Other Biomedical Engineering and Bioengineering Commons^™

Design And Development Of Contactless Capacitive Coupled Electrodes For Cardiovascular Signal Acquisition, Ramya Lakshmi Vs

Theses and Dissertations

In biosignal acquisition and physiological monitoring, developing advanced electrode technologies is crucial in enhancing signal quality, minimizing interference, and improving overall reliability. One such innovative approach is the utilization of capacitive-coupled electrodes, a cutting-edge solution that addresses some of the challenges associated with traditional electrodes in biosignal recording.

Capacitive-based sensors have become prominent in physiological signal measurement over the past decade. The electric field from the external affects both the human body and the electrode. The primary aim while designing the capacitive electrode is to maximize the signal-to-noise ratio. Hence, we introduce shielding and guarding techniques to improve the signal-to-noise …

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 …

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 …

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 …