ArticleScientific reports2017
Detecting abnormality in heart dynamics from multifractal analysis of ECG signals.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Predicting recurrence of depression using cardiac complexity in individuals tapering antidepressants.Translational psychiatry · 2023Article
- Nonlinear Statistical Analysis of Normal and Pathological Infant Cry Signals in Cepstrum Domain by Multifractal Wavelet Leaders.Entropy (Basel, Switzerland) · 2022Article
- NoLiTiA: An Open-Source Toolbox for Non-linear Time Series Analysis.Frontiers in neuroinformatics · 2022Article
- Synchronization between music dynamics and heart rhythm is modulated by the musician's emotional involvement: A single case study.Frontiers in psychology · 2022Article
- A Proposal for a Data-Driven Approach to the Influence of Music on Heart Dynamics.Frontiers in cardiovascular medicine · 2021Article
- Entropy Analysis of RR-Time Series From Stress Tests.Frontiers in physiology · 2020Article
- Multifractal Spectrum Curvature of RR Tachograms of Healthy People and Patients with Congestive Heart Failure, a New Tool to Assess Health Conditions.Entropy (Basel, Switzerland) · 2019Article
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Authors and funding
4 authors.
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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.
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