Identification of Patients with Myocardial Infarction

Correa, Raúl; Arini, Pedro David; Correa, L.; Valentinuzzi, Max E.; Laciar, Eric

doi:10.3414/me15-01-0101

Cited by 16 publications

(2 citation statements)

References 19 publications

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“…Acute MI is one of the most common and serious coronary atherosclerotic heart diseases, in which anterior MI is prone to ventricular arrhythmias; inferior MI mostly shows decreased heart rate and atrioventricular block. [5] Patients with the latter disease may develop cardiac rupture, arhythmia, cardiac failure, cardiogenic shock and other complications. Once acute MI occurs, it is supposed to early determine the infarction-associated artery and the stenosis zone.…”

Section: Discussionmentioning

confidence: 99%

Diagnostic value of VCG combined with CDE for acute inferior MI culprit vessel

Zhao

Jiang

2019

DCC

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Objective: To explore the diagnostic value of vectorcardiography (VCG) combined with color Doppler echocardiography (CDE) for acute inferior myocardial infarction (MI) culprit vessel.Methods: A total of 82 patients who had been clinically diagnosed as acute inferior MI and had received coronary arteriography (CAG) were selected as research objects. They received bedside VCG and CDE examinations after admission. Their culprit vessels were analyzed and compared with CAG results to evaluate the diagnostic efficiency.Results: The detection rate of acute inferior MI culprit vessel by VCG combined with CDE was 76.83%, including 18 left circumflex (LCX) branches and 45 right coronary arteries (RCA). The lower left constituent ratio of LCX was 61.11%, which was significantly higher than 20.00% in RCA (p < .05); the lower right constituent ratio of RCA was 71.11%, which was much higher than 11.11% in LCX (p < .05); the sensitivity and the accuracy of RCA culprit vessel judged with frontal plane ST-vector pointing to the lower right quadrant were 71.11% and 53.33% respectively; the diagnostic efficiency of LCX culprit vessel judged with frontal plane ST-vector pointing to the lower left quadrant was 61.11% and 50.00% respectively. The maximum amplitude of the frontal plane in LCX culprit vessel was significantly lower than that in RCA culprit vessel; and the frontal plane vector angle in LCX was significantly greater than that in RCA culprit vessel (p < 0.05).Conclusions: VCG combined with CDE can effectively diagnose acute inferior MI. It has a good diagnostic efficiency by judging culprit vessels according to the frontal plane ST-vector direction.

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Section: Discussionmentioning

confidence: 99%

Diagnostic value of VCG combined with CDE for acute inferior MI culprit vessel

Zhao

Jiang

2019

DCC

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“…The development of algorithms for an automatic classification of vectorcardiographic (VCG) records for the purpose of heart disease recognition helps include VCG among the commonly used diagnostic methods. Based on the facts from recent studies [e.g., Lee et al, 1968 ; Simonson, 1976 ; Ge, 2008 ; Huebner et al, 2010 ; Romero et al, 2010 ; Dehnavi et al, 2011 ; Correa et al, 2016 ; Lingman et al, 2016 ], VCG achieves more accurate results than the standard 12-lead electrocardiographic (ECG) method. Compared to the empirically assessed 12-lead ECG, VCG diagnostics offers a quantitative description of the heart’s electrical field and of the objective view on the heart vector propagation.…”

Section: Introductionmentioning

confidence: 99%

Representative QRS loop of the VCG record evaluation

Kijonka,

Vavra,

Penhaker

et al. 2024

Front. Physiol.

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Introduction: This study proposes an algorithm for preprocessing VCG records to obtain a representative QRS loop.Methods: The proposed algorithm uses the following methods: Digital filtering to remove noise from the signal, wavelet-based detection of ECG fiducial points and isoelectric PQ intervals, spatial alignment of QRS loops, QRS time synchronization using root mean square error minimization and ectopic QRS elimination. The representative QRS loop is calculated as the average of all QRS loops in the VCG record. The algorithm is evaluated on 161 VCG records from a database of 58 healthy control subjects, 69 patients with myocardial infarction, and 34 patients with bundle branch block. The morphologic intra-individual beat-to-beat variability rate is calculated for each VCG record.Results and Discussion: The maximum relative deviation is 12.2% for healthy control subjects, 19.3% for patients with myocardial infarction, and 17.2% for patients with bundle branch block. The performance of the algorithm is assessed by measuring the morphologic variability before and after QRS time synchronization and ectopic QRS elimination. The variability is reduced by a factor of 0.36 for healthy control subjects, 0.38 for patients with myocardial infarction, and 0.41 for patients with bundle branch block. The proposed algorithm can be used to generate a representative QRS loop for each VCG record. This representative QRS loop can be used to visualize, compare, and further process VCG records for automatic VCG record classification.

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