NDLI: Early diagnosis of acute coronary syndromes with automatic ST/T classifier

Content Provider	IEEE Xplore Digital Library
Author	Terzi, Merve Begum Arikan, Orhan Abaci, Adnan Candemir, Mustafa Dedoglu, Mehmet
Copyright Year	2014
Description	Author affiliation: Elektrik ve Elektronik Mühendisliği Bölümü, Bilkent Üniversitesi, Turkey (Terzi, Merve Begum; Arikan, Orhan; Abaci, Adnan; Candemir, Mustafa; Dedoglu, Mehmet)
Abstract	In patients with acute coronary syndrome, temporary chest pains together with changes in ECG ST segment and T wave occur shortly before the start of myocardial infarction. In order to diagnose acute coronary syndromes early, a new technique which detects changes in ECG ST/T sections is developed. As a result of implementing the developed technique to real ECG recordings, it is shown that the proposed technique provides reliable detections. Therefore, the developed technique is expected to provide early diagnosis of acute coronary syndromes which will lead to a significant decrease in heart failure and mortality rates.
Starting Page	1
Ending Page	4
File Size	756804
Page Count	4
File Format	PDF
ISBN	9781479975723
DOI	10.1109/BIYOMUT.2014.7026388
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-10-16
Publisher Place	Turkey
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Heart support vector machine (SVM) Electrocardiogram (ECG) signal classification kernel method Diseases Support vector machines feature detection acute coronary syndrome Neural networks Electrocardiography Myocardium Kernel acute myocardial infarction
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Mr. Siddhartha Mukherjee siddhartha@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	ndl-support@iitkgp.ac.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Libarray of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Ensemble of support vector machines for heartbeat classification

SVM based methods for arrhythmia classification in ECG

ECG codebook model for Myocardial Infarction detection

Classification of Electrocardiogram Signals With Support Vector Machines and Particle Swarm Optimization

Multi-class SVM classification combined with kernel PCA feature extraction of ECG signals

Automatic ECG interpretation via morphological feature extraction and SVM inference nets

An electrocardiogram classification method based on cascade Support Vector Machine

On the Detection of Myocadial Scar Based on ECG/VCG Analysis

Electrocardiogram Analysis with Adaptive Feature Selection and Support Vector Machines

Early diagnosis of acute coronary syndromes with automatic ST/T classifier

Similar Documents

Ensemble of support vector machines for heartbeat classification

SVM based methods for arrhythmia classification in ECG

ECG codebook model for Myocardial Infarction detection

Classification of Electrocardiogram Signals With Support Vector Machines and Particle Swarm Optimization

Multi-class SVM classification combined with kernel PCA feature extraction of ECG signals

Automatic ECG interpretation via morphological feature extraction and SVM inference nets

An electrocardiogram classification method based on cascade Support Vector Machine

On the Detection of Myocadial Scar Based on ECG/VCG Analysis

Electrocardiogram Analysis with Adaptive Feature Selection and Support Vector Machines

Early diagnosis of acute coronary syndromes with automatic ST/T classifier