scholarly journals A Classification System to Detect Congestive Heart Failure Using Second-Order Difference Plot of RR Intervals

2009 ◽  
Vol 2009 ◽  
pp. 1-7 ◽  
Author(s):  
R. A. Thuraisingham
Keyword(s):  
Heart Failure ◽  
Congestive Heart Failure ◽  
Success Rate ◽  
Classification System ◽  
Nearest Neighbor ◽  
Second Order ◽  
Rr Intervals ◽  
Order Difference ◽  
Holter Monitor ◽  
Difference Plot

A classification system to detect congestive heart failure (CHF) patients from normal (N) patients is described. The classification procedure uses thek-nearest neighbor algorithm and uses features from the second-order difference plot (SODP) obtained from Holter monitor cardiac RR intervals. The classification system which employs a statistical procedure to obtain the final result gave a success rate of 100% to distinguish CHF patients from normal patients. For this study the Holter monitor data of 36 normal and 36 CHF patients were used. The classification system using standard deviation of RR intervals also performed well, although it did not match the 100% success rate using the features from SODP. However, the success rate for classification using this procedure for SDRR was many fold higher compared to using a threshold. The classification system in this paper will be a valuable asset to the clinician, in the detection congestive heart failure.

Deep learning with 3D-second order difference plot on respiratory sounds

2018 ◽  
Vol 45 ◽  
pp. 58-69 ◽  
Author(s):  
Gokhan Altan ◽  
Yakup Kutlu ◽  
Adnan Özhan Pekmezci ◽  
Serkan Nural
Keyword(s):  
Deep Learning ◽  
Second Order ◽  
Respiratory Sounds ◽  
Order Difference ◽  
Difference Plot

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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