Применение финитных базисных сплайнов при восстановлении сигналов электрогастроэнтерографии

Electrogastroenterography is the promising method of examination of the motion activity of the digestive system. It is based on the measurement and further processing of bioelectric signals. During last years the progress in the development of electrophysiological methods of diagnostics is due to the computer processing of measuring signals. This paper is devoted to the aspects of organization of measurements in electrogastroenterography. In the paper, we present an introduction to the problem domain; analyze the information structure of a measuring signal; review the diagnostics parameters obtained as a result of spectral analysis of electrogastroenterography signals; discuss the tasks of automation of diagnostics. We propose the new method of sampling of gastroenterograms. It considers the factor of the finite length of measurement sessions and spectral properties of signals. Representation of a signal as the finite sum of finite cardinal B-splines with integer degrees is used in the method. The computer experiment for testing the accuracy of signal reconstruction with parameters of measuring session used in electrogastroenterography was conducted.Электрогастроэнтерография — перспективный метод обследования сократительной активности органов пищеварительной системы, основанный на съеме и последующей обработке биоэлектрических сигналов. Прогресс в развитии электрофизиологических методов диагностики в последние годы во многом обеспечен возможностью компьютерной обработки измерительных сигналов. Данная статья посвящена вопросам организации измерений в электрогастроэнтерографии. Дано введение в предметную область, проанализирован информационный состав измерительных сигналов электрогастроэнтерографии, приведены диагностические показатели, получаемые в результате спектрального анализа сигналов, рассмотрены задачи автоматизации диагностики. Предложен новый метод выбора частоты отсчетов гастроэнтерограмм, учитывающий фактор конечной длительности измерительных сеансов и спектральных свойств сигнала. В предлагаемом методе применяется разложение сигнала в виде конечной суммы финитных кардинальных В-сплайнов целых степеней. Проведен вычислительный эксперимент для определения точности восстановления сигнала при используемых в электрогастроэнтерографии параметрах измерительного сеанса

The Application of Genetic Algorithms in the Biological Medical Diagnostic Research

In this paper, a genetic algorithm is used to determine the Mean Corpuscular Volume (MCV) as the optimal decision-making criterion for anemia caused by iron deficiency based on the diagnostic test of patients with such anemia. On the premise of attaining maximum sensitivity and specificity for the cost, this paper studies the impact of the cost ratio of the optimal decision-making criteria and compares the mathematical derivation and binominal model method, so as to discuss the application of the optimal diagnostic criteria in the genetic algorithm and provide a practical study method for the diagnostic test

Imaging fascicular organisation in mammalian vagus nerve for selective VNS

Nerves contain a large number of nerve fibres, or axons, organised into bundles known as fascicles. Despite the somatic nervous system being well understood, the organisation of the fascicles within the nerves of the autonomic nervous system remains almost completely unknown. The new field of bioelectronics medicine, Electroceuticals, involves the electrical stimulation of nerves to treat diseases instead of administering drugs or performing complex surgical procedures. Of particular interest is the vagus nerve, a prime target for intervention due to its afferent and efferent innervation to the heart, lungs and majority of the visceral organs. Vagus nerve stimulation (VNS) is a promising therapy for treatment of various conditions resistant to standard therapeutics. However, due to the unknown anatomy, the whole nerve is stimulated which leads to unwanted off-target effects. Electrical Impedance Tomography (EIT) is a non-invasive medical imaging technique in which the impedance of a part of the body is inferred from electrode measurements and used to form a tomographic image of that part. Micro-computed tomography (microCT) is an ex vivo method that has the potential to allow for imaging and tracing of fascicles within experimental models and facilitate the development of a fascicular map. Additionally, it could validate the in vivo technique of EIT. The aim of this thesis was to develop and optimise the microCT imaging method for imaging the fascicles within the nerve and to determine the fascicular organisation of the vagus nerve, ultimately allowing for selective VNS. Understanding and imaging the fascicular anatomy of nerves will not only allow for selective VNS and the improvement of its therapeutic efficacy but could also be integrated into the study on all peripheral nerves for peripheral nerve repair, microsurgery and improving the implementation of nerve guidance conduits. Chapter 1 provides an introduction to vagus nerve anatomy and the principles of microCT, neuronal tracing and EIT. Chapter 2 describes the optimisation of microCT for imaging the fascicular anatomy of peripheral nerves in the experimental rat sciatic and pig vagus nerve models, including the development of pre-processing methods and scanning parameters. Cross-validation of this optimised microCT method, neuronal tracing and EIT in the rat sciatic nerve was detailed in Chapter 3. Chapter 4 describes the study with microCT with tracing, EIT and selective stimulation in pigs, a model for human nerves. The microCT tracing approach was then extended into the subdiaphragmatic branches of the vagus nerves, detailed in Chapter 5. The ultimate goal of human vagus nerve tracing was preliminarily performed and described in Chapter 6. Chapter 7 concludes the work and describes future work. Lastly, Appendix 1 (Chapter 8) is a mini review on the application of selective vagus nerve stimulation to treat acute respiratory distress syndrome and Appendix 2 is morphological data corresponding to Chapter 4

On the development of intelligent medical systems for pre-operative anaesthesia assessment

This thesis describes the research and development of a decision support tool for determining a medical patient's suitability for surgical anaesthesia. At present, there is a change in the way that patients are clinically assessedp rior to surgery. The pre-operative assessment, usually conducted by a qualified anaesthetist, is being more frequently performed by nursing grade staff. The pre-operative assessmenet xists to minimise the risk of surgical complications for the patient. Nursing grade staff are often not as experienced as qualified anaesthetists, and thus are not as well suited to the role of performing the pre-operative assessment. This research project used data collected during pre-operative assessments to develop a decision support tool that would assist the nurse (or anaesthetist) in determining whether a patient is suitable for surgical anaesthesia. The three main objectives are: firstly, to research and develop an automated intelligent systems technique for classifying heart and lung sounds and hence identifying cardio-respiratory pathology. Secondly, to research and develop an automated intelligent systems technique for assessing the patient's blood oxygen level and pulse waveform. Finally, to develop a decision support tool that would combine the assessmentsa bove in forming a decision as to whether the patient is suitable for surgical anaesthesia. Clinical data were collected from hospital outpatient departments and recorded alongside the diagnoses made by a qualified anaesthetist. Heart and lung sounds were collected using an electronic stethoscope. Using this data two ensembles of artificial neural networks were trained to classify the different heart and lung sounds into different pathology groups. Classification accuracies up to 99.77% for the heart sounds, and 100% for the lung sounds has been obtained. Oxygen saturation and pulse waveform measurements were recorded using a pulse oximeter. Using this data an artificial neural network was trained to discriminate between normal and abnormal pulse waveforms. A discrimination accuracy of 98% has been obtained from the system. A fuzzy inference system was generated to classify the patient's blood oxygen level as being either an inhibiting or non-inhibiting factor in their suitability for surgical anaesthesia. When tested the system successfully classified 100% of the test dataset. A decision support tool, applying the genetic programming evolutionary technique to a fuzzy classification system was created. The decision support tool combined the results from the heart sound, lung sound and pulse oximetry classifiers in determining whether a patient was suitable for surgical anaesthesia. The evolved fuzzy system attained a classification accuracy of 91.79%. The principal conclusion from this thesis is that intelligent systems, such as artificial neural networks, genetic programming, and fuzzy inference systems, can be successfully applied to the creation of medical decision support tools.EThOS - Electronic Theses Online ServiceMedicdirect.co.uk Ltd.GBUnited Kingdo

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 406)

This bibliography lists 346 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during Oct. 1995. Subject coverage includes: aerospace medicine and physiology, life support systems and man/system technology, protective clothing, exobiology and extraterrestrial life, planetary biology, and flight crew behavior and performance