Sri Hartati

Work place: Department of Computer Science and Electronics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Yogyakarta, Indonesia



Research Interests: Decision Support System, Information Systems, Medical Image Computing, Image Processing, Systems Architecture, Operating Systems, Pattern Recognition, Computer systems and computational processes, Medical Informatics


Sri Hartati is a Professor of Computer Science in Universitas Gadjah Mada, Indonesia. She obtained his bachelor degree from Electronics and Instrumentation in Universitas Gadjah Mada, Indonesia (1986). She obtained his master (M.Sc., 1990) and doctor (Ph.D., 1996) degree from Computer Science in University of New Brunswick, Canada. Now she works as a lecturer and a researchers at Department of Computer Science and Electronics in Universitas Gadjah Mada, Indonesia. Her research interests include Intelligent Systems, Knowledge Based Systems, Reasoning Systems, Expert Systems, Fuzzy Systems, Pattern Recognition Systems, Vision Systems, Natural Language Processing, Decision Support Systems (DSS), Group DSS & Clinical DSS, Medical Computing & Computational Intelligence.

Author Articles
The Feature Extraction to Determine the Wave’s Peaks in the Electrocardiogram Graphic Image

By Darwan Sri Hartati Retantyo Wardoyo Budi Yuli Setianto

DOI:, Pub. Date: 8 Jun. 2017

The electrocardiogram (ECG) will create the characteristic in the form of the wave’s peak pattern. The first peak and the next one in one ECG wave have their own value and names, namely PQRST peaks. The process of feature extraction is very significant to determine the certain pattern. The use of feature extraction will be useful to help to detect certain case, including the determination of PQRST peaks according to the ECG print-out. This study makes a method to determine the ECG peaks (PQRST), the heart rate, and ST-deviation according to the ECG graphic image. The input data is in the form of ECG graphic image which is derived from the ECG 12 lead record. This study employs segmentation method (grayscale and binary), morphology (dilation and erosion), and produce the graphic image which is read as the ECG signal in the pre-processing stage, and use the Pan-Tompkins algorithm for the feature extraction method. The result of the peak determination is validated by cardiologists. The validation shows that the result of up and down deflection computation from the isoelectric of each P, Q, R, S, and T wave has represented the ECG calculation clinically; including the calculation to determine the R-R interval, heart rate, and ST-deviation. 

[...] Read more.
Other Articles