Impedance cardiography is a noninvasive technique for estimation of stroke volume, systolic time intervals, and some other cardiovascular indices. These estimations require error-free detection of the B, C, and X points of impedance cardiogram (ICG), as markers of aortic valve opening, peak aortic blood velocity, and aortic valve closure, respectively. Based on an empirical examination of the morphological variations in the ICG waveforms, a technique for automatic beat-to-beat detection of these points is developed. It uses wavelet-based denoising for suppression of respiratory artifacts to avoid restrictions on breathing during the recording. It uses R peaks of ECG as reference points to avoid inter-cycle smearing and multiple time-domain waveform features to reduce errors due to morphological variations. The technique is evaluated on simultaneously acquired and time-aligned ICG, ECG, and Doppler echocardiogram recordings from subjects with normal-health and subjects with cardiovascular disorders. Compared to the earlier techniques, the proposed technique detects the points with low bias and precision errors. The means of differences, as referred to the mean R-R interval, in the estimation of R-C, R-B, R-X, and B-X intervals with the corresponding measurements from Doppler echocardiograms as the reference were 0.5%, 0.3%, 0.5% and 3.0% respectively. The corresponding standard deviations of differences were 1.3%, 1.3%, 5.8% and 6.0%. The proposed technique may help in improving the acceptability of impedance cardiography for diagnosis of cardiovascular disorders.

Impedance cardiography is a noninvasive technique for monitoring the variation in thoracic impedance during cardiac cycle. Estimation of the stroke volume and other cardiovascular indices using impedance cardiography requires error-free detection of characteristic points in the impedance cardiogram (ICG). A technique for automatic detection of ICG characteristic points using R peaks in ECG as reference is presented. It does not require pre-processing of the ICG signal for baseline correction and adjustment of detection parameters. The technique is validated using Doppler echocardiography as a reference technique, by recording ICG and ECG signals simultaneously along with velocity profile of blood flow at the level of left ventricular outflow tract. Application of the technique on the recordings from healthy subjects in pre-exercise and post-exercise conditions and from cardiac patients under rest condition showed a very low detection error.

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Impedance cardiography is a noninvasive technique for estimation of stroke volume (SV), based on monitoring the variation in the thoracic impedance during the cardiac cycle. The current SV calculation methods use parameters obtained by ensemble averaging of the waveform along with equations based on simplified models of the thoracic impedance and aortic blood flow profile. They often result in inconsistent estimates when compared with the reference techniques. An investigation is carried out for beat-by-beat monitoring of SV using an artificial neural network with a set of input parameters as used in the different SV equations. A threelayer feed-forward neural network is used and the impedance cardiogram parameters are obtained using an algorithm for beatby-beat automatic detection of the characteristic points. The training and testing are carried out using the SV values obtained from Doppler echocardiography as a reference technique after alignment of the signals from the two techniques. Results from the data from six subjects with recordings under rest and postexercise conditions show the neural network based estimation to be more effective than the estimations based on SV equations.