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The paper proposes identification of eight morphologically different pulse waveforms obtained from cardiovasographic observations on the radial pulse. Eight patterns correlated with normal subjects and those suffering from diseases of heart, lungs and liver are analyzed by genotyping of their independent components under the umbrella of parallel multiclass support vector architecture. The algorithm is computationally faster on the basis of its lower number of support vectors (10%) besides providing higher accuracy (81.6%) as compared with parallel multiclass architecture. The confusion parameters for Fast ICA based SVM suggest 3% improvement in the sensitivity and specificity causing similar improvements in the F scores and positive predictive value with a decrease in the negative likelihood ratio indicating a reduction in the number of false positives

Impedance Plethysmographic (IP) observations from the arterial pulse forms a powerful tool for deciphering various cardiovascular diseases. However, a major bottleneck for applying this technique effectively is the assignment of variable waveform morphology to its respective diseases. Rationale of this work is to investigate Non linear and Dynamic Time Warping (DTW) approaches for classifying Impedance Plethysmographic (IP) waveforms. Our adaptation is two fold, firstly, where we establish the IP waveforms as a dynamic system using embedding dimension. Analysis of IP waveforms reveal the existence of eight different patterns corresponding to normal subjects and those suffering from diseases of heart, liver and lungs. Secondly we take up the task of classification of these patterns with template matching using DTW technique. Parametric evaluations reveal that IP waveforms exhibit dynamical nature in a three dimensional attractor. This observation is further validated by exhibition of determinism in reccurrence plots and by parameters determined using recurrence quantification analysis. Classification of the IP patterns by DTW on 1200 samples selected meticulously from 5628 patterns shows an average accuracy of 94%, 95% sensitivity, high statistical predictive values and kappa value of 0.9314 for the eight different classes.

Radial Pulse forms the most basic and essential physical sign in clinical medicine. The paper proposes the application of crisp and fuzzy clustering algorithms under supervised and unsupervised learning scenarios for identifying non-trivial regularities and relationships of the radial pulse patterns obtained by using the Impedance Plethysmographic technique. The objective of our paper is to unearth the hidden patterns to capture the physiological variabilities from the arterial pulse for clinical analysis, thus providing a very useful tool for disease characterization. A variety of fuzzy algorithms including Gustafson-Kessel (GK) and Gath-Geva (GG)have been intensively tested over a diverse group of subjects and over 4855 data sets. Exhaustive testing over the data set show that about 80 % of the patterns are successfully classified thus providing promising results. A Rank Index of 0.7739 is obtained under supervised learning, which provides an excellent conformity of our process with the results of plethysmographic experts. A correlation of the patterns with the diseases of heart, liver and lungs is judiciously performed.

Impedance cardio-vasography (ICVG) serves as a non-invasive screening procedure prior to invasive and expensive angiographic studies. Parameters like Blood Flow Index (BFI) and Differential Pulse Arrival Time (DPAT) at different locations in both lower limbs are computed from impedance measurements on the Impedance Cardiograph. A Backpropagation neural network is developed which uses these parameters for the diagnosis of peripheral vascular diseases such as Leriche's syndrome. The target outputs at the various locations are provided to the network with the help of a medical expert. The paper proposes the use of Principal Component Analysis (PCA) based Backpropagation network where the variance in the data is captured in the first seven principal components out of a set of fourteen features. Such a Backpropagation algorithm with three hidden layers provides the least mean squared error for the network parameters. The results demonstrated that the elimination of correlated information in the training data by way of the PCA method improved the networks estimation performance. The cases of arterial Narrowing were predicted accurately with PCA based technique than with the traditional Backpropagation Technique. The diagnostic performance of the neural network to discriminate the diseased cases from normal cases, evaluated using Receiver Operating Characteristic (ROC) analysis show a sensitivity of 95.5% and specificity of 97.36% an improvement over the performance of the conventional Backpropagation algorithm. The proposed approach is a potential tool for diagnosis and prediction for non-experts and clinicians.