Corrected formula for estimating peripheral blood flow by impedance plethysmography

Electrical impedance signal analysis in assessing the possibility of non-invasive diagnosis of knee osteoarthritis

Knee osteoarthritis (OA) is a degenerating disorder that leads to pain, disability and dependence. Although significant numbers of elderly people are affected by this irreversible damage, not many non-invasive methods have been found that can detect onset of OA. The traditional x-ray has the disadvantage of detecting a problem only after many changes have taken place. Others, such as MRI and ultrasound, are either expensive or unsuitable for mass screening and repeated use. In this paper, an attempt has been made to study the usefulness of electrical impedance plethysmography (EIP) in non-invasive diagnosis of knee OA. In two experiments on 10 OA knees and eight control knees in groups aged 45 - 65 years (OA group: 62.40 +/- 3.47 years, controls: 53.38 +/- 8.55 years), knee swing (active flexion and extension of leg in sitting position, KS) and normal walking (WN) electrical impedance changes (DeltaZ) around the knee were analysed. The results indicate that there is significant difference in amplitudes of signals. Difference in mean of variances of two groups was significant (p < 0.05) for KS and WN. The difference in the mean rms values was also significant (p < 0.05) for KS and WN. Impedance changes suggest that EIP signal around the knee have the potential for non-invasive diagnosis of knee OA.

Cancellation of respiratory artifact in impedance cardiography

Impedance cardiography is a noninvasive technique for monitoring stroke volume, based on sensing variation in the thoracic impedance, z(t), due to blood flow. In this technique, first derivative of impedance, dz/dt, is used to calculate two parameters: ventricular ejection time and (-dz/dt)<inf>max.</inf>Respiration and motion artifacts cause base line drift in the sensed impedance waveform, mainly during exercise, and this drift results in errors in the estimation of the two parameters. Ensemble averaging of z(t) signal suppresses motion artifacts but it introduces distortion in the signal. In this study, simultaneously acquired respiratory signal is used for cancellation of corrupting respiratory artifact from the recorded z(t) signal, using adaptive noise cancellation technique.