Independent component analysis: algorithms and applications
Neural Networks
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
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Aortic pulse wave velocity is an independent predictive indicator for all cause mortality and cardiovascular morbidity. Unfortunately it is only invasively accessible and thus the A. carotis-A. femoralis pulse wave velocity (cfPWV) is recommended as a non-invasive substitute. This work presents a model based analysis method for the beat-to-beat online determination of an arbitrary, peripheral pulse transit time (PTT). The method is based on the recording of a three lead electrocardiography (ECG) and of pulse waves (PW) at a peripheral site such as the A. carotis by means of a multiple sensor array. The two modules for the signal acquisition work autonomously but time-wise simultaneously and transmit the data via a radio unit to the central processing unit. There the algorithms for the pulse transit time determination exploit these signals. In doing so the main focus is on an efficient implementation to assure real-time usability. The evaluation of the developed modules and algorithms was done in two separate trials. First the algorithms were tested offline against manual signal annotation using invasive data previously recorded to proof their accuracy. The resulting mean differences in PTT for pulse waves assessed at the aortic root and the aortic bifurcation are 2.86ms (4.72ms SD) and 2.00ms (2.28ms SD). To evaluate the whole system integrity in a second step online measurements on probands were carried out and compared to data from literature. The trials resulted in a mean PTT of 174.6ms (17.7ms SD) for the A. radialis and of 81.9ms (13.2ms SD) for the A. carotis. The results suggest that the method may be useful and deployable at general practitioners (GP) and in ambulatory care of (chronic) cardiovascular diseases.