Theoretical Interpretations and Applications of Radial Basis Function Networks

Medical applications usually used Radial Basis Function Networks just as Artificial Neural Networks. However, RBFNs are Knowledge-Based Networks that can be interpreted in several way: Artificial Neural Networks, Regularization Networks, Support Vector Machines, Wavelet Networks, Fuzzy Controllers, Kernel Estimators, Instanced-Based Learners. A survey of their interpretations and of their corresponding learning algorithms is provided as well as a brief survey on dynamic learning algorithms. RBFNs' interpretations can suggest applications that are particularly interesting in medical domains

Intelligent Pattern Analysis of the Foetal Electrocardiogram

The aim of the project on which this thesis is based is to develop reliable techniques for foetal electrocardiogram (ECG) based monitoring, to reduce incidents of unnecessary medical intervention and foetal injury during labour. World-wide electronic foetal monitoring is based almost entirely on the cardiotocogram (CTG), which is a continuous display of the foetal heart rate (FHR) pattern together with the contraction of the womb. Despite the widespread use of the CTG, there is no significant improvement in foetal outcome. In the UK alone it is estimated that birth related negligence claims cost the health authorities over £400M per-annum. An expert system, known as INFANT, has recently been developed to assist CTG interpretation. However, the CTG alone does not always provide all the information required to improve the outcome of labour. The widespread use of ECG analysis has been hindered by the difficulties with poor signal quality and the difficulties in applying the specialised knowledge required for interpreting ECG patterns, in association with other events in labour, in an objective way. A fundamental investigation and development of optimal signal enhancement techniques that maximise the available information in the ECG signal, along with different techniques for detecting individual waveforms from poor quality signals, has been carried out. To automate the visual interpretation of the ECG waveform, novel techniques have been developed that allow reliable extraction of key features and hence allow a detailed ECG waveform analysis. Fuzzy logic is used to automatically classify the ECG waveform shape using these features by using knowledge that was elicited from expert sources and derived from example data. This allows the subtle changes in the ECG waveform to be automatically detected in relation to other events in labour, and thus improve the clinicians position for making an accurate diagnosis. To ensure the interpretation is based on reliable information and takes place in the proper context, a new and sensitive index for assessing the quality of the ECG has been developed. New techniques to capture, for the first time in machine form, the clinical expertise / guidelines for electronic foetal monitoring have been developed based on fuzzy logic and finite state machines, The software model provides a flexible framework to further develop and optimise rules for ECG pattern analysis. The signal enhancement, QRS detection and pattern recognition of important ECG waveform shapes have had extensive testing and results are presented. Results show that no significant loss of information is incurred as a result of the signal enhancement and feature extraction techniques

Fuzzy rule-based system applied to risk estimation of cardiovascular patients

Cardiovascular decision support is one area of increasing research interest. On-going collaborations between clinicians and computer scientists are looking at the application of knowledge discovery in databases to the area of patient diagnosis, based on clinical records. A fuzzy rule-based system for risk estimation of cardiovascular patients is proposed. It uses a group of fuzzy rules as a knowledge representation about data pertaining to cardiovascular patients. Several algorithms for the discovery of an easily readable and understandable group of fuzzy rules are formalized and analysed. The accuracy of risk estimation and the interpretability of fuzzy rules are discussed. Our study shows, in comparison to other algorithms used in knowledge discovery, that classifcation with a group of fuzzy rules is a useful technique for risk estimation of cardiovascular patients. © 2013 Old City Publishing, Inc

Novel hybrid extraction systems for fetal heart rate variability monitoring based on non-invasive fetal electrocardiogram

This study focuses on the design, implementation and subsequent verification of a new type of hybrid extraction system for noninvasive fetal electrocardiogram (NI-fECG) processing. The system designed combines the advantages of individual adaptive and non-adaptive algorithms. The pilot study reviews two innovative hybrid systems called ICA-ANFIS-WT and ICA-RLS-WT. This is a combination of independent component analysis (ICA), adaptive neuro-fuzzy inference system (ANFIS) algorithm or recursive least squares (RLS) algorithm and wavelet transform (WT) algorithm. The study was conducted on clinical practice data (extended ADFECGDB database and Physionet Challenge 2013 database) from the perspective of non-invasive fetal heart rate variability monitoring based on the determination of the overall probability of correct detection (ACC), sensitivity (SE), positive predictive value (PPV) and harmonic mean between SE and PPV (F1). System functionality was verified against a relevant reference obtained by an invasive way using a scalp electrode (ADFECGDB database), or relevant reference obtained by annotations (Physionet Challenge 2013 database). The study showed that ICA-RLS-WT hybrid system achieve better results than ICA-ANFIS-WT. During experiment on ADFECGDB database, the ICA-RLS-WT hybrid system reached ACC > 80 % on 9 recordings out of 12 and the ICA-ANFIS-WT hybrid system reached ACC > 80 % only on 6 recordings out of 12. During experiment on Physionet Challenge 2013 database the ICA-RLS-WT hybrid system reached ACC > 80 % on 13 recordings out of 25 and the ICA-ANFIS-WT hybrid system reached ACC > 80 % only on 7 recordings out of 25. Both hybrid systems achieve provably better results than the individual algorithms tested in previous studies.Web of Science713178413175

Computer Aided ECG Analysis - State of the Art and Upcoming Challenges

In this paper we present current achievements in computer aided ECG analysis and their applicability in real world medical diagnosis process. Most of the current work is covering problems of removing noise, detecting heartbeats and rhythm-based analysis. There are some advancements in particular ECG segments detection and beat classifications but with limited evaluations and without clinical approvals. This paper presents state of the art advancements in those areas till present day. Besides this short computer science and signal processing literature review, paper covers future challenges regarding the ECG signal morphology analysis deriving from the medical literature review. Paper is concluded with identified gaps in current advancements and testing, upcoming challenges for future research and a bullseye test is suggested for morphology analysis evaluation.Comment: 7 pages, 3 figures, IEEE EUROCON 2013 International conference on computer as a tool, 1-4 July 2013, Zagreb, Croati

Development of Low Cost Heart Rate Monitoring Device and Classification Technique Using Fuzzy Logics Algorithm

Heart as one of necessary organs, has been examined profoundly by the heart rate changes. The heart rate is affected by many factors, such as age, gender, physiological conditions. Hence, better diagnosis can be made if the interpretation of heart rate signal would be automated that eliminates the human error while comprising the influential factors. Subjective readings may lead to imprecise diagnosis. In this project, proposed tool is designed for medical experts that can reliably interpret the heart signal based on age, gender and heart condition. PPG sensor was utilized to sense the heartbeats. Furthermore, the raw signal was transferred through wireless medium using RF Transceivers and Arduino Uno as a microcontroller to the remote base station. This would let end users (physicians/Caregivers) to have a real-time heart rate monitoring without a need of connecting wires from the patient ward/room to the remote station which was designed in MATLAB GUI. The classification of the signal being obtained is achieved through fuzzy logics algorithm inside the MATLAB Fuzzy Logic Toolbox. The cost-effectiveness of the proposed project was another benefits that could be added to an automated heart rate monitoring device

Cardiomyopathy Detection from Electrocardiogram Features

Cardiomyopathy means heart (cardio) muscle (myo) disease (pathy) . Currently, cardiomyopathies are defined as myocardial disorders in which the heart muscle is structurally and/or functionally abnormal in the absence of a coronary artery disease, hypertension, valvular heart disease or congenital heart disease sufficient to cause the observed myocardial abnormalities. This book provides a comprehensive, state-of-the-art review of the current knowledge of cardiomyopathies. Instead of following the classic interdisciplinary division, the entire cardiovascular system is presented as a functional unity, and the contributors explore pathophysiological mechanisms from different perspectives, including genetics, molecular biology, electrophysiology, invasive and non-invasive cardiology, imaging methods and surgery. In order to provide a balanced medical view, this book was edited by a clinical cardiologist