Heart Rate Variability in Congestive Heart Failure

Stein, Phyllis K.; Pu, Yachuan

doi:10.1201/b12756-21

Cited by 5 publications

(3 citation statements)

References 85 publications

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“…The HRV is the change between consecutive R-R intervals, so it is essential to acquire the R-wave signal during the measurement. R-wave signals can usually be obtained in the V5 and V6 leads in the chest [ 29 ]. In this test, two electrodes (Shanghai Junkang Medical Equipment Co., Ltd., Shanghai, China) were used for measurements.…”

Section: Methodsmentioning

confidence: 99%

Can Heart Rate Variability (HRV) Be Used as a Biomarker of Thermal Comfort for Mine Workers?

Liu

et al. 2021

IJERPH

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This study aimed to determine whether heart rate variability (HRV) can express the thermal comfort of mine workers. Eight subjects ran on a treadmill (5.5 km/h) to simulate heavy labor in three kinds of mining environments (22 °C/90%, 26 °C/90%, 30 °C/90%), respectively. Based on the measured electrocardiogram (ECG) data, the HRV of the subjects was calculated. The results showed that the HRV indices changed obviously under different temperature environments. In the neutral and hot environment, except for the LF, TP and LF/HF, there were significant differences in each index. However, there was no significant difference between the cold and neutral environments. The R-R intervals, the very low-frequency power (VLF), pNN20 and SampEN had strong negative correlation with the thermal sensation of people from sitting to work (ρ < −0.700). These indices may be used as thermal comfort predictive biomarkers of mine workers.

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Section: Methodsmentioning

confidence: 99%

Can Heart Rate Variability (HRV) Be Used as a Biomarker of Thermal Comfort for Mine Workers?

Liu

et al. 2021

IJERPH

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“…This method is known as heart rate variability (HRV). [5][6][7][8] HRV is an indicator of the Autonomic Nervous System (ANS) and gives information about internal physiology. 9 Some features are extracted from HRV analysis, represent different indicators for understanding the ANS, cardiac diseases, neuropathy, and diabetes.…”

Section: Heart Rate Variabilitymentioning

confidence: 99%

The effect of principal component analysis in the diagnosis of congestive heart failure via heart rate variability analysis

Selek

Yesilkaya

Egeli

et al. 2021

Proc Inst Mech Eng H

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In this study, we investigated the effect of principal component analysis (PCA) in congestive heart failure (CHF) diagnosis using various machine learning algorithms from 5-min HRV data. The extracted 59 heart rate variability (HRV) features consist of statistical time-domain measures, frequency-domain measures (power spectral density estimations from Fourier transform and Lomb-Scargle methods), time-frequency HRV measures (Wavelet transform), and nonlinear HRV measures (Poincare plot, symbolic dynamics, detrended fluctuation analysis, and sample entropy). All these HRV features are the classifiers’ inputs. We repeated the study ten times using the first one to the first 10 principal components from PCA instead of all HRV features. Nine different classifiers, namely logistic regression, Naive Bayes, k-nearest neighbors, decision tree, AdaBoost, support vector machines, stochastic gradient descent, random forest, and artificial neuronal network (multilayer perceptron) are examined. The proposed study results in the 100% accuracy, 100% specificity, and 100% sensitivity after utilizing PCA (with the first eight principal components) using the Random Forest classifier where the maximum classifier performances are the 86% accuracy, 79% specificity, and 86% sensitivity before PCA. In conclusion, PCA is beneficial in the diagnosis of patients with CHF. In addition, we experienced the online Python-based visual machine learning tool, Orange, which can implement well-known machine learning algorithms.

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“…Abnormal nonlinear HRV may predict sudden cardiac death (Stein, 2006). The association between seizures and HR changes has been documented since 30 years ago in studies in patients with partial seizures (Blumhardt, Smith, & Owen, 1986;Marshall, Westmoreland, & Sharbrough, 1983).…”

Section: Introductionmentioning

confidence: 97%

A new algorithm for detection of epileptic seizures based on HRV signal

Behbahani

Dabanloo

Nasrabadi

et al. 2014

Journal of Experimental & Theoretical Artificial Intelligen

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Epilepsy is one of the most frequent neurological disorders. In a significant number of cases, skilled professionals carry out the detection of the epileptic seizures manually. This necessitates automated epileptic seizure detection. Many researchers have presented computational methods for detecting epileptic seizures based on electroencephalogram signals. In this article, we propose a novel and efficient algorithm for detecting the presence of epileptic seizures in heart rate variability (HRV). This algorithm includes feature extraction and classification. Ten features include time and frequency domain analysis and nonlinear features extracted from onelead electrocardiogram signal of epileptic patients. Extracted features were used as the input of an artificial neural network, which provides the final classification of the HRV segments (existence of epileptic seizure or not). Multilayer perceptron neural networks with different number of hidden layers and five training algorithms were designed. The results show sensitivity, specificity and accuracy of 88.66%, 90% and 88.33%, respectively, in secondary generalised and 83.33%, 86.11% and 84.72%, respectively, in complex partial seizures. The experimental results portray that the proposed algorithm efficiently detects the presence of epileptic seizure in HRV signals and showed a reasonable accuracy in detection.

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Heart Rate Variability in Congestive Heart Failure

Cited by 5 publications

References 85 publications

Can Heart Rate Variability (HRV) Be Used as a Biomarker of Thermal Comfort for Mine Workers?

Can Heart Rate Variability (HRV) Be Used as a Biomarker of Thermal Comfort for Mine Workers?

The effect of principal component analysis in the diagnosis of congestive heart failure via heart rate variability analysis

A new algorithm for detection of epileptic seizures based on HRV signal

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