Fractal behavior of heart rate variability during ECG stress test in cardiac patients

An important challenge in heart research is to make the relation between the features of external stimuli and heart activity. Olfactory stimulation is an important type of stimulation that affects the heart activity, which is mapped on Electrocardiogram (ECG) signal. Yet, no one has discovered any relation between the structures of olfactory stimuli and the ECG signal. This study investigates the relation between the structures of heart rate and the olfactory stimulus (odorant). We show that the complexity of the heart rate is coupled with the molecular complexity of the odorant, where more structurally complex odorant causes less fractal heart rate. Also, odorant having higher entropy causes the heart rate having lower approximate entropy. The method discussed here can be applied and investigated in case of patients with heart diseases as the rehabilitation purpose.

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“…D’Addio et al 19. performed fractal analysis on cardiac patients during resting, stress, early and late recovery phases of ECG stress test.…”

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confidence: 99%

Fractal Based Analysis of the Influence of Odorants on Heart Activity

Namazi

Kulish

2016

Sci Rep

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“…They explain that the model works well even when it has been tested with data that the model has not been trained by them. In [55], the fractal dimension of ECG signal in OSAS (Obstructive Sleep Apnea Syndrome) patients has been studied. It is reported that normal breathing epochs can be discriminated from abnormal ones with fractal dimension.…”

Section: Methods To Classify Ecg Signals Into Different Disease Categoriesmentioning

confidence: 99%

Hybrid Neuro-Fractal Analysis of ECG Signals to Predict Ischemia

Montazeri¹

2021

Preprint

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In this thesis, we propose and implement a new hybrid approach using fractal analysis, statistical analysis and neural network computation to build a model for prediction the number of ischemia occurrence based on ECG recordings. The main advantage of the proposed approach over similar earlier related works is that first useful parameters from fractal analysis of the signal are extracted to build a model that includes both clinical characteristics and signal attributes. Statistical analysis such as binary logistic regression and multivariate linear regression are then used to further explore the relation of parameters in order to obtain a more accurate model. We show that the results compare well with those of earlier work and clearly indicate that the augmentation of the above mentioned approaches improves the prediction accuracy.

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“…HFD and Katz were measured on wavelet sub-band for detecting voice pathology [8]. HFD was also utilized to identify heart rate variability (HRV) on a cardiac patient ECG stress test [9]. HFD measurement depends on the value of the specified maximum time interval kmax.…”

Section: Introductionmentioning

confidence: 99%

“…HFD measurement depends on the value of the specified maximum time interval kmax. In previous studies, HFD was commonly measured at only one particular value of kmax [7]- [9]. Some researchers tried to analyze the HFD value in a range of kmax to obtain an optimum HFD value [10].…”

Section: Introductionmentioning

confidence: 99%

Lung sound classification using multiresolution Higuchi fractal dimension measurement

Rizal

Hidayat

Nugroho

et al. 2023

IJECE

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Lung sound is one indicator of abnormalities in the lungs and respiratory tract. Research for automatic lung sound classification has become one of the interests for researchers because lung disease is one of the diseases with the most sufferers in the world. The use of lung sounds as a source of information because of the ease in data acquisition and auscultation is a standard method in examining pulmonary function. This study simulated the potential use of Higuchi fractal dimension (HFD) as a feature extraction method for lung sound classification. HFD calculations were run on a series of k values to generate some HFD values as features. According to the simulation results, the proposed method could produce an accuracy of up to 97.98% for five classes of lung sound data. The results also suggested that the shift in HFD values over the selection of a time interval k can be used for lung sound classification.

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Fractal behavior of heart rate variability during ECG stress test in cardiac patients

Cited by 6 publications

References 7 publications

Fractal Based Analysis of the Influence of Odorants on Heart Activity

Fractal Based Analysis of the Influence of Odorants on Heart Activity

Hybrid Neuro-Fractal Analysis of ECG Signals to Predict Ischemia

Lung sound classification using multiresolution Higuchi fractal dimension measurement

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