Left Ventricular Wall Motion Estimation by Active Polynomials for Acute Myocardial Infarction Detection
Echocardiogram (echo) is the earliest and the primary tool for identifying regional wall motion abnormalities (RWMA) in order to diagnose myocardial infarction (MI) or commonly known as heart attack. This paper proposes a novel approach, Active Polynomials, which can accurately and robustly estimate...
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doaj-4223d54053cb455d92fe4a389c12c44d2021-03-30T04:55:20ZengIEEEIEEE Access2169-35362020-01-01821030121031710.1109/ACCESS.2020.30387439261387Left Ventricular Wall Motion Estimation by Active Polynomials for Acute Myocardial Infarction DetectionSerkan Kiranyaz0https://orcid.org/0000-0003-1551-3397Aysen Degerli1https://orcid.org/0000-0002-9478-033XTahir Hamid2Rashid Mazhar3https://orcid.org/0000-0003-4255-8996Rayyan El Fadil Ahmed4https://orcid.org/0000-0003-2218-152XRayaan Abouhasera5https://orcid.org/0000-0001-7261-9249Morteza Zabihi6https://orcid.org/0000-0002-6758-4384Junaid Malik7Ridha Hamila8https://orcid.org/0000-0002-6920-7371Moncef Gabbouj9https://orcid.org/0000-0002-9788-2323Department of Electrical Engineering, Qatar University, Doha, QatarFaculty of Information Technology and Communication Sciences, Tampere University, Tampere, FinlandHeart Hospital, HMC, Doha, QatarHeart Hospital, HMC, Doha, QatarDepartment of Electrical Engineering, Qatar University, Doha, QatarDepartment of Electrical Engineering, Qatar University, Doha, QatarDepartment of Electrical Engineering, Qatar University, Doha, QatarFaculty of Information Technology and Communication Sciences, Tampere University, Tampere, FinlandDepartment of Electrical Engineering, Qatar University, Doha, QatarFaculty of Information Technology and Communication Sciences, Tampere University, Tampere, FinlandEchocardiogram (echo) is the earliest and the primary tool for identifying regional wall motion abnormalities (RWMA) in order to diagnose myocardial infarction (MI) or commonly known as heart attack. This paper proposes a novel approach, Active Polynomials, which can accurately and robustly estimate the global motion of the Left Ventricular (LV) wall from any echo in a robust and accurate way. The proposed algorithm quantifies the true wall motion occurring in LV wall segments so as to assist cardiologists diagnose early signs of an acute MI. It further enables medical experts to gain an enhanced visualization capability of echo images through color-coded segments along with their “maximum motion displacement” plots helping them to better assess wall motion and LV Ejection-Fraction (LVEF). The outputs of the method can further help echo-technicians to assess and improve the quality of the echocardiogram recording. A major contribution of this study is the first public echo database collection composed by physicians at the Hamad Medical Corporation Hospital in Qatar. The so-called HMC-QU database will serve as the benchmark for the forthcoming relevant studies. The results over HMC-QU dataset show that the proposed approach can achieve 87.94% accuracy, 92.86% sensitivity and 87.64% precision in MI detection even though the echo quality is quite poor and the temporal resolution is low.https://ieeexplore.ieee.org/document/9261387/Echocardiogramleft ventricular wall motion estimationmyocardial infarction |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Serkan Kiranyaz Aysen Degerli Tahir Hamid Rashid Mazhar Rayyan El Fadil Ahmed Rayaan Abouhasera Morteza Zabihi Junaid Malik Ridha Hamila Moncef Gabbouj |
spellingShingle |
Serkan Kiranyaz Aysen Degerli Tahir Hamid Rashid Mazhar Rayyan El Fadil Ahmed Rayaan Abouhasera Morteza Zabihi Junaid Malik Ridha Hamila Moncef Gabbouj Left Ventricular Wall Motion Estimation by Active Polynomials for Acute Myocardial Infarction Detection IEEE Access Echocardiogram left ventricular wall motion estimation myocardial infarction |
author_facet |
Serkan Kiranyaz Aysen Degerli Tahir Hamid Rashid Mazhar Rayyan El Fadil Ahmed Rayaan Abouhasera Morteza Zabihi Junaid Malik Ridha Hamila Moncef Gabbouj |
author_sort |
Serkan Kiranyaz |
title |
Left Ventricular Wall Motion Estimation by Active Polynomials for Acute Myocardial Infarction Detection |
title_short |
Left Ventricular Wall Motion Estimation by Active Polynomials for Acute Myocardial Infarction Detection |
title_full |
Left Ventricular Wall Motion Estimation by Active Polynomials for Acute Myocardial Infarction Detection |
title_fullStr |
Left Ventricular Wall Motion Estimation by Active Polynomials for Acute Myocardial Infarction Detection |
title_full_unstemmed |
Left Ventricular Wall Motion Estimation by Active Polynomials for Acute Myocardial Infarction Detection |
title_sort |
left ventricular wall motion estimation by active polynomials for acute myocardial infarction detection |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
Echocardiogram (echo) is the earliest and the primary tool for identifying regional wall motion abnormalities (RWMA) in order to diagnose myocardial infarction (MI) or commonly known as heart attack. This paper proposes a novel approach, Active Polynomials, which can accurately and robustly estimate the global motion of the Left Ventricular (LV) wall from any echo in a robust and accurate way. The proposed algorithm quantifies the true wall motion occurring in LV wall segments so as to assist cardiologists diagnose early signs of an acute MI. It further enables medical experts to gain an enhanced visualization capability of echo images through color-coded segments along with their “maximum motion displacement” plots helping them to better assess wall motion and LV Ejection-Fraction (LVEF). The outputs of the method can further help echo-technicians to assess and improve the quality of the echocardiogram recording. A major contribution of this study is the first public echo database collection composed by physicians at the Hamad Medical Corporation Hospital in Qatar. The so-called HMC-QU database will serve as the benchmark for the forthcoming relevant studies. The results over HMC-QU dataset show that the proposed approach can achieve 87.94% accuracy, 92.86% sensitivity and 87.64% precision in MI detection even though the echo quality is quite poor and the temporal resolution is low. |
topic |
Echocardiogram left ventricular wall motion estimation myocardial infarction |
url |
https://ieeexplore.ieee.org/document/9261387/ |
work_keys_str_mv |
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