Classifying Ayurvedic Pulse Signals Via Consensus Locally Linear Embedding

In this paper, we present a novel method for analysis of Ayurvedic pulse signals via a recently developed nonlinear dimensionality reduction scheme called Consensus Locally Linear Embedding (C-LLE). Pulse Based Diagnosis (PBD) is a prominent method of disease detection in Ayurveda, the system of Indian traditional medicine. Ample anecdotal evidence suggests that for several conditions, PBD, based on sensing changes in the patient’s pulse waveform, is superior to conventional allopathic diagnostic methods. PBD is an inexpensive, non-invasive, and painless method; however, a lack of quantification and standardization in Ayurveda, and a paucity of expert practitioners, has limited its widespread use. The goal of this work is to develop the first Computer-Aided Diagnosis (CAD) system able to distinguish between normal and diseased patients based on their PBD. Such a system would be inexpensive, reproducible, and facilitate the spread of Ayurvedic methods. Digitized Ayurvedic pulse signals are acquired from patients using a specialized pulse waveform recording device. In our experiments we considered a total of 50 patients. The 50 patients comprised of two cohorts obtained at different frequencies. The first cohort comprised 24 patients that were normal or diseased (slipped disc (backache), stomach ailments) while the second consists of a set of 26 patients who were normal or diseased (diabetic, with skin disorders, slipped disc (backache) and stress related headaches). In this study, we consider the C-LLE scheme which non-linearly projects the high-dimensional Ayurvedic pulse data into a lower dimensional space where a consensus clustering scheme is employed to distinguish normal and abnormal waveforms. C-LLE differs from other linear and nonlinear dimensionality reduction schemes in that it respects the underlying nonlinear manifold structure on which the data lies and attempts to directly estimate the pairwise object adjacencies in the lower dimensional embedding space. A major contribution of this work is that it employs non-Euclidean similarity measures such as mutual information and relative entropy to estimate object similarity in the high-dimensional space which are more appropriate for measuring the similarity of the pulse signals. Our C-LLE based CAD scheme results in a classification accuracy of 80.57% using relative entropy as the signal distance measure in distinguishing between normal and diseased patients for the first cohort, based on their Ayurvedic pulse signal. For the 500Hz data we got a maximum of 88.34% accuracy with C-LLE and relative entropy as a distance measure. Furthermore, C-LLE was found to outperform LLE, Isomap, PCA across multiple distance measures for both cohorts.