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.

In a study comprising 100 patients referred to a surgical clinic with symptoms suggestive of gastro-oesophageal reflux disease the value of different diagnostic procedures was investigated. Positive acid perfusion and 24-h pH tests were the commonest findings. Forty-nine per cent showed a normal oesophageal mucosa or diffuse oesophagitis at endoscopy. The severity of heartburn and regurgitation did not differ between patients with normal oesophageal mucosa and oesophagitis of various severities. The severity of macroscopic oesophagitis was significantly correlated to the total reflux time, the presence of reflux or a hiatal hernia at radiology, an open cardia or reflux at endoscopy, pressure transmission or reflux and low lower oesophageal sphincter pressure at manometry. Gastric hypersecretion was found in 66% of the patients. Gastric acid secretion was not correlated to the severity of oesophagitis or to the findings at 24-h pH test. In patients with severe oesophagitis the sensitivity for radiologic, manometric, and endoscopic signs of incompetence of the gastro-oesophageal junction was 94%.

Clothing with conductive textiles for health care applications has in the last decade been of an upcoming research interest. An advantage with the technique is its suitability in distributed and home health care. The present study investigates the electrical properties of conductive yarns and textile electrodes in contact with human skin, thus representing a real ECG-registration situation. The yarn measurements showed a pure resistive characteristic proportional to the length. The electrodes made of pure stainless steel (electrode A) and 20% stainless steel/80% polyester (electrode B) showed acceptable stability of electrode potentials, the stability of A was better than that of B. The electrode made of silver plated copper (electrode C) was less stable. The electrode impedance was lower for electrodes A and B than that for electrode C. From an electrical properties point of view we recommend to use electrodes of type A to be used in intelligent textile medical applications.