Early prediction of paroxysmal atrial fibrillation based on short-term heart rate variability

Narin, Ali; İşler, Yalçın; Özer, Mahmut; Perc, Matjaž

doi:10.1016/j.physa.2018.06.022

Cited by 46 publications

(54 citation statements)

References 36 publications

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“…Where C j is the j-th value of the wavelet coefficient, and C f is the average value of all wavelet coefficients at the EEG frequency band of f [45][46][47].…”

Section: Wavelet-based Feature Extractionmentioning

confidence: 99%

“…In addition, whenever the WT is adopted and implemented in a pattern recognition system, a feature and/or features must first be extracted for it [43]. In many studies, energy and/or entropy and/or variance are calculated from heart rate variability (HRV) [44][45][46][47], electrocardiography (ECG) [49], electromyography (EMG) [50,51], electrooculography (EOG) [52], electroencephalography (EEG) [53,54], steady-state visually-evoked potentials (SSVEP) [54] signals first. When their mathematical formulas are examined, these features seem to be similar.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of mother wavelets on steady-state visually-evoked potentials for triple-command brain-computer interfaces

Sayılgan¹,

Yüce²,

İşler³

2021

Turk J Elec Eng & Comp Sci

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Wavelet transform (WT) is an important tool to analyze the time-frequency structure of a signal. The WT relies on a prototype signal that is called the mother wavelet. However, there is no single universal wavelet that fits all signals. Thus, the selection of mother wavelet function might be challenging to represent the signal to achieve the optimum performance. There are some studies to determine the optimal mother wavelet for other biomedical signals, however, there exists no evaluation for Steady-State Visually-Evoked Potentials (SSVEP) signals that becomes very popular among signals manipulated for brain-computer interfaces (BCIs) recently. This study aims to explore if any, the mother wavelet that suits best to represent SSVEP signals for classification purposes in BCIs. In this study, three common wavelet-based features (variance, energy, and entropy) extracted from SSVEP signals for five distinct EEG frequency bands (delta, theta, alpha, beta, and gamma) were classified to determine three different user commands using six fundamental classifier algorithms. The study was repeated for six different commonly-used mother wavelet functions (Haar, Daubechies, Symlet, Coiflet, Biorthogonal, and Reverse Biorthogonal). The best discrimination was obtained as the accuracy of 100% with the average of 75.85%. Besides, Ensemble Learner gives the highest accuracies for half of the trials. Haar wavelet had the best performance in representing SSVEP signals among other all mother wavelets adopted in this study. Concomitantly, all three features of energy, variance, and entropy should be used together since none of these features had superior classifier performance alone.

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“…Where C j is the j-th value of the wavelet coefficient, and C f is the average value of all wavelet coefficients at the EEG frequency band of f [45][46][47].…”

Section: Wavelet-based Feature Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of mother wavelets on steady-state visually-evoked potentials for triple-command brain-computer interfaces

Sayılgan¹,

Yüce²,

İşler³

2021

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

show abstract

“…However, PAF is usually not diagnosed earlier because it is asymptomatic in general [4]. When PAF cases reach the clinically detectable stage, symptoms that decrease the quality of life emerge suddenly [5]. This study aims to distinguish individuals with PAF disease from individuals without known heart disease.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of paroxysmal atrial fibrillation from thirty-minute heart rate variability data using convolutional neural networks

Surucu¹,

İşler²,

Kara³

2021

Turk J Elec Eng & Comp Sci

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Paroxysmal atrial fibrillation (PAF) is the initial stage of atrial fibrillation, one of the most common arrhythmia types. PAF worsens with time and affects the patient's life quality negatively. In this study, we aimed to early diagnose PAF patients, so patients can start taking precautions before this disease gets worse. We used the Atrial Fibrillation Prediction Database, an open data from Physionet, and constructed our approach using convolutional neural networks. Heart rate variability (HRV) features are calculated from time-domain measures, frequency-domain measures using power spectral density estimations (fast Fourier transform, Lomb-Scargle, and Welch periodogram), timefrequency-domain measures using wavelet transform, and nonlinear Poincare plot measures. We also normalized these features using min-max normalization and z-score normalization methods. In addition, we also applied alternatively the heart rate normalization (HRN), which gave promising results in a few HRV-based research, before calculating these features. Thus, HRV data, HRN data, and HRV features extracted from six different combinations of these normalizations, in addition to no normalization cases, were applied to the convolutional neural networks classifier. We tuned the classifiers using 90% of samples and tested the classifiers' performances using 10% of data. The proposed approach resulted in 95.92% accuracy, 100% precision, 91.84% recall, and 95.74% f1-score in HRV with z-score feature normalization. When the heart rate normalization was also applied, the proposed approach reached 100% accuracy, 100% precision, 100% recall, and 100% f1-score in HRV with z-score feature normalization. The proposed method with heart rate normalization and z-score normalization methods resulted in better classification performance than similar studies in the literature. In addition, although deep learning models offer no use of separate feature extraction processes, this study reveals that using HRV-specific feature extraction techniques may improve the performance of deep learning algorithms in HRV-based studies. Comparing the existing studies, we concluded that our approach provides a much better tool to diagnose PAF patients.

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“…Recent examples of research 12 include adaptive neural net for diagnosing diabetes, 12 ''multi-stage classification of congestive heart failure based on short-term heart rate variability,'' 13 and ''early prediction of paroxysmal atrial fibrillation based on short-term heart rate variability.'' 14 Recently, metaheuristic search algorithms have been used efficiently for solving optimization problems of different domains. Mostly, the metaheuristic algorithms mimic the characteristics of living and nonliving things and are not dependent on the characteristics of the given optimization problem.…”

Section: Introductionmentioning

confidence: 99%

Hybrid chaotic firefly decision making model for Parkinson’s disease diagnosis

Dash

Abraham

Luhach

et al. 2020

International Journal of Distributed Sensor Networks

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Parkinson’s disease is found as a progressive neurodegenerative condition which affects motor circuit by the loss of up to 70% of dopaminergic neurons. Thus, diagnosing the early stages of incidence is of great importance. In this article, a novel chaos-based stochastic model is proposed by combining the characteristics of chaotic firefly algorithm with Kernel-based Naïve Bayes (KNB) algorithm for diagnosis of Parkinson’s disease at an early stage. The efficiency of the model is tested on a voice measurement dataset that is collected from “UC Irvine Machine Learning Repository.” The dynamics of chaos optimization algorithm will enhance the firefly algorithm by introducing six types of chaotic maps which will increase the diversification and intensification capability of chaos-based firefly algorithm. The objective of chaos-based maps is to select initial values of the population of fireflies and change the value of absorption coefficient so as to increase the diversity of populations and improve the search process to achieve global optima avoiding the local optima. For selecting the most discriminant features from the search space, Naïve Bayesian stochastic algorithm with kernel density estimation as learning algorithm is applied to evaluate the discriminative features from different perspectives, namely, subset size, accuracy, stability, and generalization. The experimental study of the problem established that chaos-based logistic model overshadowed other chaotic models. In addition, four widely used classifiers such as Naïve Bayes classifier, k-nearest neighbor, decision tree, and radial basis function classifier are used to prove the generalization and stability of the logistic chaotic model. As a result, the model identified as the best one and could be used as a decision making tool by clinicians to diagnose Parkinson’s disease patients.

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Early prediction of paroxysmal atrial fibrillation based on short-term heart rate variability

Cited by 46 publications

References 36 publications

Evaluation of mother wavelets on steady-state visually-evoked potentials for triple-command brain-computer interfaces

Evaluation of mother wavelets on steady-state visually-evoked potentials for triple-command brain-computer interfaces

Diagnosis of paroxysmal atrial fibrillation from thirty-minute heart rate variability data using convolutional neural networks

Hybrid chaotic firefly decision making model for Parkinson’s disease diagnosis

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