Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 2 of 2
Full-Text Articles in Physical Sciences and Mathematics
Optimal Analytical Methods For High Accuracy Cardiac Disease Classification And Treatment Based On Ecg Data, Jianwei Zheng
Optimal Analytical Methods For High Accuracy Cardiac Disease Classification And Treatment Based On Ecg Data, Jianwei Zheng
Computational and Data Sciences (PhD) Dissertations
This work constitutes six projects. In the first project, a newly inaugurated research database for 12-lead electrocardiogram signals was created under the auspices of Chapman University and Shaoxing People's Hospital (Shaoxing Hospital Zhejiang University School of Medicine). This database aims to enable the scientific community in conducting new studies on arrhythmia and other cardiovascular conditions. In the second project, we created a new 12-lead ECG database under the auspices of Chapman University and Ningbo First Hospital of Zhejiang University that aims to provide high quality data enabling detection of the distinctions between idiopathic ventricular arrhythmia from right ventricular outflow tract …
Assessing The Re-Identification Risk In Ecg Datasets And An Application Of Privacy Preserving Techniques In Ecg Analysis, Arin Ghazarian
Assessing The Re-Identification Risk In Ecg Datasets And An Application Of Privacy Preserving Techniques In Ecg Analysis, Arin Ghazarian
Computational and Data Sciences (PhD) Dissertations
In this work, first we investigate the use of ECG signal as a biometric in human identification systems using deep learning models. We train convolutional neural network models on ECG samples from approximately 81k patients. Our models achieved an over-all accuracy of 95.69%. Further, we assess the accuracy of our ECG identification model for distinct groups of patients with particular heart conditions and combinations of such conditions. For example, we observed that the identification accuracy was the highest (99.7%) for patients with both ST changes and supraventricular tachycardia. On the other hand, we also found that the identification rate was …