Accelerated plethysmographybased enhanced pittaclassification

Abstract

The Accelerated Plethysmography(APG)is a non-invasive optical technique developedforits experimental usage in cardio vascular diseases. The existing traditional cardiovascular diagnostics tools might be replaced by this technique. It uses the second derivativeof Photoplethysmography(PPG)waveformtoevenoutthebaselineandtoisolatethecomponents more visibly as compared to the first derivative. The purpose of this research is to design aHigh Pitta Classifier and to find the features that may relate to the intensified pittalevel. Our focus of research is to analyze Accelerated Plethysmography and optimally selectthose featuresthatmaygivehighaccuracyofenhancedPittalevel.Forthisfisherlineardiscriminant analysisandcorrelationhasbeenemployed.Thefeaturesselectedareclassifiedusingdifferent classificationtechniques.Aclassifierachievinganaccuracyof75%forComparativeGroup1, 75% for Comparative Group 2 and 68.75% for Comparative Group 3 has beendeveloped effectively usingLIBSVM. Artificial Neural Network approach has been used to verify the result further.Comparative Group 1 classifies high Pitta on the basis of effect of Mid-day only. 81.30% accuracy isachieved usingArtificialNeuralNetworkhaving2neuronsinthehiddenlayer.ComparativeGroup2 classifiesonthebasisofmid-dayaswellasdigestionfollowingtheconsumptionofmeals. 87.5%accuracyisachievedusingArtificialNeuralNetworkhaving2neuronsinthehidden layer. This indicates that the consumption of meals also have some role in the enhancedpitta level.ComparativeGroup3classifiesonthebasisofdigestionfollowingtheconsumptionof meals.75% accuracy is achieved using Artificial Neural Network having 6 neurons in thehidden layer.Fromthisstudyitisconcludedthat(i)PittadetectionusingPhotoplethysmographyis feasible (ii) Effect of mid-day is prominent (iii) Effect of consumption of meals is also there(iii) Effect of mid-day is more as compared to consumption ofmeals.