Automatic identification of cardiac health using modeling techniques: A comparative study

Acharya, U. Rajendra; Sankaranarayanan, Meena; Nayak, Jagadish; Chen, Xiang; Tamura, Toshiyo

doi:10.1016/j.ins.2008.08.006

Cited by 81 publications

(35 citation statements)

References 53 publications

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“…In [8,9], HRV signals are used to automatically diagnose Coronary Artery Disease (CAD). Moreover, HRV signals are used to analyze arrhythmia subjects [10] and for the risk prediction of cardiovascular diseases [11]. HRV signals of post MI patients are studied in [12].…”

Section: Introductionmentioning

confidence: 99%

Use of Accumulated Entropies for Automated Detection of Congestive Heart Failure in Flexible Analytic Wavelet Transform Framework Based on Short-Term HRV Signals

Kumar

Pachori

Acharya

2017

Entropy

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Abstract:In the present work, an automated method to diagnose Congestive Heart Failure (CHF) using Heart Rate Variability (HRV) signals is proposed. This method is based on Flexible Analytic Wavelet Transform (FAWT), which decomposes the HRV signals into different sub-band signals. Further, Accumulated Fuzzy Entropy (AFEnt) and Accumulated Permutation Entropy (APEnt) are computed over cumulative sums of these sub-band signals. This provides complexity analysis using fuzzy and permutation entropies at different frequency scales. We have extracted 20 features from these signals obtained at different frequency scales of HRV signals. The Bhattacharyya ranking method is used to rank the extracted features from the HRV signals of three different lengths (500, 1000 and 2000 samples). These ranked features are fed to the Least Squares Support Vector Machine (LS-SVM) classifier. Our proposed system has obtained a sensitivity of 98.07%, specificity of 98.33% and accuracy of 98.21% for the 500-sample length of HRV signals. Our system yielded a sensitivity of 97.95%, specificity of 98.07% and accuracy of 98.01% for HRV signals of a length of 1000 samples and a sensitivity of 97.76%, specificity of 97.67% and accuracy of 97.71% for signals corresponding to the 2000-sample length of HRV signals. Our automated system can aid clinicians in the accurate detection of CHF using HRV signals. It can be installed in hospitals, polyclinics and remote villages where there is no access to cardiologists.

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Section: Introductionmentioning

confidence: 99%

Use of Accumulated Entropies for Automated Detection of Congestive Heart Failure in Flexible Analytic Wavelet Transform Framework Based on Short-Term HRV Signals

Kumar

Pachori

Acharya

2017

Entropy

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“…In some other research recent research works, prediction of heart diseases is done just by analyzing a single type of data sets like ECG alone. In [3,7] only ECG signals are processed to predict the heart diseases, thus making the decision making system prone to error, that is why in our proposed method the risk prediction uses many types of diagnostics data. There are some recent research works which does not consider a single classifier's decision to predict the heart disease, like in [6], for predicting heart arrhythmia random forest classifier is used, which is a collection of classification trees.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Classification Algorithms on a Novel Unified Clinical Decision Support Model for Predicting Coronary Heart Disease Risks

Puyalnithi¹,

Viswanatham²

2017

IJIES

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Abstract:Most of the existing Clinical Decision Support Systems(CDSS) for predicting Coronary Heart Disease(CHD) risks rely solely on the analysis of single type of diagnostic data or a certain type of classification algorithm, thus making the predicted component unreliable. Since the Risk value very much influences the further course of the treatment, the correctness of the risk value is eminent. So the novelty of the proposed method lies in considering many classification algorithms and many diagnostics data sets to predict the risk value. Moreover in the proposed method we have given flowchart for finding the risk value, which is a novelty. The results shows that the selection of classification algorithms cannot be universal as proposed by many recent researches and each type of data set will be having a certain type of classifier which works best for the data set. The proposed system has four classifiers namely Mixed cardio data classifier, Single Photon Emission Computed Tomography(SPECT) data classifier, Electrocardiogram(ECG) data classifier and 1-year survival Electrocardiogram data classifier, each trained with benchmark datasets of University of California, Irvine(UCI) Machine learning repository. The performance of algorithms such as Iterative Dichotomiser3(ID3), Naïve Bayes(NB), Support Vector Machine(SVM), k-Nearest Neighbors(kNN) and Ensemble classifiers such as Bagging and Boosting are evaluated for every data set and best classifier is chosen by its accuracy measure. Ensemble method is the most efficient for 1-year survival ECG data classifier with 89.5% accuracy, ID3 is the most efficient algorithm for Mixed cardiac data classifier with 96.9% accuracy and SVM is the most efficient algorithm for SPECT data classifier and ECG data Classifier with accuracy of 98.3% and 100% respectively. The proposed work also finds the optimal number of samples needed for Bagging and Boosting type of ensemble classifiers.

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“…In the literature, several methods have been proposed for the automatic classification of ECG signals. The recently published papers are presented in [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. In [3], the authors used the discrete wavelet transform as the feature extractor and linear discriminants as the classifier for PVC beat classification and achieved the recognition accuracy (RA) about 95.6%.…”

Section: Introductionmentioning

confidence: 99%

Classification of ECG signals using Hermite functions and MLP neural networks

Ebrahimzadeh

Ahmadi

Safarnejad

2016

JAIDM

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Classification of heart arrhythmia is an important step in developing devices for monitoring the health of individuals. This paper proposes a three module system for classification of electrocardiogram (ECG) beats. These modules are: denoising module, feature extraction module and a classification module. In the first module the stationary wavelet transform (SWF) is used for noise reduction of the ECG signals. The feature extraction module extracts a balanced combination of the Hermit features and three timing interval feature. Then a number of multi-layer perceptron (MLP) neural networks with different number of layers and eight training algorithms are designed. Seven files from the MIT/BIH arrhythmia database are selected as test data and the performances of the networks, for speed of convergence and accuracy classifications, are evaluated. Generally all of the proposed algorisms have good training time, however, the resilient back propagation (RP) algorithm illustrated the best overall training time among the different training algorithms. The Conjugate gradient back propagation (CGP) algorithm shows the best recognition accuracy about 98.02% using a little amount of features.

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Automatic identification of cardiac health using modeling techniques: A comparative study

Cited by 81 publications

References 53 publications

Use of Accumulated Entropies for Automated Detection of Congestive Heart Failure in Flexible Analytic Wavelet Transform Framework Based on Short-Term HRV Signals

Use of Accumulated Entropies for Automated Detection of Congestive Heart Failure in Flexible Analytic Wavelet Transform Framework Based on Short-Term HRV Signals

Performance Analysis of Classification Algorithms on a Novel Unified Clinical Decision Support Model for Predicting Coronary Heart Disease Risks

Classification of ECG signals using Hermite functions and MLP neural networks

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