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 a different feature extraction method to prepare the data for Naive-Bayesian classification. The effectiveness of the different machine learning techniques, when applied to electrocardiogram, is assessed by measuring the sensitivity and specificity of the classifications. Comparing the sensitivity and specificity of each machine learning technique to the other techniques revealed that the Wavelet Packet transform, followed by Naïve-Bayesian classification, is the most effective machine learning technique.
| Identifer | oai:union.ndltd.org:CALPOLY/oai:digitalcommons.calpoly.edu:theses-3991 |
| Date | 01 December 2021 |
| Creators | Jarrar, Hillal |
| Publisher | DigitalCommons@CalPoly |
| Source Sets | California Polytechnic State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Master's Theses |
Page generated in 0.0018 seconds