Detection Of Congestive Heart Failure Research Articles

Congestive Heart Failure Detection From ECG Signals Using Deep Residual Neural Network

The early and accurate detection of congestive heart failure (CHF) using an electrocardiogram (ECG) is of great significance for improving the survival rate of patients. Existing approaches show limited detection accuracy as they fail to capture the temporal ECG dynamics. Also, these methods lack model transparency and are often difficult to interpret. This article proposes a novel end-to-end diagnostic attention-based deep residual recurrent neural network (DA-DRRNet) that effectively captures the temporal dynamics and extracts high-level attentive representations for accurate CHF detection. Specifically, we first employ a recurrent neural network (RNN) layer to encode the temporal dynamics from the raw ECG beats. Then, multilayered RNNs with residual connections are incorporated to extract high-level feature representations hierarchically. The residual connections allow gradients in deep RNN to propagate effectively, thereby improving the network’s representation ability. Finally, an attention module identifies the hidden vectors corresponding to the diagnostically prominent ECG characteristics to form an attentive representation for improved CHF detection. Using ECG signals from the three publicly available datasets (BIDMC-CHF, PTBDB, and MIT-BIH NSRDB), the proposed method achieves an impressive accuracy of 98.57 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\%$</tex-math> </inline-formula> and nearly 100 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\%$</tex-math> </inline-formula> for beat-level and 24-h record-level diagnosis, respectively. Notably, the analysis of learned attention weights demonstrates that the proposed model focuses on the clinically relevant ECG features that characterize CHF. This model transparency and improved detection results advance research in this field and provide a reliable and transparent diagnostic system for CHF analysis.

Automatic Detection of Congestive Heart Failure Based on Multiscale Residual UNet++: From Centralized Learning to Federated Learning

Congestive heart failure (CHF) is a progressive and complex syndrome resulted from ventricular dysfunction, which is difficult to detect at early stages. Heart rate variability (HRV) has been identified as a prognostic indicator for CHF. The traditional diagnosis methods based on analyzing the electrocardiogram (ECG) are time-consuming and laborious, and the interpretation of the results is subjective. Inspired by the outstanding performance of U-shaped networks in medical image segmentation, in this article, we propose a novel end-to-end classification model based on 2000 intervals between successive R-peaks of ECG signals. The proposed model integrates the outputs of encoders, decoders, and intermediate units through a unified scale operation, which can not only preserve low-level details from the input signals but also extract the high-level pathology-related information. We further employ a variant of residual module with group convolution and squeeze-and-excitation (SE) block, enhancing the network’s expression capability. In addition, considering the challenge of collecting large and diverse samples by individual institutions, we decentralize the data across different clients and extend the proposed model with a federated version, which is able to facilitate multi-institutional collaborations while maintaining data anonymity. A total of 29 CHF patients and 177 non-CHF subjects (i.e., 54 normal sinus rhythm (NSR) subjects, 84 atrial fibrillation (AF), and 39 Apnea subjects) from PhysioBank are included in this article. The experimental results show that the proposed model outperforms the state of the art both in centralized and decentralized learning, with an accuracy of 89.83% and 87.54%, respectively. The diagnosis model trained in federated framework provides competitive performance to that in centralized learning, which demonstrates its potential of utilizing multisite data to improve CHF detection performance without sharing patient privacy.

Congestive heart failure detection based on attention mechanism-enabled bi-directional long short-term memory model in the internet of medical things

As congestive heart failure (CHF) has become an important medical problem facing the world, the detection of the CHF is very important. Internet of medical things (IoMT) has been increasing popularity as its application and function in industry 4.0 era. IoMT systems provide an opportunity for innovations and cross-discipline applications in the medical fields, that can significantly improve the efficiency. With the help of IoMT systems, patients can have in-home and on-body sensors that monitor their vitals easily and constantly, which can provide massive data for detecting disease. However, the most important thing is to find a good model to the automatic detection for massive medical data collected by IoMT systems. In this case, the paper proposes attention mechanism-enabled bi-directional long short-term memory (ABLSTM) model based on Electrocardiogram (ECG) signals, to automatically detect the CHF in IoMT systems. This method makes full use of the model features that can record long-term and short-term signal information, as well as the functions of attention mechanism with adaptive learning in local features, and effectively extracts complex features of ECG signals and performs detection. ECG signal data is from two public datasets, to train and test the proposed ABLSTM model. At the same time, for the cases with noise and data differences, we propose a preprocessing process for ECG signals, and discuss the impact of different data segmentation methods on the model performance. The experimental results show that the proposed ABLSTM model has the highest accuracy rate with 96.6% for the CHF detection, which is higher than other four baseline methods. Therefore, this proposed method can achieve a good result in the detection of the CHF.

Congested Heart Failure in Small Breeds Dogs

Mitral valve disease, commonly known as MVD, is the most common heart disease in dogs, accounting for nearly 75% of all dogs affected. The characteristic of this predominant disease includes collagen degradation, elastic fiber fragmentation and proteoglycan accumulation, leading to deterioration of the mitral valve and ultimately, congestive heart failure (CHF). Treatment to reverse or even delay the damaging effects of MVD has not yet been discovered due to a lack of awareness of the underlying processes. Furthermore, this disease in dogs has many similarities with the human MVD and may potentially serve as a translational model for studying MVD further. Therefore, it is imperative to identify the causes and underlying mechanisms associated with canine MVD in order to treat these patients more efficiently at the earliest detectable stage. MicroRNAs (miRNAs) are small, 21‐25 nucleotides in length, non‐coding RNAs that can down‐regulate the expression of protein coding genes by binding to the 3’UTR sites in their messenger RNAs (mRNA), causing either degradation or gene silencing. Prior studies have shown that one miRNA may regulate multiple mRNAs, subsequently effecting the downstream levels of gene expression. Furthermore, if miRNAs prove to be a useful biomarker of heart disease, not only may they serve as detectors of early disease onset, but they may also be useful as therapeutic agents. Given this recent data, we propose to test the hypothesis that circulating miRNAs may be useful biomarkers for detection of CHF, secondary to MVD. Liquid biopsies (blood samples) were collected in EDTA tubes from 7 healthy dogs with no prior history of heart disease and 7 dogs being treated for CHF secondary to MVD. Total serum RNA was isolated using miRNeasy Serum/Plasma Kit (Qiagen). Isolated RNA was then reverse transcribed using miScript II RT Kit (Qiagen) and lastly, miRNA was quantified using miScript SYBR Green PCR Kit, along with miScript miRNA PCR Arrays (Qiagen).

Inter-Patient Congestive Heart Failure Detection Using ECG-Convolution-Vision Transformer Network.

An attack of congestive heart failure (CHF) can cause symptoms such as difficulty breathing, dizziness, or fatigue, which can be life-threatening in severe cases. An electrocardiogram (ECG) is a simple and economical method for diagnosing CHF. Due to the inherent complexity of ECGs and the subtle differences in the ECG waveform, misdiagnosis happens often. At present, the research on automatic CHF detection methods based on machine learning has become a research hotspot. However, the existing research focuses on an intra-patient experimental scheme and lacks the performance evaluation of working under noise, which cannot meet the application requirements. To solve the above issues, we propose a novel method to identify CHF using the ECG-Convolution-Vision Transformer Network (ECVT-Net). The algorithm combines the characteristics of a Convolutional Neural Network (CNN) and a Vision Transformer, which can automatically extract high-dimensional abstract features of ECGs with simple pre-processing. In this study, the model reached an accuracy of 98.88% for the inter-patient scheme. Furthermore, we added different degrees of noise to the original ECGs to verify the model’s noise robustness. The model’s performance in the above experiments proved that it could effectively identify CHF ECGs and can work under certain noise.

ECG signals-based automated diagnosis of congestive heart failure using Deep CNN and LSTM architecture

In humans, Congestive Heart Failure (CHF) refers to the chronic progressive condition that drastically influences the pumping potentiality of the heart muscle. This CHF has the possibility of increasing health expenditure, morbidity, mortality and minimized quality of life. In this context, Electrocardiogram (ECG) is considered as the simplest and a non-invasive diagnosis method that aids in detecting and demonstrating the realizable changes in CHF. However, diagnosing CHF based on manual exploration of ECG signals is frequently impacted by errors as duration and small amplitude of the signals either investigated separately or in the integration is determined to neither specific nor sensitive. At this juncture, the reliability and diagnostic objectivity of ECG signals during the CHF detection process may be enhanced through the inclusion of automated computer-aided system. In this paper, Deep CNN and LSTM Architecture (DCNN-LSTM)-based automated diagnosis system is proposed for detecting CHF using ECG signals. In specific, CNN is included for the purpose of extracting deep features and LSTM is used for attaining the objective of CHF detection using the extracted features. This proposed DCNN-LSTM is evolved with minimal pre-processing of ECG signals and does not involve any classification process or manual engineered features during diagnosis. The experimentation of the proposed DCNN-LSTM conducted using the real time ECG signals datasets confirmed an accuracy of 99.52, sensitivity of 99.31%, specificity of 99.28%, F-Score of 98.94% and AUC of 99.9%, respectively.

Detection of congestive heart failure by mitral annular displacement in cats with hypertrophic cardiomyopathy – concordance between tissue Doppler imaging–derived tissue tracking and M-mode

IntroductionThe left ventricular systolic longitudinal function, traditionally measured by M-mode–derived mitral annular plane systolic excursion (MAPSE), is reduced in feline hypertrophic cardiomyopathy (HCM) and further reduced in cats with left-sided congestive heart failure (CHF). The objectives of this study were to compare longitudinal displacement measured by tissue tracking (TT-LD) and MAPSE in feline HCM and assess these methods’ ability to differentiate CHF from preclinical HCM. A further objective was to provide preliminary reference intervals for TT-LD. AnimalsEighty-five client-owned cats. MethodsA retrospective case–control study. Anatomical M-mode was used to record MAPSE, and TT-LD was recorded by tissue tracking. ResultsReduced longitudinal displacement measured by either MAPSE or TT-LD was significantly associated with CHF in cats with HCM (p < 0.036). Receiver-operating characteristic analysis indicated that TT-LD (AUC: 92.9%–97.9%) was more sensitive and specific than MAPSE (AUC: 85.8%–89.1%) for the detection of CHF. A diagnostic cut-off of 2.89 mm for maximal TT-LD in the left ventricular septum resulted in a sensitivity and specificity of 100% and 83.3%, while a diagnostic cut-off of 2.41 mm in the left ventricular posterior wall resulted in a sensitivity of 100% and a specificity of 90%. ConclusionsM-mode–derived mitral annular plane systolic excursion and TT-LD were strongly correlated, but not interchangeable. Longitudinal displacement measured by tissue tracking decreased more with disease severity than traditional MAPSE. Longitudinal displacement may help detect CHF in cats with HCM – with the maximal TT-LD of the left ventricular posterior wall achieving the highest AUC value.

An Improved UNet++ Model for Congestive Heart Failure Diagnosis Using Short-Term RR Intervals.

Congestive heart failure (CHF), a progressive and complex syndrome caused by ventricular dysfunction, is difficult to detect at an early stage. Heart rate variability (HRV) was proposed as a prognostic indicator for CHF. Inspired by the success of 2-D UNet++ in medical image segmentation, in this paper, we introduce an end-to-end encoder-decoder model to detect CHF using HRV signals. The developed model enhances the UNet++ model with Squeeze-and-Excitation (SE) residual blocks to extract deep features hierarchically and distinguish CHF patients from normal subjects. Two open-source databases are utilized for evaluating the proposed method, and three segment lengths of intervals between successive R-peaks are employed in comparison with state-of-the-art methods. The proposed method achieves an accuracy of 85.64%, 86.65% and 88.79% when 500, 1000 and 2000 RR intervals are utilized, respectively. It demonstrates that HRV evaluation based on deep learning can be an important tool for early detection of CHF, and may assist clinicians in achieving timely and accurate diagnoses.

Machine learning based congestive heart failure detection using feature importance ranking of multimodal features.

In this study, we ranked the Multimodal Features extracted from Congestive Heart Failure (CHF) and Normal Sinus Rhythm (NSR) subjects. We categorized the ranked features into 1 to 5 categories based on Empirical Receiver Operating Characteristics (EROC) values. Instead of using all multimodal features, we use high ranking features for detection of CHF and normal subjects. We employed powerful machine learning techniques such as Decision Tree (DT), Naïve Bayes (NB), SVM Gaussian, SVM RBF and SVM Polynomial. The performance was measured in terms of Sensitivity, Specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV), Accuracy, False Positive Rate (FPR), and area under the Receiver Operating characteristic Curve (AUC). The highest detection performance in terms of accuracy and AUC was obtained with all multimodal features using SVM Gaussian with Sensitivity (93.06%), Specificity (81.82%), Accuracy (88.79%) and AUC (0.95). Using the top five ranked features, the highest performance was obtained with SVM Gaussian yields accuracy (84.48%), AUC (0.86); top nine ranked features using Decision Tree and Naïve Bayes got accuracy (84.48%), AUC (0.88); last thirteen ranked features using SVM polynomial obtained accuracy (80.17%), AUC (0.84). The findings indicate that proposed approach with feature ranking can be very useful for automatic detection of congestive heart failure patients and can be very helpful for further decision making by the clinicians and physicians in order to decrease the mortality rate.

Automatic Detection of Congestive Heart Failure Based on a Hybrid Deep Learning Algorithm in the Internet of Medical Things

Congestive heart failure (CHF) is a chronic heart condition with heart function decline caused by various heart diseases that requires long-term treatment and affects personal life safety. Presently, CHF diagnosis is being conducted by experts, which is a nonspecific mode that is time consuming and depends on experience. Therefore, it is of great clinical value to conduct CHF recognition through automatic detection. This article proposed an automatic CHF detection model based on a hybrid deep learning algorithm that is composed of a convolutional neural network (CNN) and a recursive neural network (RNN). We also classified normal sinus heart rate signals and CHF signals based on electrocardiography (ECG) and time-frequency spectra during the RR interval. The accuracy of this algorithm was 99.93%, the sensitivity was 99.85%, and the specificity was 100% when 5-min ECG signals were analyzed. It showed a certain improvement over previous studies. We also investigated the detection of CHF patients from healthy subjects by ultrashort-term ECG, and good performance was obtained. The hybrid deep learning algorithm can make objective, accurate classifications of CHF signals and serve as an effective auxiliary tool for the clinical detection of CHF patients.

Application of Deep Neural Network for Congestive Heart Failure Detection Using ECG Signals

Congestive heart failure (CHF) is a common serious heart disease that requires a number of clinical examinations to diagnose, which are costly and time-consuming. Electrocardiogram (ECG) is widely used in the diagnosis of various cardiovascular diseases due to its advantages of non-invasive, convenient and cheap, so the automatic CHF detection algorithm based on ECG signals can overcome the above shortcomings and has great application prospects. In this paper, inspired by the idea of DenseNet in computer version, we refined it to be applicable to CHF detection task, thus improving the diagnosis accuracy of the model. Secondly, to improve the robustness of the algorithm, we built a CHF database on PhysioBank, which contained more diverse data compared with similar studies, and conducted experiments on the built database. Finally, we presented an evaluation method based on the “inter-patient” pattern to evaluate the performance of the method more objectively. The results show that our algorithm can efficiently detect CHF with accuracy, sensitivity and specificity up to 94.97%, 89.38% and 99.50%, respectively. The algorithm proposed in this study can provide reliable references for doctors, and can be used in portable devices to realize real-time monitoring for patients.

Detection of congestive heart failure by Doppler echocardiography in cats with hypertrophic cardiomyopathy.

BackgroundLeft‐sided congestive heart failure (CHF) is characterized by increased filling pressures and related Doppler echocardiographic (DE) filling patterns.HypothesisDoppler echocardiographic variables of left ventricular filling derived from transmitral flow, pulmonary vein flow, and tissue Doppler can be used to detect CHF in cats with hypertrophic cardiomyopathy (HCM).AnimalsForty‐seven client‐owned cats.MethodsProspective clinical cohort study. Cats underwent physical examination, thoracic radiography, analysis of N‐terminal pro‐brain natriuretic peptide (NT‐proBNP), and transthoracic echocardiography and were divided into 3 age‐matched groups: Group 1 (apparently healthy control), Group 2 (preclinical HCM), and Group 3 (HCM and CHF). Measured and calculated variables included respiratory rate, DE estimates, serum NT‐proBNP concentration, and radiographic CHF score. Groups were compared using ANOVA, and receiver operating characteristic (ROC) curve and multivariate analyses were used to identify diagnostic cutoffs for the detection of CHF.ResultsFifteen cats were in Group 1, 17 in Group 2, and 15 in Group 3. The ROC analysis indicated that the ratio of peak velocity of early diastolic transmitral flow to peak velocity of late diastolic transmitral flow (area under the curve [AUC], 1.0; diagnostic cutoff, 1.77; P = .001), ratio of left atrial size to aortic annular dimension (AUC, 0.91; diagnostic cutoff, 1.96; P = .003), left atrial diameter (AUC, 0.89; cutoff, 18.5 mm; P = .004), diastolic functional class (AUC, 0.89; cutoff, class 2; P = .005), respiratory (AUC, 0.79; cutoff, 36 breaths per minute [brpm]; P = .02), and the ratio of the peak velocity of fused early and late transmitral flow velocities to the peak velocity of the fused early and late diastolic tissue Doppler waveforms (AUC, 0.74; cutoff, 15.1; P = .05) performed best for detecting CHF.Conclusions and Clinical ImportanceVarious DE variables can be used to detect CHF in cats with HCM. Determination of the clinical benefit of such variables in initiating treatments and assessing treatment success needs further study.

A convolutional neural network approach to detect congestive heart failure

Congestive Heart Failure (CHF) is a severe pathophysiological condition associated with high prevalence, high mortality rates, and sustained healthcare costs, therefore demanding efficient methods for its detection. Despite recent research has provided methods focused on advanced signal processing and machine learning, the potential of applying Convolutional Neural Network (CNN) approaches to the automatic detection of CHF has been largely overlooked thus far. This study addresses this important gap by presenting a CNN model that accurately identifies CHF on the basis of one raw electrocardiogram (ECG) heartbeat only, also juxtaposing existing methods typically grounded on Heart Rate Variability. We trained and tested the model on publicly available ECG datasets, comprising a total of 490,505 heartbeats, to achieve 100% CHF detection accuracy. Importantly, the model also identifies those heartbeat sequences and ECG’s morphological characteristics which are class-discriminative and thus prominent for CHF detection. Overall, our contribution substantially advances the current methodology for detecting CHF and caters to clinical practitioners’ needs by providing an accurate and fully transparent tool to support decisions concerning CHF detection.

Detection of Congestive Heart Failure Based on LSTM-Based Deep Network via Short-Term RR Intervals.

Congestive heart failure (CHF) refers to the inadequate blood filling function of the ventricular pump and it may cause an insufficient heart discharge volume that fails to meet the needs of body metabolism. Heart rate variability (HRV) based on the RR interval is a proven effective predictor of CHF. Short-term HRV has been used widely in many healthcare applications to monitor patients’ health, especially in combination with mobile phones and smart watches. Inspired by the inception module from GoogLeNet, we combined long short-term memory (LSTM) and an Inception module for CHF detection. Five open-source databases were used for training and testing, and three RR segment length types (N = 500, 1000 and 2000) were used for the comparison with other studies. With blindfold validation, the proposed method achieved 99.22%, 98.85% and 98.92% accuracy using the Beth Israel Deaconess Medical Center (BIDMC) CHF, normal sinus rhythm (NSR) and the Fantasia database (FD) databases and 82.51%, 86.68% and 87.55% accuracy using the NSR-RR and CHF-RR databases, with N = 500, 1000 and 2000 length RR interval segments, respectively. Our end-to-end system can help clinicians to detect CHF using short-term assessment of the heartbeat. It can be installed in healthcare applications to monitor the status of human heart.

Automated detection of congestive heart failure from electrocardiogram signal using Stockwell transform and hybrid classification scheme

Background and ObjectiveThe congestive heart failure (CHF) is a life-threatening cardiac disease which arises when the pumping action of the heart is less than that of the normal case. This paper proposes a novel approach to design a classifier-based system for the automated detection of CHF. MethodsThe approach is founded on the use of the Stockwell (S)-transform and frequency division to analyze the time-frequency sub-band matrices stemming from electrocardiogram (ECG) signals. Then, the entropy features are evaluated from the sub-band matrices of ECG. A hybrid classification scheme is adopted taking the sparse representation classifier and the average of the distances from the nearest neighbors into account for the detection of CHF. The proposition is validated using ECG signals from CHF subjects and normal sinus rhythm from public databases. ResultsThe results reveal that the proposed system is successful for the detection of CHF with an accuracy, a sensitivity and a specificity values of 98.78%, 98.48%, and 99.09%, respectively. A comparison with the existing approaches for the detection of CHF is accomplished. ConclusionsThe time-frequency entropy features of the ECG signal in the frequency range from 11 Hz to 30 Hz have higher performance for the detection of CHF using a hybrid classifier. The approach can be used for the automated detection of CHF in tele-healthcare monitoring systems.

Abstract P150: Age, Gender and Race Variations in QRS Duration in Patients With Atrial Fibrillation

Background: Atrial fibrillation (AF) is associated with increased incidence of congestive heart failure (CHF). While patients with intraventricular conduction delay were shown to have increased risk of atrial fibrillation, recent analysis of a study revealed increased mortality among patients with QRS 90-119 ms and AF. The demographics of QRS duration (QRSD) in AF remain under-studied. Methods: A University-based EKG database was randomly searched for 150 total EKGs: 75 EKGs were with AF; another 75 were with sinus rhythm, as a control group. Demographics, heart rate and QRSD were documented. QRSDs were retrieved from the baseline electrocardiograms of patients included in both groups, and were compared using student’s T test. Further subgroup analyses based on age, gender and race were also performed. Results: The mean QRSD in AF group was 102 ± 22 ms compared to 92 ± 19 ms in SR group (P &lt; 0.05). In patient’s &lt; 65 years of age, QRSD in AF group was 96 ± 12 ms compared to 92 ± 19 ms in SR group (P = NS); however, In patient’s ≥ 65 years of age, QRSD in AF group was 107 ± 26 ms compared to 91 ± 18 ms in SR group (P &lt; 0.05). In males, QRSD in AF group was 102 ± 22 ms compared to 89 ± 16 ms in SR group (P &lt; 0.05); however, in females, QRSD in AF group was 94 ± 21 ms compared to 93 ± 20 ms in SR group (P = NS). In white patients, QRSD in AF group was 102 ± 20 ms compared to 90 ± 14 ms in SR group (P &lt; 0.05); however, in black patients, QRSD in AF group was 104 ± 27 ms compared to 94 ± 23 ms in SR group (P = NS). Conclusion: The incidence of AF, approaching 6 million in the US, continues to rise in epidemic proportions, and is expected to double over the next few decades as the population ages. The poor rate control in AF, in addition to the loss of the atrial contraction, are two main factors thought to be responsible for symptoms and development of CHF. In our cohort, we demonstrated a significant association between AF and widened QRSD, previously shown to correlate with poor outcome in AF patients. In age analysis, the development of intraventricular conduction delay (IVCD) was significant only in the elderly with AF, who are known to be at higher risk of CHF. IVCD was also more common in white patients compared to black patients; of interest, white patients have been previously shown to develop CHF at an older age compared to black patients, suggesting that IVCD may play a role in the CHF mechanism in this subgroup with AF. In our cohort, males, but not females, had a significantly wider QRSD in AF compared to control. Our findings shed some light on a potentially new mechanism of added morbidity and mortality in AF patients, with higher prevalence in elderly white males, a subgroup which may benefit from early detection and treatment of AF and CHF. Further studies will help validate our findings in a larger cohort and in patients with CHF diagnosis.

Congestive Heart Failure Detection Via Short-Time Electrocardiographic Monitoring For Fast Reference Advice In Urgent Medical Conditions.

This study proposed a detection approach for the congestive heart failure (CHF) by short-time electrocardiographic monitoring. Recent literature only reported that RR intervals and Heart Rate Variability (HRV) indicated key hidden information to discriminate CHF groups from healthy controls. However whether it was possible to find certain short-time electrocardiographic monitoring duration for CHF clinical diagnoses, has not been well addressed. In the study, databases of 54 normal subjects and 15 CHF patients from PhysioNet were introduced. Signals were classified into variable assessment lengths. Based on R-R intervals in the assessment length, raw R-R intervals, mean and standard deviation (STD) of R-R intervals, and clinically standard features of shortterm (5-min) Heart Rate Variability (HRV), were comparatively analyzed, while combining with classifiers of Recurrent Neural Network (RNN), Random Forest (RF), and Support Vector Machine (SVM). The Leave-one-out Cross-Validation (LOOCV) was adopted for performance verification, by which the model extracted from certain assessment length was utilized to test measured data of a subject with the same length. Results showed that based on testing databases, a specific 30-minute duration can be achieved by choosing HRV features in full with sensitivity of 88.55% and specificity of 94.81%. It was believed that a short-time electrocardiographic monitoring for the CHF detection could be feasible if standard HRV features together with the classifier of RF or RNN are adopted. It implied that a short-time electrocardiographic monitoring can be applied for fast reference advice of CHF in urgent medical conditions.

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

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.

Automatic Detection of Congestive Heart Failure and Atrial Fibrillation with Short RR Interval Time Series

Atrial fibrillation (AF) and Congestive heart failure (CHF) are increasingly widespread, costly, deadly diseases and are associated with significant morbidity and mortality. In this study, we analyzed three statistical methods for automatic detection of AF and CHF based on the randomness, variability and complexity of the heart beat interval, which is RRI time series. Specifically, we used short RRI time series with 16 beats and employed the normalized root mean square of successive RR differences (RMSSD), the sample entropy and the Shannon entropy. The detection performance was analyzed using four large well documented databases, namely the MIT-BIH Atrial fibrillation (n=23), the MIT-BIH Normal Sinus Rhythm (n=18), the BIDMC Congestive Heart Failure (n=13) and the Congestive Heart Failure RRI databases (n=25). Using thresholds by Receiver Operating Characteristic (ROC) curves, we found that the normalized RMSSD provided the highest accuracy. The overall sensitivity, specificity and accuracy for AF and CHF were 0.8649, 0.9331 and 0.9104, respectively. Regarding CHF detection, the detection rate of CHF (NYHA III-IV) was 0.9113 while CHF (NYHA I-II) was 0.7312, which shows that the detection rate of CHF with higher severity is higher than that of CHF with lower severity. For the clinical 24 hour data (n=42), the overall sensitivity, specificity and accuracy for AF and CHF were 0.8809, 0.9406 and 0.9108, respectively, using normalized RMSSD.

A Novel and Effective Method for Congestive Heart Failure Detection and Quantification Using Dynamic Heart Rate Variability Measurement.

Risk assessment of congestive heart failure (CHF) is essential for detection, especially helping patients make informed decisions about medications, devices, transplantation, and end-of-life care. The majority of studies have focused on disease detection between CHF patients and normal subjects using short-/long-term heart rate variability (HRV) measures but not much on quantification. We downloaded 116 nominal 24-hour RR interval records from the MIT/BIH database, including 72 normal people and 44 CHF patients. These records were analyzed under a 4-level risk assessment model: no risk (normal people, N), mild risk (patients with New York Heart Association (NYHA) class I-II, P1), moderate risk (patients with NYHA III, P2), and severe risk (patients with NYHA III-IV, P3). A novel multistage classification approach is proposed for risk assessment and rating CHF using the non-equilibrium decision-tree–based support vector machine classifier. We propose dynamic indices of HRV to capture the dynamics of 5-minute short term HRV measurements for quantifying autonomic activity changes of CHF. We extracted 54 classical measures and 126 dynamic indices and selected from these using backward elimination to detect and quantify CHF patients. Experimental results show that the multistage risk assessment model can realize CHF detection and quantification analysis with total accuracy of 96.61%. The multistage model provides a powerful predictor between predicted and actual ratings, and it could serve as a clinically meaningful outcome providing an early assessment and a prognostic marker for CHF patients.