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Theses/Dissertations

Electrocardiogram

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Classifying Electrocardiogram With Machine Learning Techniques, Hillal Jarrar Dec 2021

Classifying Electrocardiogram With Machine Learning Techniques, Hillal Jarrar

Master's Theses

Classifying the electrocardiogram is of clinical importance because classification can be used to diagnose patients with cardiac arrhythmias. Many industries utilize machine learning techniques that consist of feature extraction methods followed by Naive- Bayesian classification in order to detect faults within machinery. Machine learning techniques that analyze vibrational machine data in a mechanical application may be used to analyze electrical data in a physiological application. Three of the most common feature extraction methods used to prepare machine vibration data for Naive-Bayesian classification are the Fourier transform, the Hilbert transform, and the Wavelet Packet transform. Each machine learning technique consists of …

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Detection Of Electrical Alternans In Ventricular Depolarization Phase Of Human Electrocardiogram, David R. Wasemiller Jan 2016

Detection Of Electrical Alternans In Ventricular Depolarization Phase Of Human Electrocardiogram, David R. Wasemiller

Theses and Dissertations--Biomedical Engineering

T-wave Alternans (TWA) in an electrocardiogram (ECG) has received considerable interest as a potential predictor of sudden cardiac death (SCD). However, large clinical trials have shown that while TWA has a very high negative predictive value, its positive predictive value is poor. Results of previous studies suggest that arrhythmia onset can be affected by the phase relationship of alternans of the depolarization and repolarization phase of the action potentials of the ventricles. To assess this relationship, one would first need to establish that depolarization alternans can be detected and then develop methods to determine its relationship with repolarization alternans, which …

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Icloudecg: A Mobile Cardiac Telemedicine System, David S. Clifford Dec 2015

Icloudecg: A Mobile Cardiac Telemedicine System, David S. Clifford

Masters Theses

With rising healthcare costs and a substantially growing number of patients 65 or over, the benefits of telemedicine and patient self-monitoring systems are becoming increasingly evident. Patients, physicians, hospitals, and even insurance providers benefit from vigilant, cost-effective patient monitoring systems. This thesis describes the development of a portable, smart-phone connected system for continuous cardiac monitoring. The system is capable of continuously monitoring the conditions of the heart, automated detection of cardiac arrhythmias, and real-time notifying patients and physicians of the detected abnormalities. The system consists of four main subsystems: 1) a Bluetooth capable chest-strap ECG, 2) an Android-enabled mobile device, …

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Hybrid Nanostructured Textile Bioelectrode For Unobtrusive Health Monitoring, Pratyush Rai Aug 2013

Hybrid Nanostructured Textile Bioelectrode For Unobtrusive Health Monitoring, Pratyush Rai

Graduate Theses and Dissertations

Coronary heart disease, cardiovascular diseases and strokes are the leading causes of mortality in United States of America. Timely point-of-care health diagnostics and therapeutics for person suffering from these diseases can save thousands of lives. However, lack of accessible minimally intrusive health monitoring systems makes timely diagnosis difficult and sometimes impossible. To remedy this problem, a textile based nano-bio-sensor was developed and evaluated in this research. The sensor was made of novel array of vertically standing nanostructures that are conductive nano-fibers projecting from a conductive fabric. These sensor electrodes were tested for the quality of electrical contact that they made …

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Automated Myocardial Infarction Diagnosis From Ecg, Chen Zhou Oct 2001

Automated Myocardial Infarction Diagnosis From Ecg, Chen Zhou

Doctoral Dissertations

In the present dissertation, an automated neural network-based ECG diagnosing system was designed to detect the presence of myocardial infarction based on the hypothesis that an artificial neural network-based ECG interpretation system may improve the clinical myocardial infarction. 137 patients were included. Among them 122 had myocardial infarction, but the remaining 15 were normal. The sensitivity and the specificity of present system were 92.2% and 50.7% respectively. The sensitivity was consistent with relevant research. The relatively low specificity results from the rippling of the low pass filtering. We can conclude that neural network-based system is a promising aid for the …

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Bottom-Up Design Of Artificial Neural Network For Single-Lead Electrocardiogram Beat And Rhythm Classification, Srikanth Thiagarajan Jan 2000

Bottom-Up Design Of Artificial Neural Network For Single-Lead Electrocardiogram Beat And Rhythm Classification, Srikanth Thiagarajan

Doctoral Dissertations

Performance improvement in computerized Electrocardiogram (ECG) classification is vital to improve reliability in this life-saving technology. The non-linearly overlapping nature of the ECG classification task prevents the statistical and the syntactic procedures from reaching the maximum performance. A new approach, a neural network-based classification scheme, has been implemented in clinical ECG problems with much success. The focus, however, has been on narrow clinical problem domains and the implementations lacked engineering precision. An optimal utilization of frequency information was missing. This dissertation attempts to improve the accuracy of neural network-based single-lead (lead-II) ECG beat and rhythm classification. A bottom-up approach defined …

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