“Analysis on CBIR System for ECG Reports” 
Raghukumar B S1, Naveen B2
1Raghukumar B S *, Electronics and communication engineering, BGS Institute of Technology affiliated to Adichunchanagiri University, Mandya, India.
2Dr Naveen B, Electronics and communication engineering, BGS Institute of Technology affiliated to Adichunchanagiri University, Mandya, India.
Manuscript received on May 29, 2020. | Revised Manuscript received on June 22, 2020. | Manuscript published on June 30, 2020. | PP: 586-592 | Volume-9 Issue-5, June 2020. | Retrieval Number: C6229029320/2020©BEIESP | DOI: 10.35940/ijeat.C6229.069520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Predicting the reason and rate of accuracy on heart attacks from ECG reports is a major obsession. The automated analysis technique will age out the problems of common people in understanding the cause for heart attack. This approach has put a serious discussion platform for the analysis of a CBIR system for ECG reports. As the brisk growth of securable image information and a maximum requirement for data documentation indexing and rectification, many scholars, researchers, and scientists worked a lot on the ECG graph report. The aim of this work is to offer a comparative analysis of the several techniques and methods that were used and applied to extricate features from ECG graph reports. Comparison analysis will help the researches and scholars to choose a suitable technique or method for future scope. Several applications of feature extraction and verification are done by many types of research such as heart attack identification based on the feature. Heart attack searching by a doctor is old and required a very long time to identify a stroke. Detecting heart attack from the ECG report is demanding due to misconception, negligence, delay, the difference among the people based on age, gender and so on. Therefore the important work is to furnish an analysis of the accuracy of widely used methods by scholars and researchers in extricating features of the ECG graph reports. Finally, the results of various methods for extricating a feature from the ECG graph reports analyzed vigorously and comparison analysis effort helps the researches to slip out the time complexity in searching for different integration tasks.
Keywords: ECG reports, feature extraction, heart attack, predictive analytics.