An efficient detection of congestive heart failure using frequency localized filter banks for the diagnosis with ECG signals

2019 ◽  
Vol 55 ◽  
pp. 82-94 ◽  
Author(s):  
Ankit A. Bhurane ◽  
Manish Sharma ◽  
Ru San-Tan ◽  
U. Rajendra Acharya
Keyword(s):  
Heart Failure ◽  
Congestive Heart Failure ◽  
Filter Banks ◽  
Ecg Signals ◽  
Efficient Detection

Deep convolutional neural network for the automated diagnosis of congestive heart failure using ECG signals

2018 ◽  
Vol 49 (1) ◽  
pp. 16-27 ◽  
Author(s):  
U Rajendra Acharya ◽  
Hamido Fujita ◽  
Shu Lih Oh ◽  
Yuki Hagiwara ◽  
Jen Hong Tan ◽  
...  
Keyword(s):  
Neural Network ◽  
Heart Failure ◽  
Congestive Heart Failure ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Automated Diagnosis ◽  
Ecg Signals

scholarly journals ECG Classification using Machine Learning

2019 ◽  
Vol 8 (4) ◽  
pp. 2492-2494
Keyword(s):  
Heart Failure ◽  
Cardiovascular Disease ◽  
Congestive Heart Failure ◽  
Sinus Rhythm ◽  
Normal Sinus Rhythm ◽  
Original Data ◽  
Ecg Signals ◽  
Normal Sinus ◽  
Electrocardiogram Signals

Recently, the obvious increasing number of cardiovascular disease, the automatic classification research of Electrocardiogram signals (ECG) has been playing a important part in the clinical diagnosis of cardiovascular disease. Convolution neural network (CNN) based method is proposed to classify ECG signals. The proposed CNN model consists of five layers in addition to the input layer and the output layer, i.e., two convolution layers, two down sampling layers and one full connection layer, extracting the effective features from the original data and classifying the features using wavelet .The classification of ARR (Arrhythmia), CHF (Congestive Heart Failure), and NSR (Normal Sinus Rhythm) signals. The experimental results contains on ARR signals from the MIT-BIH arrhythmia,CHF signals from the BIDMC Congestive Heart Failure and NSR signals from the MIT-BIH Normal Sinus Rhythm Databases show that the proposed method achieves a promising classification accuracy of 90.63%, significantly outperforming several typical ECG classification methods.

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

Symmetry ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 2019
Author(s):  
Dengao Li ◽  
Ye Tao ◽  
Jumin Zhao ◽  
Hang Wu
Keyword(s):  
Heart Failure ◽  
New York ◽  
Congestive Heart Failure ◽  
Positive Predictive Value ◽  
Predictive Value ◽  
Heart Association ◽  
Residual Network ◽  
Ecg Signals ◽  
Multi Scale ◽  
New York Heart Association

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.

Computer-aided diagnosis of congestive heart failure using ECG signals – A review

2019 ◽  
Vol 62 ◽  
pp. 95-104 ◽  
Author(s):  
V. Jahmunah ◽  
Shu Lih Oh ◽  
Joel Koh En Wei ◽  
Edward J Ciaccio ◽  
Kuang Chua ◽  
...  
Keyword(s):  
Heart Failure ◽  
Congestive Heart Failure ◽  
Computer Aided Diagnosis ◽  
Ecg Signals ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals Classification of Atrial Fibrillation and Congestive Heart Failure using Convolutional Neural Network with Electrocardiogram

2021 ◽  
Author(s):  
Yunendah Nur Fu’adah ◽  
Ki Moo Lim
Keyword(s):  
Neural Network ◽  
Heart Failure ◽  
Atrial Fibrillation ◽  
Congestive Heart Failure ◽  
Convolutional Neural Network ◽  
Classification System ◽  
Normal Sinus Rhythm ◽  
Delayed Diagnosis ◽  
Ecg Signals

Abstract Delayed diagnosis of atrial fibrillation (AF) and congestive heart failure (CHF) can lead to death. Early diagnosis of these cardiac conditions is possible by manually analyzing electrocardiogram (ECG) signals. However, manual diagnosis is complex, owing to the various characteristics of ECG signals. Several studies have reported promising results using the automatic classification of ECG signals. The performance accuracy needs to be improved considering that an accurate classification system of AF and CHF has the potential to save a patient’s life. An optimal ECG signal classification system for AF and CHF has been proposed in this study using a one-dimensional convolutional neural network (1-D CNN) to improve the performance. A total of 150 datasets of ECG signals were modeled using the1-D CNN. The proposed 1-D CNN algorithm, provided precision values, recall, f1-score, accuracy of 100%, and successfully classified raw data of ECG signals into three conditions, which are normal sinus rhythm (NSR), AF, and CHF. The results showed that the proposed method outperformed the previous methods. This approach can be considered as an adjunct for medical personnel to diagnose AF, CHF, and NSR.

Abstract 2168: Efficient Detection of Atrial Fibrillation and Congestive Heart Failure in Single-Lead Devices

Circulation ◽  
2008 ◽  
Vol 118 (suppl_18) ◽  
Author(s):  
Randall Moorman ◽  
Yuping Xiao ◽  
Douglas Lake
Keyword(s):  
Heart Failure ◽  
Atrial Fibrillation ◽  
Congestive Heart Failure ◽  
Primary Prevention ◽  
Rr Intervals ◽  
High Entropy ◽  
Efficient Detection ◽  
Entropy Measures ◽  
Ectopic Beats ◽  
Roc Area

Patients receiving primary prevention single lead ICDs are at risk for atrial fibrillation (AF) and congestive heart failure (CHF). No such device reports AF burden, and only a single CHF measure, trans-thoracic impedance, is available. Entropy measures that count the number of matching RR intervals have promise, as AF is random (high entropy) and CHF is often marked by reduced heart rate variability (RR intervals with many matches) and ectopic beats (few matches). We designed entropy-based measures to detect AF (high entropy) and CHF (mixture of RR intervals with many and with few matches). For real-world implementation, we used only 12 RR intervals, and calculated the result every 30 minutes in 24-hour Holter monitor records from the MIT-BIH databases. The Figure shows distinction among AF, NSR and CHF records using HR and S.D. (panel A) or the new entropy-based measures. Panel A shows poor diagnostic performance of conventional measures. In Panel B, the y-axis, COSEn, is the coefficient of sample entropy. The AF records all have higher values, and the ROC area is 1.00. The x-axis is a measure of template match counts. It distinguishes between normals and CHF patients with ROC area 0.92. With only 12 RR intervals every 30 minutes, entropy calculations allow for efficient detection of AF and CHF. We propose that single lead devices can be employed as monitors in the primary prevention population, where risk of AF and CHF is high.

scholarly journals Wavelet Based Method for Congestive Heart Failure Recognition by Three Confirmation Functions

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
K. Daqrouq ◽  
A. Dobaie
Keyword(s):  
Heart Failure ◽  
Congestive Heart Failure ◽  
Recognition Performance ◽  
Additive White Gaussian Noise ◽  
Characteristic Curve ◽  
Recognition Rate ◽  
Clinical Syndrome ◽  
Signal Ratio ◽  
Feature Extraction Method ◽  
Ecg Signals

An investigation of the electrocardiogram (ECG) signals and arrhythmia characterization by wavelet energy is proposed. This study employs a wavelet based feature extraction method for congestive heart failure (CHF) obtained from the percentage energy (PE) of terminal wavelet packet transform (WPT) subsignals. In addition, the average framing percentage energy (AFE) technique is proposed, termed WAFE. A new classification method is introduced by three confirmation functions. The confirmation methods are based on three concepts: percentage root mean square difference error (PRD), logarithmic difference signal ratio (LDSR), and correlation coefficient (CC). The proposed method showed to be a potential effective discriminator in recognizing such clinical syndrome. ECG signals taken from MIT-BIH arrhythmia dataset and other databases are utilized to analyze different arrhythmias and normal ECGs. Several known methods were studied for comparison. The best recognition rate selection obtained was for WAFE. The recognition performance was accomplished as 92.60% accurate. The Receiver Operating Characteristic curve as a common tool for evaluating the diagnostic accuracy was illustrated, which indicated that the tests are reliable. The performance of the presented system was investigated in additive white Gaussian noise (AWGN) environment, where the recognition rate was 81.48% for 5 dB.

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