ECG-based detection of left ventricle hypertrophy

Abstract

Introduction: Left ventricle hypertrophy (LVH) is an important risk factor for cardiovascular morbidity and mortality. It is characterized by a thickening of the walls of the left ventricle. The transthoracic echocardiogram is a very accurate method for LVH detection. However, the electrocardiogram (ECG) offers an alternative method in diagnosing LVH, besides being less expensive and easier to obtain. In this context, this study proposes an ECG based approach for left ventricle hypertrophy (LVH) classification. Methods: According to the literature, several indexes have so far been proposed that suggest specific changes in cardiac structure, however, generally speaking there is no consensus about the best criteria. This way, instead of considering only one LVH criterion, a score derived from electrocardiographic traces was employed which explores the complementarity of the best criteria through a fusion strategy. The best criteria are those which discriminate normal and LVH ECGs. Results: The experiments were performed in the Monica database with a group of fifty men. Half of the individuals had LVH diagnosed by calculating the left ventricular mass index measured by transthoracic echocardiography. The score fusion proposed achieved a sensitivity of 78.3% and specificity of 91.3%, outperforming all isolated LVH criteria. Discussion: Unlike the other methods, our score must be estimated within a computer because of its high complexity. Even with this limitation it is much less expensive than using the echocardiography.

Utilidad diagnóstica del ECG para predecir la geometría del ventrículo izquierdo por ecocardiograma

“…The Philips DXL-16 algorithm creates automatic quantitative parameters from each ECG recording. With this information, the 31 ECG criteria were calculated (Supplementary Table S1) 8 , 9 .…”

Predicting the presence of Left Ventricular Hypertrophy using Este’s Criteria with Support Vector Machine

“…For the purpose of classification, first two categories are merged in one that is "Normal" and the third is shown as instances with LVH. Heatmap of classification report for support vector classifier is shown in Figure 6 There are various performance measures among these sensitivity and specificity are most commonly used for performance indicators of the system which Based on the study it has been discerned that detection of probability of LVH can be achieved by either one or combining more than one of the following methods: (i) Proposing a 'score' system based on features extracted from the electrocardiogram of patients, such a kind of score system was proposed by [39] for defining LVH using fifty subjects and achieved the sensitivity and specificity of 78.3% and 91.3% respectively but the number of instances was considerably low. (ii) Using different image processing techniques or applying higher-order mathematics to images obtained from echocardiography to detect the mass of left ventricles.…”

Prototype of Portable Heart Monitoring System using BITalino

“…After the QRS-interval has been evaluated, a thorough examination of the ECG signal can be carried out. The shape, the amplitude, and the duration of the QRSinterval are very helpful in diagnosing heart diseases such as cardiac arrhythmias, conduction abnormalities, ventricular hypertrophy, and myocardial infections (Zago, et al, 2015).…”