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

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.

No description available.

Thesis. 1976. M.S.--Massachusetts Institute of Technology. Dept. of Aeronautics and Astronautics. / Microfiche copy available in Archives and Barker. / Includes bibliographical references. / M.S.

Thesis. 1976. M.S.--Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. / Microfiche copy available in Archives and Engineering. / Includes bibliographical references. / M.S.

Lu, Yan. / "October 2010." / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (leaves 65-68). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Chapter 1. --- Introduction --- p.1 / Chapter 1.1 --- Electrocardiogram --- p.1 / Chapter 1.1.1 --- ECG Measurement --- p.2 / Chapter 1.1.2 --- Cardiac Conduction Pathway and ECG Morphology --- p.4 / Chapter 1.1.3 --- A Basic Clinical Approach to ECG Analysis --- p.6 / Chapter 1.2 --- Cardiovascular Disease --- p.7 / Chapter 1.3 --- Motivation --- p.9 / Chapter 1.4 --- Related Work --- p.10 / Chapter 1.5 --- Overview of Proposed Approach --- p.11 / Chapter 1.6 --- Thesis Outline --- p.13 / Chapter 2. --- ECG Signal Preprocessing --- p.14 / Chapter 2.1 --- ECG Model and Its Generalization --- p.14 / Chapter 2.1.1 --- ECG Dynamic Model --- p.14 / Chapter 2.1.2 --- Generalization of ECG Model --- p.15 / Chapter 2.2 --- Empirical Mode Decomposition --- p.17 / Chapter 2.3 --- Baseline Wander Removal --- p.20 / Chapter 2.3.1 --- Sources of Baseline Wander --- p.20 / Chapter 2.3.2 --- Baseline Wander Removal by EMD --- p.20 / Chapter 2.3.3 --- Experiments on Baseline Wander Removal --- p.21 / Chapter 2.4 --- ECG Denoising --- p.24 / Chapter 2.4.1 --- Introduction --- p.24 / Chapter 2.4.2 --- Instantaneous Frequency --- p.26 / Chapter 2.4.3 --- Problem of Direct ECG Denoising by EMD : --- p.28 / Chapter 2.4.4 --- Model-based Pre-filtering --- p.30 / Chapter 2.4.5 --- EMD Denoising Using Significance Test --- p.33 / Chapter 2.4.6 --- EMD Denoising using Instantaneous Frequency --- p.35 / Chapter 2.4.7 --- Experiments --- p.39 / Chapter 2.5 --- Chapter Summary --- p.44 / Chapter 3. --- ECG Classification --- p.45 / Chapter 3.1 --- Database --- p.45 / Chapter 3.2 --- Feature Extraction --- p.46 / Chapter 3.2.1 --- Feature Selection --- p.46 / Chapter 3.2.2 --- Feature Dimension Reduction by GDA --- p.48 / Chapter 3.3 --- Classification by Support Vector Machine --- p.50 / Chapter 3.4 --- Experiments --- p.53 / Chapter 3.4.1 --- Performance of Feature Reduction --- p.54 / Chapter 3.4.2 --- Performance of Classification --- p.57 / Chapter 3.4.3 --- Performance Comparison with Other Works --- p.60 / Chapter 3.5 --- Chapter Summary --- p.61 / Chapter 4. --- Conclusions --- p.63 / Reference --- p.65