Detection of some heart diseases using fractal dimension and chaos theory

Sedielmaci, Ibticeme; Reguig, F. Bereksi

doi:10.1109/wosspa.2013.6602342

Cited by 9 publications

(7 citation statements)

References 10 publications

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“…Finally, it can also be concluded that the use of fractal dimensions plus other techniques such as artificial neural networks, frequency analysis, principals components analysis, among others, offer greater applications of identification and classification on ECG and EEG signals allowing the early detection of Alzheimer's, identifying cardiovascular diseases, predicting sudden deaths due to heart problems, classifying heart signals [3], [7], [11], [12], etc.…”

Section: Discussionmentioning

confidence: 99%

“…Likewise, the principal components analysis theory was used to reduce the dimensionality of the results obtained from the fractal dimensions of Higuchi and Katz. For this, the work of [7] was taken as reference, who used fractal analysis and chaos theory to detect dynamic changes in a group of 13 ECG signals with healthy and unhealthy segments, mainly when RR intervals and the ST segments were analyzed. Likewise, [8] was taken as reference who show the use of a procedure about analysis of fluctuation without tendency based on the theory of fractals, for patients who had cerebrovascular accidents.…”

Section: Introductionmentioning

confidence: 99%

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Application of Fractal Algorithms to Identify Cardiovascular Diseases in ECG Signals

Dávalos¹,

Navarrete²

2019

Adv. sci. technol. eng. syst. j.

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The aim of this article was the identification of cardiovascular diseases, after applying Katz and Higuchi fractal algorithms on 4 databases of ECG signals downloaded from the Physionet website: heart failure (HF), hypertension (H), ischemic heart disease (IHD) and normal sinus rhythm (NSR). For this purpose, initially the ECG signals passed through a filtering stage using a Butterworth high pass filter of order 6 and 0.5Hz of cutoff frequency, in order to cancel variations of the baseline. The fractal algorithms were applied independently for each database. For such application, all signals were standardized with a total of 100,000 data, and for the calculation of each fractal dimension (FD) a frame equal to 10,000 with an overlap of 1,000 was used in a first stage; in a second stage, a frame equal to 1,000 with an overlap of 100 was used. Thus, the results showed that the Higuchi algorithm, in general, has a better performance compared with the Katz algorithm. These results refer to the observation of the variance of the FD averages, which are shown in Table 4. For example, in the cardiovascular disease Arterial Hypertension, the Higuchi algorithm presented 0.0093 of variance compared with the algorithm of Katz that only reached 0.0352. Complementarily, we used the Principal Component Analysis (PCA) to show graphically the differences between algorithms, so the components related to the Higuchi algorithm were presented in a less dispersed and grouped form, making possible their differentiation, mainly when the second analysis stage was carried out.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of Fractal Algorithms to Identify Cardiovascular Diseases in ECG Signals

Dávalos¹,

Navarrete²

2019

Adv. sci. technol. eng. syst. j.

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“…The ECG waveform includes 5 major waves of P, Q, R, S, and T [3]. One of the most important parts of the ECG analysis is the measurement of RR-Interval and ST-segment, which are good representatives of the variety of heartbeat [4,5]. Given the non-linear dynamism of the heart and its self-similarity features, the ECG signals present the fractal properties.…”

Section: Introductionmentioning

confidence: 99%

“…Given the non-linear dynamism of the heart and its self-similarity features, the ECG signals present the fractal properties. As a result, the fractal dimension, which represents the nonlinear dynamism, is an appropriate method for analyzing and extracting the characteristics from these signals [4,6].…”

Section: Introductionmentioning

confidence: 99%

“…Over the last few decades, various techniques have been proposed to solve the ECG classification problems with a useful diagnosis. In most techniques, features of time [8], frequency [9], time-frequency [10], and non-linear dynamism [4,9,11] have been extracted and then effectively classified. Rai et al [10] analyzed the ECG signals based on the neural network and hybrid features (Discrete Wavelet Transforms and morphological).…”

Section: Introductionmentioning

confidence: 99%

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Classification of 7 Arrhythmias from ECG Using Fractal Dimensions

Kiani¹,

Maghsoudi²

2019

J Bioinform Syst Biol

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The statistics indicate a dramatic increase in mortality due to cardiovascular failures in the worldwide. In developing countries, with lower per capita income, especially in rural areas, late diagnosis of the disease leads to the sudden death of people. Since electrocardiogram (ECG) is one of the most important tools for diagnosing cardiovascular diseases, this study has been presented to analyze this highly inexpensive and available signal. Extracted characteristics of the signal are a good representation of the heart function because of the chaotic, dynamic, and nonlinear behavior of the heart. The fractal dimension is the best representative of the ECG signal which is able to take into account its hidden complexity. ECG signals are analyzed based on the fractal dimension and Back Propagation Neural Network (BPN). In this paper, a new technique is introduced for honest classification of 7 arrhythmias from ECG signals using the fractal dimension. This method is able to identify the exact location of arrhythmias. A combination of 5 reputable universal databases is used to classify based on the fractal dimension and BPN. The performance of this method is measured by Sensitivity (SE) and Specificity (SP) indices. According to the results, the accuracy of this method is equal to 98.83%.

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Classification of Arrhythmias from ECG Using Fractal Dimensions and Wavelet Theory

Sabrine

Aguili

2022

Advances in Information, Communication and Cybersecurity

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Detection of some heart diseases using fractal dimension and chaos theory

Cited by 9 publications

References 10 publications

Application of Fractal Algorithms to Identify Cardiovascular Diseases in ECG Signals

Application of Fractal Algorithms to Identify Cardiovascular Diseases in ECG Signals

Classification of 7 Arrhythmias from ECG Using Fractal Dimensions

Classification of Arrhythmias from ECG Using Fractal Dimensions and Wavelet Theory

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