Edinburgh Napier University

  It has been shown that the heati rate varies not only in relation to the cardiac demand
but is also affected by the presence of cardiac disease and diabetes. Furthermore, it has
been shown that heart rate variability may be used as an early indicator of cardiac
disease susceptibility and the presence of diabetes. Therefore, the heati rate variability
may be used for early clinical screening of these diseases. In order to reliably assess the
patient's condition, the heati rate variability infolTIlation is determined from an
electrocardiogram data acquisition system. Once collected, the heati rate variability
signal is characterised and used as a basis for classification.
This study details the development of a heart rate variability data acquisition system,
method of collecting known patient data, and design of a signal-processing algorithm
that characterises heart rate variability infolTIlation to be used as a basis for patient
classification. Specifically, six sets of 5 minute electrocardiogram signals are collected
by a personal computer based data acquisition system in a clinical setting. Consecutive
R-wave deflections are detected from the electrocardiogram and used to determine the
individual heart beat intervals. The outlying measurements are then removed and the
remaining data is interpolated. The processed data is then characterised using timefrequency
analysis and specific features are determined. Lastly, these features are used
as a basis in a classification system. The results are then compared to the known patient
conditions and the effectiveness of the screening procedure is determined.