Date of Award
Doctor of Philosophy in Electrical Engineering
Electrical and Computer Engineering
The physiological condition of the human cardiovascular system is primarily determined from the electrocardiogram (ECG) and blood pressure signals. Diagnostic and therapeutic medical procedures and the operation of various medical devices often rely on the temporal location of various events observed in these signals. The QRS complex (R wave) is one distinguishing characteristic of the ECG waveform; whereas the systolic peak, upstroke and the dicrotic notch are the most prominent events in the arterial blood pressure signal. Detection of these waveform characteristics may be used for calculating I heart rate and the systolic time intervals including the pre-ejection period (PEP), left ventricular ejection time (LVET) and electromechanical systole (QS2).
This report describes an algorithm which accurately and consistently locates the dicrotic notch in the arterial blood pressure waveform for a range of heart rates, arrhythmias and irregular pressure waveforms (including baseline drift, catheter artifact, signal damping and noise) using the dyadic wavelet transform (DyWT). Simultaneous occurrences of minima in the DyWT across several successive dyadic scales indicates a transient in the pressure waveform, from which the corresponding temporal location of the dicrotic notch is determined for each cardiac cycle.
The dyadic wavelet transform scheme for dicrotic notch detection has been tested on arterial blood pressure waveforms (radial, femoral, and axillary) with various heart rates, ranging from 40 to 140 beats per minute (bpm). Algorithm performance was evaluated using 71 patient data files from the Massachusetts General Hospital (MGH) database which includes simultaneous ECG and arterial pressure recordings. Four criteria were used to indicate detection performance: sensitivity, positive productivity, false positive rate and false negative rate. The accuracy of the proposed DyWT based dicrotic notch detection algorithm outperformed five previously published detection algorithms in terms of each of the four performance criteria. The Dy WT based detection algorithm achieved a sensitivity of 84%, a positive productivity of 85%, a false positive rate of 15 % and a false negative rate of 16% when tested 72 patient arterial blood pressure files of various waveform types, illustrative of a clinical environment. The next highest performers achieved a sensitivity as high as 66%, positive productivity of 72%, a false positive rate as low as 25%, and a false negative rate of 34%.
Antonelli, Lynn T., "Pressure Event Detection Based on the Dyadic Wavelet Transform" (1995). Open Access Dissertations. Paper 778.