Dataset Information

Detecting abnormality in heart dynamics from multifractal analysis of ECG signals.

ABSTRACT: The characterization of heart dynamics with a view to distinguish abnormal from normal behavior is an interesting topic in clinical sciences. Here we present an analysis of the Electro-cardiogram (ECG) signals from several healthy and unhealthy subjects using the framework of dynamical systems approach to multifractal analysis. Our analysis differs from the conventional nonlinear analysis in that the information contained in the amplitude variations of the signal is being extracted and quantified. The results thus obtained reveal that the attractor underlying the dynamics of the heart has multifractal structure and the variations in the resultant multifractal spectra can clearly separate healthy subjects from unhealthy ones. We use supervised machine learning approach to build a model that predicts the group label of a new subject with very high accuracy on the basis of the multifractal parameters. By comparing the computed indices in the multifractal spectra with that of beat replicated data from the same ECG, we show how each ECG can be checked for variations within itself. The increased variability observed in the measures for the unhealthy cases can be a clinically meaningful index for detecting the abnormal dynamics of the heart.

SUBMITTER: Shekatkar SM

PROVIDER: S-EPMC5680386 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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Detecting abnormality in heart dynamics from multifractal analysis of ECG signals.

Shekatkar Snehal M SM Kotriwar Yamini Y Harikrishnan K P KP Ambika G G

Scientific reports 20171109 1

The characterization of heart dynamics with a view to distinguish abnormal from normal behavior is an interesting topic in clinical sciences. Here we present an analysis of the Electro-cardiogram (ECG) signals from several healthy and unhealthy subjects using the framework of dynamical systems approach to multifractal analysis. Our analysis differs from the conventional nonlinear analysis in that the information contained in the amplitude variations of the signal is being extracted and quantifie ...[more]

PMID: 29123213

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Project description:Anti-melanoma differentiation-associated gene 5 (MDA5) antibody, a dermatomyositis (DM)-specific antibody, is strongly associated with interstitial lung disease (ILD). Patients with idiopathic inflammatory myopathy (IIM) who are anti-MDA5 antibody positive [anti-MDA5 (+)] often experience chest symptoms during the active disease phase. These symptoms are primarily explained by respiratory failure; nevertheless, cardiac involvement can also be symptomatic. Thus, the aim of this study was to investigate cardiac involvement in anti-MDA5 (+) DM. A total of 63 patients with IIM who underwent electrocardiography (ECG) and ultrasound cardiography (UCG) during the active disease phase from 2016 to 2021 [anti-MDA5 (+) group, n = 21; anti-MDA5-negative (-) group, n = 42] were enrolled in the study, and their clinical charts were retrospectively reviewed. The ECG and UCG findings were compared between the anti-MDA5 (+) and anti-MDA5 (-) groups. All anti-MDA5 (+) patients had DM with ILD. The anti-MDA5 (+) group showed more frequent skin ulcerations and lower levels of leukocytes, muscle enzymes, and electrolytes (Na, K, Cl, and Ca) than the anti-MDA5 (-) group. According to the ECG findings obtained during the active disease phase, the T wave amplitudes were significantly lower for the anti-MDA5 (+) group than for the anti-MDA5 (-) group (I, II, and V4-6 lead; p < 0.01; aVF and V3, p < 0.05). However, the lower amplitudes were restored during the remission phase. Except for the E wave, A wave and Sep e', the UCG results showed no significant differences between the groups. Four patients with anti-MDA5 (+) DM had many leads with lower T wave and cardiac abnormalities (heart failure, diastolic dysfunction, myocarditis) on and after admission. Though anti-MDA5 (+) patients clinically improved after immunosuppressive therapy, some of their ECG findings did not fully recover in remission phase. In conclusion, anti-MDA5 (+) DM appears to show cardiac involvement (electrical activity and function) during the active phase. Further studies are necessary to clarify the actual cardiac condition and mechanism of these findings in patients with anti-MDA5 (+) DM.

Dataset Information

Detecting abnormality in heart dynamics from multifractal analysis of ECG signals.

Publications

Detecting abnormality in heart dynamics from multifractal analysis of ECG signals.

Similar Datasets

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