Open Access. Powered by Scholars. Published by Universities.®
Electrical and Computer Engineering Commons™
Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 2 of 2
Full-Text Articles in Electrical and Computer Engineering
Classifying Electrocardiogram With Machine Learning Techniques, Hillal Jarrar
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 …
Portable Ventilator, Bradley C. Weeks, Jack W. Brewer, Sanders Sanabria
Portable Ventilator, Bradley C. Weeks, Jack W. Brewer, Sanders Sanabria
Electrical Engineering
The current COVID-19 pandemic has heavily impacted the healthcare system in the United States and elsewhere. The need for patients to have access to a hospital with a ventilator along with a shortage of ventilators for recovery and at-home care as a result of minimal hospital vacancy for patients has been greatly stressed. The presented problem is both an unmet demand and supply of portable and effective ventilators. Existing ventilators have many shortcomings that should be addressed: size, weight, cost, and complexity of current ventilators confines users to stay in a medical facility whilst being monitored by professionals. This both …