ECG Noise Filtering Using Online Model-Based Bayesian Filtering Techniques

The electrocardiogram (ECG) is a time-varying electrical signal that interprets the electrical activity of the heart. It is obtained by a non-invasive technique known as surface electromyography (EMG), used widely in hospitals. There are many clinical contexts in which ECGs are used, such as medical diagnosis, physiological therapy and arrhythmia monitoring. In medical diagnosis, medical conditions are interpreted by examining information and features in ECGs. Physiological therapy involves the control of some aspect of the physiological effort of a patient, such as the use of a pacemaker to regulate the beating of the heart. Moreover, arrhythmia monitoring involves observing and detecting life-threatening conditions, such as myocardial infarction or heart attacks, in a patient. ECG signals are usually corrupted with various types of unwanted interference such as muscle artifacts, electrode artifacts, power line noise and respiration interference, and are distorted in such a way that it can be difficult to perform medical diagnosis, physiological therapy or arrhythmia monitoring. Consequently signal processing on ECGs is required to remove noise and interference signals for successful clinical applications. Existing signal processing techniques can remove some of the noise in an ECG signal, but are typically inadequate for extraction of the weak ECG components contaminated with background noise and for retention of various subtle features in the ECG. For example, the noise from the EMG usually overlaps the fundamental ECG cardiac components in the frequency domain, in the range of 0.01 Hz to 100 Hz. Simple filters are inadequate to remove noise which overlaps with ECG cardiac components. Sameni et al. have proposed a Bayesian filtering framework to resolve these problems, and this gives results which are clearly superior to the results obtained from application of conventional signal processing methods to ECG. However, a drawback of this Bayesian filtering framework is that it must run offline, and this of course is not desirable for clinical applications such as arrhythmia monitoring and physiological therapy, both of which re- quire online operation in near real-time. To resolve this problem, in this thesis we propose a dynamical model which permits the Bayesian filtering framework to function online. The framework with the proposed dynamical model has less than 4% loss in performance compared to the previous (offline) version of the framework. The proposed dynamical model is based on theory from fixed-lag smoothing

Neutrophil count prediction in childhood cancer patients receiving 6-mercaptopurine chemotherapy treatment

Acute Lymphoblastic Leukaemia (ALL) is a common form of blood cancer, usually affecting children under 15 years of age. Chemotherapy treatment for ALL is delivered in three phases viz. induction (to achieve initial remission), intensification (to kill the majority of abnormal cells), and finally, maintenance. The maintenance phase involves oral administration of the chemotherapy drug 6-Mercaptopurine (6-MP) in varying doses to destroy any remaining abnormal cells and prevent reoccurrence. A key side effect of the treatment is a reduction in neutrophil counts that can result in a condition known as neutropenia, i.e. reduced immune system. This carries a risk of secondary infection and has been linked to 60% of ALL fatalities. Current practice aims to control neutrophil counts by varying 6-MP dosages on a weekly basis based on blood counts. However, its success is varied. This thesis proposes a number of intelligent prediction methods to more accurately predicting neutrophil counts one week ahead using blood count data and corresponding 6-MP dosing regimens. Firstly, a well-known and robust neural network (Nonlinear Autoregressive Exogenous) is applied to blood count data to provide an initial assessment of the feasibility of such an approach. A comparative analysis of a series of more complex algorithms is then considered for more advanced, in-depth analysis viz. Multi-Layer Perceptron (MLP) and Support Vector Machines (SVM). Both methods are shown to have a prediction accuracy of around 60% on the first sample period, with the MLP also having a prediction accuracy of more than 60% in the second sample period in seven out of ten blood data points (there was 10 timeseries blood data predictions). However, in comparison the accuracy of SVM is relatively low. Finally, an incremental learning-based approach is proposed to increase the accuracy of the system and provide a realistic framework for real-time implementation. The accuracy is shown to improve considerably as more data is added, and the predicted neutrophils data is shown to follow the trend of the actual neutrophil counts

DYNAMIC SELF-ORGANISED NEURAL NETWORK INSPIRED BY THE IMMUNE ALGORITHM FOR FINANCIAL TIME SERIES PREDICTION AND MEDICAL DATA CLASSIFICATION

Artificial neural networks have been proposed as useful tools in time series analysis in a variety of applications. They are capable of providing good solutions for a variety of problems, including classification and prediction. However, for time series analysis, it must be taken into account that the variables of data are related to the time dimension and are highly correlated. The main aim of this research work is to investigate and develop efficient dynamic neural networks in order to deal with data analysis issues. This research work proposes a novel dynamic self-organised multilayer neural network based on the immune algorithm for financial time series prediction and biomedical signal classification, combining the properties of both recurrent and self-organised neural networks. The first case study that has been addressed in this thesis is prediction of financial time series. The financial time series signal is in the form of historical prices of different companies. The future prediction of price in financial time series enables businesses to make profits by predicting or simply guessing these prices based on some historical data. However, the financial time series signal exhibits a highly random behaviour, which is non-stationary and nonlinear in nature. Therefore, the prediction of this type of time series is very challenging. In this thesis, a number of experiments have been simulated to evaluate the ability of the designed recurrent neural network to forecast the future value of financial time series. The resulting forecast made by the proposed network shows substantial profits on financial historical signals when compared to the self-organised hidden layer inspired by immune algorithm and multilayer perceptron neural networks. These results suggest that the proposed dynamic neural networks has a better ability to capture the chaotic movement in financial signals. The second case that has been addressed in this thesis is for predicting preterm birth and diagnosing preterm labour. One of the most challenging tasks currently facing the healthcare community is the identification of preterm labour, which has important significances for both healthcare and the economy. Premature birth occurs when the baby is born before completion of the 37-week gestation period. Incomplete understanding of the physiology of the uterus and parturition means that premature labour prediction is a difficult task. The early prediction of preterm births could help to improve prevention, through appropriate medical and lifestyle interventions. One promising method is the use of Electrohysterography. This method records the uterine electrical activity during pregnancy. In this thesis, the proposed dynamic neural network has been used for classifying between term and preterm labour using uterine signals. The results indicated that the proposed network generated improved classification accuracy in comparison to the benchmarked neural network architectures