Application of the empirical mode decomposition to ECG and HRV signals for congestive heart failure classification

2011 ◽  
Author(s):  
Mohamed Omar Ahmed Omar ◽  
Abdalla Sayd Ahmed Mohamed
Keyword(s):  
Heart Failure ◽  
Congestive Heart Failure ◽  
Empirical Mode Decomposition ◽  
Mode Decomposition ◽  
Failure Classification

Application of empirical mode decomposition (EMD) for automated identification of congestive heart failure using heart rate signals

2016 ◽  
Vol 28 (10) ◽  
pp. 3073-3094 ◽  
Author(s):  
U. Rajendra Acharya ◽  
Hamido Fujita ◽  
Vidya K. Sudarshan ◽  
Shu Lih Oh ◽  
Adam Muhammad ◽  
...  
Keyword(s):  
Heart Failure ◽  
Heart Rate ◽  
Congestive Heart Failure ◽  
Empirical Mode Decomposition ◽  
Automated Identification ◽  
Mode Decomposition

scholarly journals Empirical Mode Decomposition as a Novel Approach to Study Heart Rate Variability in Congestive Heart Failure Assessment

Entropy ◽  
2019 ◽  
Vol 21 (12) ◽  
pp. 1169
Author(s):  
Mingjing Chen ◽  
Aodi He ◽  
Kaicheng Feng ◽  
Guanzheng Liu ◽  
Qian Wang
Keyword(s):  
Heart Failure ◽  
Congestive Heart Failure ◽  
Empirical Mode Decomposition ◽  
Transfer Entropy ◽  
Intrinsic Mode Functions ◽  
Rr Interval ◽  
Novel Approach ◽  
Mode Decomposition ◽  
Novel Method ◽  
Screening Accuracy

Congestive heart failure (CHF) is a cardiovascular disease related to autonomic nervous system (ANS) dysfunction and fragmented patterns. There is a growing demand for assessing CHF accurately. In this work, 24-h RR interval signals (the time elapsed between two successive R waves of the QRS signal on the electrocardiogram) of 98 subjects (54 healthy and 44 CHF subjects) were analyzed. Empirical mode decomposition (EMD) was chosen to decompose RR interval signals into four intrinsic mode functions (IMFs). Then transfer entropy (TE) was employed to study the information transaction among four IMFs. Compared with the normal group, significant decrease in TE (*→1; information transferring from other IMFs to IMF1, p < 0.001) and TE (3→*; information transferring from IMF3 to other IMFs, p < 0.05) was observed. Moreover, the combination of TE (*→1), TE (3→*) and LF/HF reached the highest CHF screening accuracy (85.7%) in IBM SPSS Statistics discriminant analysis, while LF/HF only achieved 79.6%. This novel method and indices could serve as a new way to assessing CHF and studying the interaction of the physiological phenomena. Simulation examples and transfer entropy applications are provided to demonstrate the effectiveness of the proposed EMD decomposition method in assessing CHF.

scholarly journals The Analysis Performance of Heart Failure Classification by Using Machine Learning Techniques

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Nurul Farhana Hamzah ◽  
◽  
Nazri Mohd Nawi ◽  
Abdulkareem A. Hezam ◽  
◽  
...  
Keyword(s):  
Heart Failure ◽  
Congestive Heart Failure ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Classification Algorithms ◽  
Learning Techniques ◽  
Boosted Decision Tree ◽  
Failure Classification ◽  
Artery Disease ◽  
Using Data

Heart failure means that the heart is not pumping well as normal as it should be. A congestive heart failure is a form of heart failure that involves seeking timely medical care, although the two terms are sometimes used interchangeably. Heart failure happens when the heart muscle does not pump blood as well as it can, often referred to as congestive heart failure. Some disorders, such as heart's narrowed arteries (coronary artery disease) or high blood pressure, eventually make the heart too weak or rigid to fill and pump effectively. Early detection of heart failure by using data mining techniques has gained popularity among researchers. This research uses some classification techniques for heart failure classification from medical data. This research analyzed the performance of some classification algorithms, namely Support Vector Machine (SVM), Decision Forest (DF), and Boosted Decision Tree (BDT), to classify accurately heart failure risk data as input. The best algorithm among the three is discovered for heart failure classification at the end of this research.

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