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Call number: LD2668 .T4 1978 K56 / Master of Science

Electrocardiogram (ECG) data are stored and analyzed in different formats, devices, and computer platforms. There is a need to have an independent platform to support ECG processes among different resources for the purposes of improving the quality of health care and proliferating the results from research. Currently, ECG devices are proprietary. Devices from different manufacturers cannot communicate with each other. It is crucial to have an open standard to manage ECG data for representation and diagnosis. This research explores methods for representation and diagnosis of ECG by developing an Ontology for shared ECG data based on the Health Level Seven (HL7) standard. The developed Ontology bridges the conceptual gap by integrating ECG waveform data, HL7 standard data descriptions, and cardiac diagnosis rules. The Ontology is encoded in Extensible Markup Language (XML) providing human and machine readable format. Thus, the interoperability issue is resolved and ECG data can be shared among different ECG devices and systems. This developed Ontology also provides a mechanism for diagnostic decision support through an automated ECG diagnosis system for a medical technician or physician in the diagnosis of cardiac disease. An experiment was conducted to validate the interoperability of the Ontology, and also to assess the accuracy of the diagnosis model provided through the Ontology. Results showed 100% interoperability from ECG data provided through eight different databases, and a 93% accuracy in diagnosis of normal and abnormal cardiac conditions. / Graduation date: 2006

Thesis (M.S.)--Central Connecticut State University, 2004. / Thesis advisor: Cheryl Watson. " ... in partial fulfillment of the requirements for the degree of Master of Science in Biology." Includes bibliographical references (leaves 43-47). Also available via the World Wide Web.

Thesis (M. Phil.)--University of Hong Kong, 1989.

published_or_final_version / Electrical Engineering / Master / Master of Philosophy

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Electrocardiogram (EKG) signals were digitized and the data analyzed with a fast Fourier transform computer pro- rain. The signals were amplified with a differential input EKG amplifier and converted to a frequency with a model 8038 function generator. The output frequency response was linear from 150 kHz to 300 kHz for an input voltage range of four volts. The frequency was recorded as a function of time Nuclear Data 2200 multichannel analyzer operated in the multiscale mode utilizing a dwell time of four cosec per channel. Digitized EKG data for 17 subjects were obtained in this manner. Previously digitized data for 29 patients were also obtained from the Public Health Service. Discrete Fourier transform analysis was performed on the data and the power spectrum was investigated for diagnostic use. The presence of ST depression in the EKG trace was found to be accompanied by a significantly larger harmonic amplitude coefficient at n = 2 and significantly lower harmonic amplitude coefficients for n = 13 through 20 than for normal EKG's. Diagnostic criteria were developed based on these power spectrum coefficients for the identification of EKG traces with abnormal ST segments.

This thesis investigates the feasibility of using a set of non-invasive biomedical signals to monitor respiration. The signals of interest being the electrocardiogram (EGG), photoplethysmography (PPG) and impedance plethysmography (IP) signals. The work has two main aims; the first being to estimate breathing rates from the signals, the second being to detect apnoeas from the signals. The fusion of information from different signals is used throughout in developing algorithms that give more accurate respiratory information than that obtained using one signal alone. Respiratory waveforms are derived from the signals, and the accuracy of detecting individual breaths from the waveforms is assessed and compared objectively. Results from evaluations on two separate databases show there is no waveform that gives sufficient accuracy to consider using it alone. A novel fusion method is developed which uses measurements from all three signals. This fusion method is based on weighting the estimates from each signal, according to the innovation from a Kalman filter model, applied to each respiratory waveform separately. The fused estimates give a higher overall correlation with respect to the reference breathing rate values than any of the breathing estimates derived from a single waveform. The detection of both central and obstructive sleep apnoea from the signals is investigated. It is shown that the accuracy of detecting central apnoeas from the IP signal using a timedomain method, often used in practice, can be improved by combining it with information from the frequency-domain. When discriminating between obstructive sleep apnoeic and non-apnoeic data it is seen that combining features from two signals results in a superior classification accuracy than is possible by using features from just one signal. The proposed classification system using just one of these signals, the EGG, is shown to give a performance accuracy comparable to that found in the literature. In conclusion this thesis shows that by fusing information from a number of non-invasive biomedical signals, estimations of breathing rates can be found with correlation 0.8. This is superior to estimation using only the impedance pneumography signal (correlation 0.64) which is currently used to monitor respiration. The fusion approach could potentially be applied to improve other non-invasive physiological monitoring systems.