Left Ventricular Wall Motion Estimation by Active Polynomials for Acute Myocardial Infarction Detection

Kıranyaz, Serkan; Degerli, Aysen; Hamid, Tahir; Mazhar, Rashid; Ahmed, Rayyan; Abouhasera, Rayaan; Zabihi, Morteza; Malik, Junaid; Hamila, Ridha; Gabbouj, Moncef

doi:10.1109/access.2020.3038743

Cited by 31 publications

(22 citation statements)

References 41 publications

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“…Contrary to the recent studies proposed for single-view, this is the first study that accomplishes a multi-view MI detection for a reliable and robust diagnosis. Moreover, this study shows that the threshold-based APs method in [34] can significantly be improved by using an ML-based approach even for singleview MI detection. The experimental results show that the detection performance has increased with the proposed approach in single-view echocardiography by 2.50% and 7.35% for the sensitivity metric in A4C and A2C views, respectively.…”

Section: Discussionmentioning

confidence: 88%

“…The MI detection results are presented in Table III. In A4C view echocardiography, the prior approach with the threshold-based APs method [34] achieves 86.25% sensitivity with a specificity level of 77.08%. The results indicate that imposing ML into the algorithm generally outperforms the threshold-based APs method in [34] by the classifiers utilized in this study.…”

Section: Resultsmentioning

confidence: 99%

“…Active Polynomials [34] to extract the endocardial boundary of the LV wall. Echocardiography is usually subjected to a high level of noise, and during acquisition, some parts of the chamber walls might be missing or out of view.…”

Section: A Endocardial Boundary Extraction By Active Polynomialsmentioning

confidence: 99%

“…APs provide a robust and reliable segmentation and tracking of the LV wall, where their formation is illustrated in Figure 4. A brief summary will be presented next and the details of the method can be found in [34]. In the first stage of the APs formation, the Ridge Polynomials (RPs) are formed on the LV wall.…”

Section: A Endocardial Boundary Extraction By Active Polynomialsmentioning

confidence: 99%

“…In this study, in order to overcome the aforementioned limitations of the motion estimation algorithms, we use the Active Polynomials (APs) [34] that constraint the active contours to achieve robust segmentation and tracking of the endocardial boundary of the LV wall. In our previous study [34], we proposed a single-view MI detection approach by thresholding the maximum displacement of APs in A4C view echocardiography. However, setting a fixed threshold is never guaranteed to be optimal for decision-making.…”

Section: Introductionmentioning

confidence: 99%

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Early Detection of Myocardial Infarction in Low-Quality Echocardiography

et al. 2021

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Myocardial infarction (MI) is the leading cause of mortality in the world that occurs due to a blockage of the coronary arteries feeding the myocardium. An early diagnosis of MI and its localization can mitigate the extent of myocardial damage by facilitating early therapeutic interventions. Following the blockage of a coronary artery, the regional wall motion abnormality (RWMA) of the ischemic myocardial segments is the earliest change to set in. Echocardiography is the fundamental tool to assess any RWMA. Assessing the motion of the left ventricle (LV) wall only from a single echocardiography view may lead to missing the diagnosis of MI as the RWMA may not be visible on that specific view. Therefore, in this study, we propose to fuse apical 4-chamber (A4C) and apical 2chamber (A2C) views in which a total of 11 myocardial segments can be analyzed for MI detection. The proposed method first estimates the motion of the LV wall by Active Polynomials (APs), which extract and track the endocardial boundary to compute myocardial segment displacements. The features are extracted from the A4C and A2C view displacements, which are fused and fed into the classifiers to detect MI. The main contributions of this study are 1) creation of a new benchmark dataset by including both A4C and A2C views in a total of 260 echocardiography recordings, which is publicly shared with the research community, 2) improving the performance of the prior work of threshold-based APs by a Machine Learning based approach, and 3) a pioneer MI detection approach via multi-view echocardiography by fusing the information of A4C and A2C views. Experimental results show that the proposed method achieves 90.91% sensitivity and 86.36% precision for MI detection over multi-view echocardiography.

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Section: Discussionmentioning

confidence: 88%

Section: Resultsmentioning

confidence: 99%

Section: A Endocardial Boundary Extraction By Active Polynomialsmentioning

confidence: 99%

Section: A Endocardial Boundary Extraction By Active Polynomialsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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