Teaching Signal Processing to the Medical Profession

Knowledge of signal processing is very important for medical students. A medical signal may be used for monitoring, constructing an image, or for extracting the numerical quantity of a parameter. This information forms a basis for medical decisions. However, the processing of the signal may lead to distortion and an incorrect interpretation. The present article describes an educational practical for first year medical students. It uses the electrocardiogram, which can be obtained easily, as a convenient example of a medical signal. The practical was developed at the VU University Amsterdam and summarizes the elementary concepts of signal processing

High-resolution magnetic mapping of PR-interval phenomena of normal subjects

In this experimental study we measured the magnetocardiograms (MCGs) of 17 normals at 42 measuring sites in a plane near the chest. The recordings were made in an unshielded environment with a single channel second-order SOUID gradiometer. From these recordings spatial maps were constructed of the atrial depolarisation and repolarisation. The two series of maps were found to be dipolar, but opposite in sign for 14 of the 17 subjects. The relatively small change in the amplitude and pattern of the atrial repolarisation maps built up out confidence in the method of subtracting a computed mean repolarisation map from the maps obtained during the last 40 ms before the QRS-onset. This method of separating His-Purkinje activity from atrial repolarisation led to distinct dipolar patterns in 9 out of the 17 cases. The direction and location of the associated electric current dipole was in good agreement with what can be expected electrophysiologically for His-Purkinje activation

Glucose tolerance and other determinants of cardiovascular autonomic function: the Hoorn Study.

Aims/hypothesis. Currently, three categories of measures are used to assess cardiovascular autonomic dysfunction: measures of the Ewing-test, measures of heart-rate variability, and measures of baroreflex sensitivity. We studied the determinants of these measures obtained from cardiovascular autonomic function tests in the Hoorn Study. Methods. The study group (n = 631) consisted of a glucose-tolerance-stratified sample from a 50- to 75- year-old group of people. Cardiac cycle duration (RR interval) and continuous finger arterial pressure were measured under three conditions: during (a) spontaneous breathing, (b) six deep breaths over one minute, and (c) an active change in position from lying to standing. From these readings, ten measures of autonomic function were assessed (three Ewing, six heart-rate variability and one baroreflex sensitivity). As possible determinants we considered age, sex, glucose tolerance, cardiovascular disease, use of anti- hypertensive drugs, anthropometric factors, metabolic factors and lifestyle factors. Results. Multivariate analysis showed that eight of ten cardiovascular autonomic function measures were most strongly associated with glucose tolerance. Furthermore, measures were moderately associated with age, sex, waist-to-hip ratio, use of anti-hypertensive drugs, and insulin. The measures were weakly associated With coronary artery disease but not with lipids. The strongest determinants seemed to differ between subjects with and without diabetes: in the non-diabetic subjects the most strongly associated were age and use of anti-hypertensive drugs and in subjects with diabetes, insulin. No consistent differences in association between the three categories of measures were observed. Conclusion/interpretation. The strongest determinants of autonomic function were age, presence of diabetes and use of anti-hypertensive drugs