Principal component analysis of T-waves

The mechanical work of the heart is triggered by electrical activity in the muscle fibres. The potentials from individual muscle fibres interfere with each other to produce an electrical field which can be examined by recording potential differences on the body surface with an electrocardiogram (ECG). An ECG contains several wave- components, each describing one particular sequence of the heart cycle. The so-called T wave in the ECG contains information about the electrical recovery phase (repolarization). Disturbances in the repolarization phase may increase the risk of fatal arrhythmias. Thus, markers which indicate repolarization disturbances would be of great clinical value.

In an earlier study, principal component analysis (PCA) of the T wave in a 12-channel ECG, showed differences between groups of patients with different degrees of repolarization disturbances. However, the method is not sensitive enough to be used on single individuals. In order to make the method more sensitive, one hypothesis is to increase the number of ECG channels and the sampling frequency. Testing this hypothesis requires a software tool and unconventional measurement equipment.

This master’s thesis considers implementation and simulation tests of a software tool for PCA of the T wave in multi-channel ECGs. The practical aspect of this study consisted of building a connection block which gives extra surface ECG channels to an apparatus for invasive measurements.

Before PCA can be performed, the ECG signals must pass a number of preprocessing steps. Much of the effort in this study revolved around defining the time instances when the T wave starts and ends. Three algorithms for defining the T wave onset and four algorithms for defining the T wave end where implemented and evaluated in a simulation study. In addition, a test was performed in which simulated data were reproduced according to an earlier simulation study. The reproduced data were analyzed by the implemented software tool and compared with the previous study.

The results from the simulation study suggested that a combination of a T- onset algorithm based on the heart rate interval and an area-based T-end algorithm is the most robust in sense of noise and wave-shape abnormalities. The test with the reproduced data showed similar results as the earlier simulation study.

Author: Gustavsson, Ola

Source: Lulea University of Technology

Download Link: Click Here To Download This Report (PDF)

Reference URL: Visit Now

Leave a Comment