Arrhythmia classification using local Holder exponents and support vector machine - Joshi, Aniruddha; Rajshekhar; Chandran, S.; Phadke, S.; Jayaraman, Valadi K.; Kulkarni, B. D. (1st International Conference on Pattern Recognition and Machine Intelligence, 2005)
We propose a novel hybrid Holder-SVM detection algorithm for arrhythmia classification. The Holder exponents are computed efficiently using the wavelet transform modulus maxima (WTMM) method. The hybrid system performance is evaluated using the benchmark MIT-BIH arrhythmia database. The implemented model classifies 160 of Normal sinus rhythm, 25 of Ventricular bigeminy, 155 of Atrial fibrillation and 146 of Nodal (A-V junctional) rhythm with 96.94% accuracy. The distinct scaling properties of different types of heart rhythms may be of clinical importance.
1st International Conference on Pattern Recognition and Machine Intelligence, Statist Inst Kolkata, Kolkata, INDIA, DEC 20-22, 2005

Fault diagnosis using dynamic time warping - Rajshekhar; Gupta, Ankur; Samanta, A. N.; Kulkarni, B. D.; Jayaraman, Valadi K. (Pattern Recognition and Machine Intelligence, Proceedings, 2007)
Owing to the superiority of Dynamic Time Warping as a similarity measure of time series, it can become an effective tool for fault diagnosis in chemical process plants. However, direct application of Dynamic Time Warping can be computationally inefficient, given the complexity involved. In this work we have tackled this problem by employing a warping window constraint and a Lower Bounding measure. A novel methodology for online fault diagnosis with Dynamic Time Warping has been suggested and its performance has been investigated using two simulated case studies.
2nd International Conference on Pattern Recognition and Machine Intelligence, Calcutta, INDIA, DEC 18-22, 2007