Cardiovascular mortality continues to be the leading cause of death in the United Kingdom, China and the United States. Many of these deaths occur suddenly, called sudden cardiac death (SCD), with the number of these events estimated from these three Countries to be over 1,500,000 annually. In the vast majority of documented cases, the SCD is directly caused by ventricular tachycardia (V1). Prediction of the presence of the VT is of great importance. It has been found by using signal averaging (SA) techniques that the appearance of micro signals, called ventricular late potentials (VLPs), is highly correlated with the appearance of VT or SCD. The VLPs are about 0.1% - 1% of the size of the normal ECG in most patients and are masked by various noise sources, so that they can not be seen from the standard electrocardiogram (ECG). The SA techniques, depending largly on averaging many beats, can only detect the microvolt signals that are strictly constant in duration, morphology and timing relative to the QRS complex amongst the considerable amounts of noise which are present The main disadvantage of the technique is that it cannot offer information from an individual beat, i.e. variations among the beats and individual beat information are lost when averaging. This information can be very important in the diagnosis of the development of many heart abnormalities, particularly arrhythmias. This thesis describes various techniques that have been developed for a real-time processing system, in which the system can detect VLPs at the body surface with beat-to-beat variations. One of the most important techniques is the use of adaptive filters to reduce the most disruptive noise -random noise. Clinical investigations have been carried out based on 14 normal and 20 abnormal pathological subjects to produce reproducible results on the developed system. The results show that the system can produce much more information than SA techniques for the prediction of VT.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:358246 |
| Date | January 1993 |
| Creators | Wang, Wei |
| Publisher | University of Sussex |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
Page generated in 0.0019 seconds