and lead to false positive QRS detection. We will illustrate the effectiveness of this new approach by mapping out bispectral regions with a strong bicoherence manifestation and showing their corresponding temporal/spatial origins. Furthermore, we will present a few examples of our own application of these non-invasive techniques to illustrate what we see as their promise for analysis of heart abnormality.

A Petri net based model of patient-flows in a progressive patient-care system

Modeling patient flows is an important modeling issue, providing healthcare managers with information regarding the resourcing requirements during the planning phase of new healthcare facilities, as well as the performance evaluation of existing facilities. This paper develops a generic model of patient flows in a progressive patient-care system based on colored Petri nets. The model is developed at two different levels of detail. At the higher-level is the flow of patients between different units. At the lower-level is the functioning of the individual units which gives rise to the higher-level behavior. Each individual unit comprising the system is represented as having a generic structure with patient length of stay and admission rates modeled as random variables for different types of patient. The model proposed here improves on previous models of patient flow in terms of flexibility, extensibility, and the range of performance parameters it is able to generate.

Virtues and vices of source separation using linear independent component analysis for blind source separation of non-linearly coupled and synchronised fetal and mother ECGs

In this paper, we address the imminent problem which arises when researchers unjudiciously use a linear and instantaneous (memoryless) model for the source mixing structures of independent component analysis (ICA), also known as blind source separation (BSS), in pursuit of separating noisy and frequently nonstationary combined mother and fetal electrocardiogram (ECG) signals from cutaneous measurements under the following false assumptions. (1) Sensors (electrodes) are instantaneous linear mixtures of mother and fetal source signals. (2) Noise is an additive Gaussian perturbation. (3) Mother and fetal ECG signals are assumed to be stationary and linear, mutually statistically independent and statistically independent from noise. (4) Most of the second-order (SO) and fourth-order (FO) blind source separation (BSS) methods developed this last decade assume that third-order cumulants vanish hence the need to use FO. All these assumptions are not valid and will be challenged. We will expose these vices without providing any significant contributions for overcoming them. Rather, we provide a framework for investigations which are based on conformal mapping of nonlinear mixtures and novel dynamic nonlinear structures with time-variant memory to cater for quadratic coupling between mother and fetal which is quasi-periodical and the concomitant (quasi) cyclostationarity. Results given here show linear ICA shortfalls in nonstationary environment which is precipitated by quadratic coupling between mother and fetal ECGs during events of synchronised QRS complexes and P-waves and account for more than 20% of the 100,000 maternal cardiac cycles obtained from several clinical trials.

Highly accurate higher order statistics based neural network classifier of specific abnormality in electrocardiogram signals

The paper describes a simple yet highly accurate multilayer feed-forward neural network classifier (based on the backpropagation algorithm) specifically designed to successfully distinguish between normal and abnormal higher-order statistics features of electrocardiogram (EGG) signals. The concerned abnormality in ECG is associated with ventricular late potentials (LPs) indicative of life threatening heart diseases. The LPs are defined as signals from areas of delayed conduction which outlast the normal QRS period (80-100 msec). The QRS along with the P and T waves constitute the heart beat cycle. This classifier incorporates both preprocessing and adaptive weight adjustments across the input layer during the training phase of the network to enhance extraction of features pertinent to LPs found in 1-D cumulants. The latter is deemed necessary to offset the low S/N ratio in the cumulant domains concomitant to performing short data segmentation in order to capture the LPs transient appearance. We summarize the procedures of feature selection for neural network training, modification to the backpropagation algorithm to speed its rate of conversion, and the pilot trial results of the neural ECG classifier.

Evaluating admissions control in a surgical progressive-care system

Abstract To be able to optimise perfonnance variables such as rates of bed-occupancy, staff utilization and numbers of delayed or cancelled admissions in any healthcare unit, the rate at which patients are admitted to that unit must be controlled. However, before these performance variables may be controlled it is necessary to identify and measure those factors which limit optimisation and control. This paper presents the results of a study which attempts to identify and measure control limiting factors in the operation of a surgical progressive-care system using a novel approach to control system evaluation.

Multifractility in labor contraction dynamics

Recently, we presented evidence that electromyographic signals during healthy uterine contractions may have a fractal temporal structure [M. Sabry-Rizk et al., Transactions of the Institute of Measurement and Control, Vol. 22, No. 3, p. 243-70, 2000]. We also uncovered a loss of chaoticity in very early segments of uterine contractions for typical cases of failure to progress in the first stage of labor and ending with surgical procedure (Caesarean section). In both healthy and unhealthy labor contractions, quadratic Volterra structures were used to estimate non-linearity in their respective measured time series. Healthy uterine contractions start from the 16/sup th/ week of the human pregnancy. These contractions become more and more frequent and increase in strength up to the end of pregnancy. In this paper, we use the Hurst analysis algorithm to detect fractility in abdominal electromyographic signals (AEMG), and show that the multi-fractal character and nonlinear properties of the healthy contractions are encoded in the Fourier phases. Results are shown for representatives of the following groups: (i) healthy pregnancy, spontaneous labor and parturition, and (ii) full term at 40 weeks, prolonged labor (arbitrarily defined as lasting more than 12 hours) and ending with Caesarean section. Our ongoing study is aimed at exploiting the aforementioned method to characterize potentially pre-term labor (less than 37 weeks) by analyzing electromyographic signals as early as 16 weeks gestation.

Systems identification in biology

The nature of system identification in biological systems is analysed, in order to show what are the special features of biological systems in relation to the identification process, and to reveal what general lessons for measurement and control can be learned from the quantitative study of biological systems. Approaches to be adopted in the identification process are ou tlined, highlighting the importance of adopting an appropriate mathematical model. It is shown that biological systems constitute a paradigm for the application of identification procedures in the observational sciences generally. The paper sets the scene for the four application studies which follow, illustrating the application of identification methods to mechanical parameters of the lung, to elements of the neuromuscular system, to the blood pressure control system and in pharmacokinetics.