Congestive Heart Failure Classification Research Articles

An Integrated Machine Learning Approach for Congestive Heart Failure Prediction.

Congestive heart failure (CHF) is one of the primary sources of mortality and morbidity among the global population. Over 26 million individuals globally are affected by heart disease, and its prevalence is rising by 2% yearly. With advances in healthcare technologies, if we predict CHF in the early stages, one of the leading global mortality factors can be reduced. Therefore, the main objective of this study is to use machine learning applications to enhance the diagnosis of CHF and to reduce the cost of diagnosis by employing minimum features to forecast the possibility of a CHF occurring. We employ a deep neural network (DNN) classifier for CHF classification and compare the performance of DNN with various machine learning classifiers. In this research, we use a very challenging dataset, called the Cardiovascular Health Study (CHS) dataset, and a unique pre-processing technique by integrating C4.5 and K-nearest neighbor (KNN). While the C4.5 technique is used to find significant features and remove the outlier data from the dataset, the KNN algorithm is employed for missing data imputation. For classification, we compare six state-of-the-art machine learning (ML) algorithms (KNN, logistic regression (LR), naive Bayes (NB), random forest (RF), support vector machine (SVM), and decision tree (DT)) with DNN. To evaluate the performance, we use seven statistical measurements (i.e., accuracy, specificity, sensitivity, F1-score, precision, Matthew's correlation coefficient, and false positive rate). Overall, our results reflect our proposed integrated approach, which outperformed other machine learning algorithms in terms of CHF prediction, reducing patient expenses by reducing the number of medical tests. The proposed model obtained 97.03% F1-score, 95.30% accuracy, 96.49% sensitivity, and 97.58% precision.

Classification of Congestive Heart Failure Using Artificial Neural Network Based on Higher-Order Moments Detrended Fluctuation Analysis of Heart Rate Variability

Coronary heart disease or cardiovascular disease is a kind of heart disease that affects the heart and all the blood vessels in the body caused by a build-up of plaque in a person's arteries and can result in a stroke or heart attack. According to the Indonesian Ministry of Health, (Depkes RI) coronary heart disease is the main and first cause of all deaths by heart disease, which account for 26.4% among all other causes. Considering the severity of the condition, it is necessary to detect this kind of heart disease to reduce the number of deaths from heart disease. Heart disease detection can be performed by checking the Heart Rate Variability (HRV) signal. HRV assessment is carried out by analyzing short-term and long-term Electrocardiogram (ECG) records. To analyze HRV signals, a Higher-Order Moments Detrended Fluctuation Analysis feature extraction method is proposed to see the non-linear structure of HRV data. The input for Higher-Order Moments Detrended Fluctuation Analysis is an ECG signal which has been converted into an HRV signal. The output of the feature extraction shows that the value of kurtosis-based fluctuation function has a statistical significance in order to differentiate the HRV of heart disease patients and that of normal subjects. These values are then used as the input for classification using an Artificial Neural Network. The output of the classification is the classification of patients with heart disease and normal subjects. The results of the Higher-Order Moments Detrended Fluctuation Analysis feature extraction classification yield the best accuracy of 71.43% with a ROC value of 0.774 which can be categorized as a pretty good classification.

Inter-patient congestive heart failure automatic recognition using attention-based multi-scale convolutional neural network

Accurate classification of congestive heart failure (CHF) is essential to reduce the mortality of cardiovascular disease. Many existing researches suffer from unsatisfactory performance in inter-patient scheme closer to clinical application. To address this issue, this paper presents a novel attention-based multi-scale convolutional neural network (AMCNN) to automatically detect CHF. An effective multi-scale convolution structure is designed to enhance feature extraction capability. By combining channel attention mechanism, the output feature maps of multi-scale CNN are reweighted to explore the most decisive information. To validate the proposed method, we conducted experiments under both intra- and inter- patient schemes using long term ECGs from 73 subjects. The overall accuracies yield 99.97% and 99.71% respectively, outperforming traditional CNN 98.97% accuracy and the state-of-the-art methods. Moreover, our method performs well on unbalanced data with noise at speed of 0.000127 s for recognizing per segment, which has potential values to provide reliable diagnostic advice for cardiologists.

Similarity Changes Analysis for Heart Rate Fluctuation Regularity as a New Screening Method for Congestive Heart Failure.

Congestive heart failure (CHF) is a chronic cardiovascular condition associated with dysfunction of the autonomic nervous system (ANS). Heart rate variability (HRV) has been widely used to assess ANS. This paper proposes a new HRV analysis method, which uses information-based similarity (IBS) transformation and fuzzy approximate entropy (fApEn) algorithm to obtain the fApEn_IBS index, which is used to observe the complexity of autonomic fluctuations in CHF within 24 h. We used 98 ECG records (54 health records and 44 CHF records) from the PhysioNet database. The fApEn_IBS index was statistically significant between the control and CHF groups (p < 0.001). Compared with the classical indices low-to-high frequency power ratio (LF/HF) and IBS, the fApEn_IBS index further utilizes the changes in the rhythm of heart rate (HR) fluctuations between RR intervals to fully extract relevant information between adjacent time intervals and significantly improves the performance of CHF screening. The CHF classification accuracy of fApEn_IBS was 84.69%, higher than LF/HF (77.55%) and IBS (83.67%). Moreover, the combination of IBS, fApEn_IBS, and LF/HF reached the highest CHF screening accuracy (98.98%) with the random forest (RF) classifier, indicating that the IBS and LF/HF had good complementarity. Therefore, fApEn_IBS effusively reflects the complexity of autonomic nerves in CHF and is a valuable CHF assessment tool.

Classification of Congestive Heart Failure from ECG Segments with a Multi-Scale Residual Network

Congestive heart failure (CHF) poses a serious threat to human health. Once the diagnosis of CHF is established, clinical experts need to assess the severity of CHF in a timely manner. It is proved that electrocardiogram (ECG) signals are useful for assessing the severity of CHF. However, since the ECG perturbations are subtle, it is difficult for doctors to detect the differences of ECGs. In order to help doctors to make an accurate diagnosis, we proposed a novel multi-scale residual network (ResNet) to automatically classify CHF into four classifications according to the New York Heart Association (NYHA) functional classification system. Furthermore, in order to make the reported results more realistic, we used an inter-patient paradigm to divide the dataset, and segmented the ECG signals into two different intervals. The experimental results show that the proposed multi-scale ResNet-34 has achieved an average positive predictive value, sensitivity and accuracy of 93.49%, 93.44% and 93.60% respectively for two seconds of ECG segments. We have also obtained an average positive predictive value, sensitivity and accuracy of 94.16%, 93.79% and 94.29% respectively for five seconds of ECG segments. The proposed method can be used as an auxiliary tool to help doctors to classify CHF.

Automated intra-patient and inter-patient coronary artery disease and congestive heart failure detection using EFAP-Net

Coronary artery disease (CAD) and congestive heart failure (CHF) occur worldwide, putting patients at risk of death. Researchers have developed many automatic methods for CAD and CHF classification. However, most have neglected evaluating the performance of these methods in inter-patient experiments that can guarantee their generalization in practical applications. Furthermore, the applicability of these methods to noisy and extremely unbalanced data has also not been validated. To address these issues, we propose a novel CAD and CHF classification method based on ECG fragment alignment (EFA)-principal component analysis (PCA) convolutional network (EFAP-Net). EFA is a method for eliminating heart rate differences that can ensure the component consistency of heartbeats between individuals. To deeply mine discriminative features, a network containing two convolutional layers and an output layer is used to extract high-dimensional abstract features from heartbeats. In this network, we employed PCA with a certain noise robustness to extract the suitable convolutional kernels for each layer, providing a network that is quickly trained and yielding remarkable performance in processing unbalanced data. Finally, a linear support vector machine specializing in classifying high-dimensional features is adopted as the classifier. In the intra-patient experiment, 99.84%, 99.92%, and 99.80% accuracies were achieved on balanced datasets A (normal+CAD), B (normal+CHF), and C (normal+CAD+CHF), respectively. In the inter-patient experiment, 94.64%, 98.94% and 86.86% accuracies were achieved on datasets G (normal+CAD), H (normal+CHF), and I (normal+CAD+CHF), respectively. Additionally, multi-level noisy and unbalanced ECG data were classified well. Hence, this method can be implemented to diagnose CAD and CHF effectively.

Classification of congestive heart failure with different New York Heart Association functional classes based on heart rate variability indices and machine learning

AbstractThis study aims to evaluate the effect of heart rate variability (HRV) indices on the New York Heart Association (NYHA) classification of patients with congestive heart failure and to test the effectiveness of different machine learning algorithms. Twenty‐nine long‐term RR interval recordings from subjects (aged 34 to 79) with congestive heart failure (NYHA classes I, II, and III) in MIT‐BIH Database were studied. We firstly removed the unreasonable RR intervals and segment the RR recordings with a 300‐RR interval length window. Then the multiple HRV indexes were calculated for each RR segment. Support vector machine (SVM) and classification and regression tree (CART) methods were then separately used to distinguish patients with different NYHA classes based on the selected HRV indices. Receiver operating characteristic curve analysis was finally employed as the evaluation indicator to compare the performance of the two classifiers. The SVM classifier achieved accuracy, sensitivity, and specificity of 84.0%, 71.2%, and 83.4%, respectively, whereas the CART classifier achieved 81.4%, 66.5%, and 81.6%, respectively. The area under the curve of receiver operating characteristic for the two classifiers was 86.4% and 84.7%, respectively. It is possible for accurately classifying the NYHA functional classes I, II, and III when using the combination of HRV indices and machine learning algorithms. The SVM classifier performed better in classification than the CART classifier using the same HRV indices.

Multi-Frequency Components Entropy as Novel Heart Rate Variability Indices in Congestive Heart Failure Assessment

In this paper, novel heart rate variability (HRV) indices were extracted for the autonomic nervous system (ANS) activity assessment in congestive heart failure (CHF). It has been reported that CHF is a chronic cardiovascular syndrome along with ANS dysfunction, and HRV is a useful tool for ANS assessment. The multi-frequency components Entropy (MFC-En), which is obtained by the Hilbert-Huang transform and the entropy algorithm, was proposed as novel HRV indices for analyzing ANS with CHF. This paper included 24-h HRV signals of 98 subjects collected with Holter (54 healthy, 12 low-risk CHF, and 32 high-risk CHF subjects). The MFC-En indices successfully showed a statistical significance between the control and CHF groups (p <; 0.001). The CHF classification accuracy of the MFC-En was 86.7%, while the ratio of the lowand high-frequency power was only 79.6%. Moreover, statistical significances were found among the control, low-risk CHF, and high-risk CHF groups (p <; 0.01). Therefore, the MFC-En is a useful tool for CHF assessment that revealed the ANS of CHF patient is more activated by measuring the complexity of the rhythms changes of the ANS throughout the day.

Multi-stage classification of congestive heart failure based on short-term heart rate variability

In this study, we propose an automatic system to diagnose congestive heart failure using short-term heart rate variability analysis. The system involves a multi-stage classifier. The features of heart rate variability are computed from time-domain and frequency-domain measures through power spectral density estimations of different transform methods. Nonlinear heart rate variability measures are also calculated by using Poincare plot, symbolic dynamics, detrended fluctuation analysis, and sample entropy. Different combinations of heart rate variability features are selected according to their statistical significance levels and then applied to the classifier. The first two stages of the classifier consist of simple perceptron classifiers that are trained by a genetic algorithm. Five different classifiers, namely k-nearest neighbors, linear discriminant analyses, multilayer perceptron, support vector machines, and radial basis function artificial neuronal network, are tested for the third stage. The proposed system results in a classification performance of an accuracy of 98.8%, specificity of 98.1%, and sensitivity of 100%. We show that our approach provides an effective and computationally efficient tool to automatically diagnose congestive heart failure patients.

Heart rate monitoring and therapeutic devices: A wavelet transform based approach for the modeling and classification of congestive heart failure

Heart rate monitoring and therapeutic devices include real-time sensing capabilities reflecting the state of the heart. Current circuitry can be interpreted as a cardiac electrical signal compression algorithm representing the time signal information into a single event description of the cardiac activity. It is observed that some detection techniques developed for ECG signal detection like artificial neural network, genetic algorithm, Hilbert transform, hidden Markov model are some sophisticated algorithms which provide suitable results but their implementation on a silicon chip is very complicated. Due to less complexity and high performance, wavelet transform based approaches are widely used. In this paper, after a thorough analysis of various wavelet transforms, it is found that Biorthogonal wavelet transform is best suited to detect ECG signal's QRS complex. The main steps involved in ECG detection process consist of de-noising and locating different ECG peaks using adaptive slope prediction thresholding. Furthermore, the significant challenges involved in the wireless transmission of ECG data are data conversion and power consumption. As medical regulatory boards demand a lossless compression technique, lossless compression technique with a high bit compression ratio is highly required. Furthermore, in this work, LZMA based ECG data compression technique is proposed. The proposed methodology achieves the highest signal to noise ratio, and lowest root mean square error. Also, the proposed ECG detection technique is capable of distinguishing accurately between healthy, myocardial infarction, congestive heart failure and coronary artery disease patients with a detection accuracy, sensitivity, specificity, and error of 99.92%, 99.94%, 99.92% and 0.0013, respectively. The use of LZMA data compression of ECG data achieves a high compression ratio of 18.84. The advantages and effectiveness of the proposed algorithm are verified by comparing with the existing methods.

A Spectral Exponent-Based Feature of RR Interval Data for Congestive Heart Failure Discrimination Using a Wavelet-Based Approach

Congestive heart failure (CHF) is a common chronic condition which affects several millions of people around the world. Heart rate data which are crucial information needed for diagnosis and classification of CHF are similar to several physiological signals that exhibit an extraordinary range of patterns and behaviors. In this study, the spectral exponents of RR interval data of two groups of subjects, i.e., subjects with CHF and subjects with normal sinus rhythm, obtained using a wavelet-based approach are examined where the second order Daubechies wavelets are used. The spectral exponent of RR interval data is determined from a slope of logarithm of variance of wavelet coefficients (log2var(d m,n )) versus levels of wavelet-based decomposition (m) graph. In particular, the spectral exponent is estimated from the levels of wavelet-based decomposition ranges between m = 1 and m = 3 corresponding to finer-scale components of RR interval data. The minimum of spectral exponents of epochs of RR interval data is proposed as a quantitative feature for discriminating a subject with CHF. The computational results show that the subjects with CHF can be perfectly discriminated from the subjects with normal sinus rhythm using the spectral exponent-based feature. Furthermore, the perfect CHF discrimination can be achieved using the RR interval data with epoch size as short as 128 beats (approximately 2 min).

Mode by Mode Classification of Congestive Heart Failure from Long Term HRV Analysis

In this work a new approach for automatic discrimination of Congestive Heart Failure (CHF) from Normal Sinus Rhythm (NSR) using long-term Heart Rate Variability (HRV) signals is presented. Unlike classical approaches where features are extracted from the input HRV signal, in the present work the hidden information of the signal is retrieved from its different narrow-band components. These components are extracted using the Multivariate Empirical Modes Decomposition (MEMD). Being fully data driven approach, the MEMD is well suited for processing non-stationary signals such as HRV signals. The idea is to identify physically meaningful components that convey pertinent information about the CHF. From these components statistical and geometrical features extracted and combined. We investigate the potential of the higher order statistics (Skewness and Kurtosis) combined with the Hjorth and Poincare plot parameters as features for HRV classification. Different combinations of these features are analyzed, and feature vectors are selected as an input into the binary Support Vector Machines (SVM) classifier. To evaluate the performance of the proposed strategy, experimental tests have been conducted. Results of classification show an accuracy rate around 100%. We expected that the proposed method would be helpful, in clinical diagnosis, to discriminate CHF patients from NSR subjects using long-term HRV recordings.

Prevalent cardiac, renal and cardiorenal damage in patients with advanced abdominal aortic aneurysms.

Chronic kidney disease (CKD), cardiac damage (CD) and the combination of the two are associated with increased morbidity and death in patients admitted to vascular surgery units. We assessed the prevalence of cardiac and renal damage and cardiorenal syndrome (CRS) in 563 patients with abdominal aortic aneurysms (AAA) who underwent cardiac screening before either an endovascular procedure (EVAR) or open surgery (OS) for aneurysm repair. CD was defined by ≥stage B as per the ACC/AHA classification of congestive heart failure (CHF), while CKD was defined by estimated GFR <60mL/min/1.73m(2) (CKD-EPI). Anemia [World Health Organization (WHO) guidelines] and iron deficiency (ID) (criteria for CHF patients) were also calculated. AAA patients were stratified into the following groups: CD, CKD, CRS or none of these conditions [no risk factors (NoRF)]. The prevalence of isolated cardiac and renal structural damage, of combined cardiorenal damage and of ID was 24.1, 15.0, 20.6 and 23.4%, respectively. The frequency of anemia (mostly unrecognized) among the groups increased from NoRF (12.8%)/CKD (19%)/CD (25%) up to CRS (38.8%). This large-scale observational study provides clues for the increased CD/CKD risk profiles of unselected AAA patients, and underlines the need for better identification of ID/anemia and for appropriate treatment of CKD and CD before these patients undergo EVAR/OS.

Use of discriminant analysis based on echocardiography for classification of congestive heart failure in dogs with myxomatous mitral valve disease

Mixomatous mitral valve disease (MMVD) is one of the most common cardiac abnormalities in dogs and humans that can lead to cardiac heart failure (CHF). Its diagnosis remains based on echocardiography and clinical signs. However, the early diagnose of MMVD can contribute to a better prognosis and avoid CHF. The aim of this study was to evaluate the clinical, radiographic and echocardiographic presence of CHF in dogs with MMVD in combination with a statistical model as a mathematical tool. For this purpose, dogs were divided into three groups (healthy; MMVD without CHF; and MMVD with CHF), according the clinical, radiographic and echocardiographic evaluation findings. Thus, linear discriminant functions were obtained by analyzing the variables weight, body surface area, aortic diameter, the ratio of the left atrium/aortic diameter, the ratio between the mitral regurgitation jet area and the left atrial area, vena contracta diameter and mitral valve proximal isovelocity surface area. Then, mathematical equations were established for each group of dogs. Statistical functions obtained in this study enabled to classify the dogs, regarding the presence of CHF with a probability of correct classification of 90.4%. Thus the statistical model demonstrated that it could be used as an auxiliary method for identifying CHF in dogs with MMVD.

Automated quantitative analysis of capnogram shape for COPD-normal and COPD-CHF classification.

We develop an approach to quantitative analysis of carbon dioxide concentration in exhaled breath, recorded as a function of time by capnography. The generated waveform--or capnogram--is currently used in clinical practice to establish the presence of respiration as well as determine respiratory rate and end-tidal CO 2 concentration. The capnogram shape also has diagnostic value, but is presently assessed qualitatively, by visual inspection. Prior approaches to quantitatively characterizing the capnogram shape have explored the correlation of various geometric parameters with pulmonary function tests. These studies attempted to characterize the capnogram in normal subjects and patients with cardiopulmonary disease, but no consistent progress was made, and no translation into clinical practice was achieved. We apply automated quantitative analysis to discriminate between chronic obstructive pulmonary disease (COPD) and congestive heart failure (CHF), and between COPD and normal. Capnograms were collected from 30 normal subjects, 56 COPD patients, and 53 CHF patients. We computationally extract four physiologically based capnogram features. Classification on a hold-out test set was performed by an ensemble of classifiers employing quadratic discriminant analysis, designed through cross validation on a labeled training set. Using 80 exhalations of each capnogram record in the test set, performance analysis with bootstrapping yields areas under the receiver operating characteristic (ROC) curve of 0.89 (95% CI: 0.72-0.96) for COPD/CHF classification, and 0.98 (95% CI: 0.82-1.0) for COPD/normal classification. This classification performance is obtained with a run time sufficiently fast for real-time monitoring.

Anesthetic management and outcomes for patients with pulmonary hypertension and intracardiac shunts and Eisenmenger syndrome: a review of institutional experience

Study ObjectiveTo propose a set of recommendations for the perioperative management of patients with Eisenmenger syndrome and similar physiology, based on 20 years of experience at a single institution. DesignRetrospective study of institutional outcomes of Eisenmenger syndrome patients and patients with balanced or fixed right-to-left intracardiac shunts with pulmonary hypertension undergoing noncardiac surgery. SettingSingle center, university-affiliated hospital. MeasurementsMeasurements included data from patients with Eisenmenger syndrome or similar physiology, shunt direction, right ventricular systolic pressure, congestive heart failure classification, noncardiac surgery, type of anesthesia, echocardiographic and catheterization data, mortality within 30 days of surgery, choice of monitoring, and vasopressor use. Main Results33 patients with Eisenmenger syndrome or similar physiology undergoing 53 general, regional and/or monitored anesthetic procedures were identified. Significant systemic arterial hypotension occurred in 14 individuals (26%) and oxygen desaturation in 9 (17%) patients. Administration of an intravenous (IV) vasopressor agent during induction significantly decreased the incidence of hypotension. The type of IV induction agent did not influence hemodynamic alterations, though patients who received propofol experienced a trend towards increased hypotension (83% of pts) when a vasopressor was not used. Inhalational induction, regardless of vasopressor use, was more likely to result in hypotension (60% of pts). The 30-day mortality was 3.8% (two pts). Both patients had minor elective procedures with monitored anesthesia care (MAC). ConclusionsHypotension is more common in patients with Eisenmenger syndrome and similar physiology when a vasopressor is not used during the peri-induction period, regardless of induction agent. Etomidate tended to have better hemodynamic stability than other induction agents. The use of a vasopressor is recommended. We present general recommendations for anesthesiologists and strongly recommend use of a vasopressor before or during induction to reduce hypotension along with complete avoidance of inhalational induction. Further, MAC anesthesia has been associated with perioperative and 30-day mortality.

A new approach to detect congestive heart failure using short-term heart rate variability measures.

Heart rate variability (HRV) analysis has quantified the functioning of the autonomic regulation of the heart and heart's ability to respond. However, majority of studies on HRV report several differences between patients with congestive heart failure (CHF) and healthy subjects, such as time-domain, frequency domain and nonlinear HRV measures. In the paper, we mainly presented a new approach to detect congestive heart failure (CHF) based on combination support vector machine (SVM) and three nonstandard heart rate variability (HRV) measures (e.g. SUM_TD, SUM_FD and SUM_IE). The CHF classification model was presented by using SVM classifier with the combination SUM_TD and SUM_FD. In the analysis performed, we found that the CHF classification algorithm could obtain the best performance with the CHF classification accuracy, sensitivity and specificity of 100%, 100%, 100%, respectively.

Bispectral analysis and genetic algorithm for congestive heart failure recognition based on heart rate variability

This paper proposes a congestive heart failure (CHF) recognition method that includes features calculated from the bispectrum of heart rate variability (HRV) diagrams and a genetic algorithm (GA) for feature selection. The roles of the bispectrum-related features and the GA feature selector are investigated. Features calculated from the subband regions of the HRV bispectrum are added into a feature set containing only regular time-domain and frequency-domain features. A support vector machine (SVM) is employed as the classifier. A feature selector based on genetic algorithm proceeds to select the most effective features for the classifier. The results confirm the effectiveness of including bispectrum-related features for promoting the discrimination power of the classifier. When compared with the other two methods in the literature, the proposed method (without GA) outperforms both of them with a high accuracy of 96.38%. More than 3.14% surpluses in accuracies are observed. The application of GA as a feature selector further elevates the recognition accuracy from 96.38% to 98.79%. When compared to the Isler and Kuntalp's impressive results recently published in the literature that also uses GA for feature selection, the proposed method (with GA) outperforms them with more than 2.4% surpass in the recognition accuracy. These results confirm the significance of recruiting bispectrum-related features in a CHF classification system. Moreover, the application of GA as feature selector can further improve the performance of the classifier.

Captopril inhibits the production of tumor necrosis factor-α by human mononuclear cells in patients with congestive heart failure

To study the effects of captopril on tumor necrosis factor-α produced by peripheral blood mononuclear cells (PBMC) of patients with congestive heart failure (CHF), we determined the TNF-α concentrations of culture supernatants of PBMC with and without catopril in 74 CHF patients with various heart diseases. The results showed that the supernatants concentrations of TNF-α in cultured PBMC (PBMC-TNF-α) were significantly increased in non-cachetic and cachetic CHF patients, and even higher in cachetic CHF patients, as compared with the controls (i.e., patients with New York Heart Association CHF classification I). The PBMC-TNF-α was significantly inhibited by captopril. These results demonstrate that the expression of TNF-α in PBMC is increased and can be inhibited by captopril in patients with CHF, especially in those accompanied by cachexia. This suggests that the immunomodulatory effects of captopril may contribute to its beneficial effects in heart failure patients.