Severe blood pressure changes are well known in hemodialysis. Detection and prediction of these are important for the well-being of the patient and for optimizing treatment. New noninvasive methods for this purpose are required. The pulse wave transit time technique is an indirect estimation of blood pressure, and our intention is to investigate whether this technique is applicable for hemodialysis treatment. A measurement setup utilizing lower body negative pressure and isometric contraction was used to simulate dialysis-related blood pressure changes in normal test subjects. Systolic blood pressure levels were compared to different pulse wave transit times, including and excluding the cardiac preejection period. Based on the results of these investigations, a pulse wave transit time technique adapted for dialysis treatment was developed and tried out on patients. To determine systolic blood pressure in the normal group, the total pulse wave transit time was found most suitable (including the cardiac preejection period). Correlation coefficients were r = 0.80 +/- 0.06 (mean +/- SD) overall and r = 0.81 +/- 0.16 and r = 0.09 +/- 0.62 for the hypotension and hypertension phases, respectively. When applying the adapted technique in dialysis patients, large blood pressure variations could easily be detected when present. Pulse wave transit time is correlated to systolic blood pressure within the acceptable range for a trend-indicating system. The method's applicability for dialysis treatment requires further studies. The results indicate that large sudden pressure drops, like those seen in sudden hypovolemia, can be detected.

Abstract-Heart murmurs are often the first signs of pathological changes of the heart valves, and they are usually found during auscultation in the primary health care. Distinguishing a pathological murmur from a physiological murmur is however difficult, why an ''intelligent stethoscope'' with decision support abilities would be of great value. Phonocardiographic signals were acquired from 36 patients with aortic valve stenosis, mitral insufficiency or physiological murmurs, and the data were analyzed with the aim to find a suitable feature subset for automatic classification of heart murmurs. Techniques such as Shannon energy, wavelets, fractal dimensions and recurrence quantification analysis were used to extract 207 features. 157 of these features have not previously been used in heart murmur classification. A multi-domain subset consisting of 14, both old and new, features was derived using PudilÕs sequential floating forward selection (SFFS) method. This subset was compared with several single domain feature sets. Using neural network classification, the selected multi-domain subset gave the best results; 86% correct classifications compared to 68% for the first runner-up. In conclusion, the derived feature set was superior to the comparative sets, and seems rather robust to noisy data.

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