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Vol: 51(65) No: 4 / December 2006 

Unified Neural Network-based Adaptive ECG Signal Analysis and Compression
Sandor M. Szilagyi
Faculty of Technical and Human Science, Sapientia – Hungarian Science University of Transylvania, Targu Mures, Romania, 540053 Targu Mures, P-ta Trandafirilor nr. 61, phone: (40) 265-206210, e-mail: szs@ms.sapientia.ro
Laszlo Szilagyi
Faculty of Technical and Human Science, Sapientia – Hungarian Science University of Transylvania, Targu Mures, Romania, 540053 Targu Mures, P-ta Trandafirilor nr. 61, phone: (40) 265-206210, e-mail: lalo@ms.sapientia.ro
David Iclanzan
Faculty of Mathematics and Computer Science, University of Babes-Bolyai, 400084 Str. M. Kogalniceanu nr. 1, Cluj Napoca, Romania, phone: (40) 264-405300/5240, e-mail: iclanzand@yahoo.com
Zoltan Benyo
Department of Control Engineering and Information Technology, Budapest University of Technology and Economics, Magyar Tudosok krt. 2, 1117 Budapest, Hungary, phone: (36) 1-463-1410, e-mail: benyo@iit.bme.hu


Keywords: medical applications, real-time systems, unified neural network, support vector machine, adaptive signal processing, biological models.

Abstract
This paper presents an adaptive, iteratively functioning and support vector machine (SVM)–based ECG signal processing method. After a conventional pre-filtering step, the characteristic waves (QRS, P, T) from the ECG signal are localized. The implemented event estimation and recognition method uses an SVM-based unified neural network (UNN) in order to determine the most relevant parameters. A UNN-based preliminary ECG analyzer system has been created to reduce the searching space of the optimization algorithm. The obtained model parameters were determined by a relation between objective function minimization and robustness of the solution. The gained information allows an iterative filtering in permanent concordance with the aimed processing manner. Using these methods for one channel records from the MIT-BIH database, the detection rate of QRS complexes is above 99.9%. The negative influence of various noise types, like 50/60 Hz power line, abrupt baseline shift or drift, and low sampling rate was almost completely eliminated. The obtained wave locations and types can form a solid base to improve diagnosis performance in clinical environment.

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