Effect of principal component analysis on diagnosing congestive heart failure patients using heart rate records

2012 ◽  
Author(s):  
Ali Narin ◽  
Yalcin Isler
Keyword(s):  
Heart Failure ◽  
Heart Rate ◽  
Principal Component Analysis ◽  
Congestive Heart Failure ◽  
Principal Component ◽  
Component Analysis ◽  
Heart Failure Patients

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

2021 ◽  
pp. 095441192110368
Author(s):  
Mustafa B Selek ◽  
Bartu Yesilkaya ◽  
Saadet S Egeli ◽  
Yalcin Isler
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Heart Rate ◽  
Principal Component Analysis ◽  
Congestive Heart Failure ◽  
Heart Rate Variability ◽  
Random Forest ◽  
Principal Components ◽  
Principal Component ◽  
Machine Learning Algorithms

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.

CARDIAC ARRHYTHMIA DIAGNOSIS BY HRV SIGNAL PROCESSING USING PRINCIPAL COMPONENT ANALYSIS

2012 ◽  
Vol 12 (05) ◽  
pp. 1240032 ◽  
Author(s):  
S. VINITHA SREE ◽  
DHANJOO N. GHISTA ◽  
KWAN-HOONG NG
Keyword(s):  
Heart Rate ◽  
Principal Component Analysis ◽  
Nervous System ◽  
Standard Deviation ◽  
Probabilistic Neural Network ◽  
Principal Component ◽  
Component Analysis ◽  
Cardiac Cells ◽  
Ecg Signal ◽  
Mean Values

An electrocardiogram (ECG) signal represents the sum total of millions of cardiac cells' depolarization potentials. It helps to identify the cardiac health of the subject by inspecting its P-QRS-T wave. The heart rate variability (HRV) data, extracted from the ECG signal, reflects the balance between sympathetic and parasympathetic components of the autonomic nervous system. Hence, HRV signal contains information on the imbalance between these two nervous system components that results in cardiac arrhythmias. Thus in this paper, we have analyzed HRV signal abnormalities to determine and classify arrhythmias. The HRV signals are non-stationary and non-linear in nature. In this work, we have used continuous wavelet transform (CWT) coupled with principal component analysis (PCA) to extract the important features from the heart rate signals. These features are fed to the probabilistic neural network (PNN) classifier, for automated classification. Our proposed system demonstrates an average accuracy of 80% and sensitivity and specificity of 82% and 85.6%, respectively, for arrhythmia detection and classification. Our system can be operated on larger data sets. Our CWT–PCA analysis resulted in eigenvalues which constituted the HRV signal analysis parameters. We have shown and plotted the distribution of the parameters' mean values and the standard deviation for arrhythmia classification. We found some overlap in the distribution of these eigenvalue parameters for the different arrhythmia classes, which mitigates the effective use of these parameters to separate out the various arrhythmia classes. Therefore, we have formulated a HRV Integrated Index (HRVID) of these eigenvalues, and determined and plotted the mean values and standard deviation of HRVID for the various arrhythmia classifications. From this information, it can be seen that the HRVID is able to distinguish among the various arrhythmia classes. Hence, we have made a case for the employment of this HRVID as an index to effectively diagnose arrhythmia disorders.

scholarly journals Dynamic heart rate estimation using principal component analysis

2015 ◽  
Vol 6 (11) ◽  
pp. 4610 ◽  
Author(s):  
Yong-Poh Yu ◽  
P. Raveendran ◽  
Chern-Loon Lim ◽  
Ban-Hoe Kwan
Keyword(s):  
Heart Rate ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Rate Estimation ◽  
Heart Rate Estimation

USING DIFFERENT ENTROPIES TO ANALYZE THE HEART RATE VARIABILITY OF CONGESTIVE HEART FAILURE PATIENTS

2011 ◽  
Vol 23 (04) ◽  
pp. 253-260 ◽  
Author(s):  
Ren-Guey Lee ◽  
Chun-Chieh Hsiao ◽  
Chieh-Yi Kao
Keyword(s):  
Heart Failure ◽  
Heart Rate ◽  
Congestive Heart Failure ◽  
Heart Rate Variability ◽  
Analysis Of Variance ◽  
Human Body ◽  
Approximate Entropy ◽  
Young Group ◽  
Multiscale Entropy ◽  
Heart Failure Patients

The purpose of this paper is to show the influence of congestive heart failure (CHF) on heart by using different entropies to apply on the group of patients with CHF and normal group. Three different entropies are used: approximate entropy (ApEn), multiscale entropy (MSE), and base-scale entropy (BsEn). We use these three entropies to measure the complexity of the heart rate variability (HRV) and also use analysis of variance (ANOVA) to analyze the result of entropies to discuss the feasibility of recognizing CHF patients by utilizing entropies. With the analysis results of different entropies, the influence of CHF on heart has also been clearly demonstrated. The results on the approximate entropy show that the normal young group has a higher approximate entropy value while the CHF group has a lower value. This can be explained as a healthy, strong heart that can change its heart rate freely to adapt the change of the environment or the needs of the human body, therefore the HRV will be more complex. From the ANOVA results of approximate entropy, it can be observed that the F value is larger than 1, but is still small. In other words, the approximate entropy can be used to distinguish the three groups, the effect is, however, not good. It is hard to recognize a CHF patient by using approximate entropy.

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