e-journal

Spatiotemporal Differentiation of Myocardial Infarctions

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Myocardial infarction (MI), also known as a heart attack, is the leading cause of death in the U.S. It often occurs due to the occlusion of coronary arteries, thereby leading to insufficient blood and oxygen supply that damage cardiac muscle cells. Because blood vessels are branching throughout the heart, MI occurs at different spatial locations (e.g., anterior and inferior portions) of the heart. The spatial location of the diseased is rupts normal excitation and propagation of cardiac electrical activity in space and time. Most previous studies focused on the relationships between disease and time-domain biomarkers from 12-lead ECG signals (e.g., Q wave, QT interval, ST elevation/depression,
T wave). Few, if any, previous approaches investigated how the spatial location of diseases will alter cardiac vectorcardiogram (VCG) signals in both space and time. This paper presents a novel spatiotemporal warping approach to quantify the dissimilarity of disease-altered patterns in 3-lead spatiotemporal VCG signals. The hypothesis testing shows that there are significant spatiotemporal differences between healthy control, MI-anterior, MI-anterior-septal, MI-anterior-lateral, MI-inferior, and MI-inferior-lateral. Furthermore, we optimize the embedding of each functional recording as a feature vector in the high-dimensional space that preserves the dissimilarity distance matrix. This novel spatial embedding approach facilitates the construction of classification models and yields an averaged accuracy of 95.1% for separating MIs and Healthy Controls (HCs) and an averaged accuracy of 95.8% in identifying anterior-related MIs and inferior-related MIs.
Note to Practitioners—This paper is motivated by our previous findings that cardiovascular diseases, e.g., myocardial infarction, alter cardiac electrical activity in space and time. However, most
of existing approaches focused on the characteristics of time-domain ECG signals (e.g., Q wave, QT interval, ST elevation/depression, T wave). This paper suggests a novel spatiotemporal warping
approach to quantify the dissimilarity of disease-altered patterns in 3-lead space-time VCG signals. We optimize the embedding of each space-time VCG recording as a feature vector in the high-dimensional
space that preserves the dissimilarity distance matrix. In the practice, when a new VCG recording is presented, the pattern dissimilarity will be measured against the database of N patients. Then, a new row and column will be obtained in the warping matrix, and a new feature vector will be embedded in the high-dimensional space. Finally, the classification model will predict cardiac conditions with this feature vector.

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Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING

No. Panggil

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Penerbit

New York : IEEE., 2013

Deskripsi Fisik

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, VOL. 10, NO. 4, OCTOBER 2013

Bahasa

English

ISBN/ISSN

1545-5955

Klasifikasi

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Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

VOL. 10, NO. 4, OCTOBER 2013

Subjek

TEKNIK

Info Detail Spesifik

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Pernyataan Tanggungjawab

Yuli/Agus

Versi lain/terkait

Lampiran Berkas

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