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.

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

The place of beta-adrenoreceptor-blocking drugs in the treatment of congestive heart failure in elderly people: review of current opinion

1998 ◽  
Vol 8 (1) ◽  
pp. 89-92
Author(s):  
L Woosnam ◽  
M Hasan
Keyword(s):  
Heart Failure ◽  
Congestive Heart Failure ◽  
Angiotensin Converting Enzyme ◽  
Elderly People ◽  
Ace Inhibitors ◽  
Survival Benefit ◽  
Clinical Syndrome ◽  
Morbidity And Mortality ◽  
Converting Enzyme ◽  
Current Opinion

The clinical syndrome of heart failure affects more than 1% of the population, and its prevalence increases steeply with advancing age, especially after 75, where it reaches approximately 10%. It is one of the commonest reasons for the admission of elderly people to hospital.Despite recent advances in treatment of heart failure with angiotensin-converting enzyme (ACE) inhibitors, which were proven to provide survival benefit, the rates of morbidity and mortality from heart failure remain high, and new therapeutic strategie s are needed.

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

Phase Autocorrelation Bark Wavelet Transform (PACWT) Features for Robust Speech Recognition

2015 ◽  
Vol 40 (1) ◽  
pp. 25-31 ◽  
Author(s):  
Sayf A. Majeed ◽  
Hafizah Husain ◽  
Salina A. Samad
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Speech Recognition ◽  
Extraction Method ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Feature Extraction Method ◽  
Female Data ◽  
Recognition Systems ◽  
New Feature

Abstract In this paper, a new feature-extraction method is proposed to achieve robustness of speech recognition systems. This method combines the benefits of phase autocorrelation (PAC) with bark wavelet transform. PAC uses the angle to measure correlation instead of the traditional autocorrelation measure, whereas the bark wavelet transform is a special type of wavelet transform that is particularly designed for speech signals. The extracted features from this combined method are called phase autocorrelation bark wavelet transform (PACWT) features. The speech recognition performance of the PACWT features is evaluated and compared to the conventional feature extraction method mel frequency cepstrum coefficients (MFCC) using TI-Digits database under different types of noise and noise levels. This database has been divided into male and female data. The result shows that the word recognition rate using the PACWT features for noisy male data (white noise at 0 dB SNR) is 60%, whereas it is 41.35% for the MFCC features under identical conditions

scholarly journals PENERAPAN POSISI SEMI FOWLER TERHADAP KETIDAKEFEKTIFAN POLA NAFAS PADA PASIEN CONGESTIVE HEART FAILURE (CHF)

2020 ◽  
Vol 1 (1) ◽  
pp. 19-24
Author(s):  
Ahmad Muzaki, Yuli Ani Yuli Ani
Keyword(s):  
Heart Failure ◽  
Congestive Heart Failure ◽  
Breathing Pattern ◽  
Heart Function ◽  
Clinical Syndrome ◽  
Shortness Of Breath ◽  
Breathing Patterns ◽  
Study Approach ◽  
Lung Expansion

Congestive Heart Failure or more commonly known as Heart failure is a disease of clinical syndrome that is characterized by shortness of breath and fatique at rest or during activities caused by structural abnormalities or heart function. CHF patients with ineffective breathing patterns need to be given a semifowler position. Objective: is given this position to decrease oxygen consumption and increase maximal lung expansion, so the ineffectiveness of the client breathing pattern is more optimal CHF patients at RSUD Wates. Method: This type of research is descriptive using case study approach method. Subjects in this case study were two patients who had CHF patient with ineffective breathing pattern. Semifowler interventions is given where the position of the head and body is increased with declivity 45°. Results: The application of semi-fowler position (sitting position 45 °) for 3x24 hours in accordance with the SOP helps reduce shortness of breath and helps optimize RR on  the client so that the problem of ineffective breathing patterns can be overcome. Conclusion: Significant sleep angle adjustment interventions can produce good respiration, so it can be considered as one of the  interventions to maximize the ineffectiveness of breathing patterns.

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.

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