A Qualitative Overview of Fuzzy Logic in ECG Arrhythmia Classification

@article{Farhan2018AQO,
  title={A Qualitative Overview of Fuzzy Logic in ECG Arrhythmia Classification},
  author={Ahmed Farhan and Chenxuan Wei and Toukir Ahmed},
  journal={International Journal of Engineering},
  year={2018},
  volume={5},
  pages={11761-11766}
}
Achieving elevated efficiency for the classification of the ECG signal is a noteworthy issue in the present world. Electrocardiogram (ECG) is a technique to identify heart diseases. However, the detection of the actual type of heart diseases is indispensable for further treatment. Various techniques have been invented and explored to categorize the heart diseases which are recognized as arrhythmias. This paper aims to investigate the development of various techniques of arrhythmia… 
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This research emphasizes a fuzzy inference system eradicating several types of noise from the images by determining the disparity between the focused pixel's intensity with the minimum, average, and maximum power of the chosen window.

References

SHOWING 1-10 OF 61 REFERENCES
Fuzzy Support Vector Machines for ECG Arrhythmia Detection
TLDR
It is shown that the accuracy of classification can be improved by defining appropriate membership functions and the ANFIS model of the ECG data is compared to and found consistent with the medical knowledge.
FUZZY C-MEANS CLUSTERING, NEURAL NETWORK, WT, AND HRV FOR CLASSIFICATION OF CARDIAC ARRHYTHMIA
TLDR
A combined classification system has been designed using fuzzy c-means clustering (FCMC) algorithm and neural networks to classify RR intervals of ECG signals and suggests that HRV-FCMCNN structure can generalize better and is faster than other structures.
A Neuro-Fuzzy Approach to Classification of ECG Signals for Ischemic Heart Disease Diagnosis
TLDR
This paper focuses on the neuro-fuzzy classifier called Fuzzy-Gaussian Neural Network (FGNN) to recognize the ECG signals for Ischemic Heart Disease (IHD) diagnosis and has performed the software implementation and experimented the proposed neuro- fuzzy model for IHD diagnosis.
An approach for ECG beats classification using Adaptive Neuro Fuzzy Inference System
TLDR
The useful feature of ECG signals is extracted and this features are the combination of morphological based and proper statistical features proposed, and Adaptive Neuro Fuzzy Inference System (ANFIS) will train to classify the ECG pattern.
Adaptive Neuro-Fuzzy Inference System for classification of ECG signals
TLDR
The proposed ANFIS model combined the Neural Network adaptive capabilities and the fuzzy Inference System for classification of Electrocardiogram (ECG) signals with high level of efficient of tools used with an accuracy level of more than 97%.
A Neuro-fuzzy Based Model for Analysis of an ECG Signal Using Wavelet Packet Tree
AFC-ECG : An Intelligent Fuzzy ECG Classifier
TLDR
A fuzzy classifier with orientation to smart ECG interpreters is introduced that can parameterize the incoming ECG signals and then classify them into four major types for health reference: Normal, Premature Atria Contraction, Right Bundle Block Beat, and Left Bundle Block beat.
A novel algorithm for ventricular arrhythmia classification using a fuzzy logic approach
  • Nong Wei-xin
  • Medicine
    Australasian Physical & Engineering Sciences in Medicine
  • 2016
TLDR
The validation results indicate that the proposed method has superior performance in identifying the organization level between the three types of arrhythmias (VT, OVF and DVF) and is promising for improving the appropriate therapy choice and decreasing the possibility of sudden cardiac death.
A novel approach for classification of ECG arrhythmias: Type-2 fuzzy clustering neural network
ECG arrhythmia classification based on logistic model tree
TLDR
This paper presents a diagnostic system for classification of cardiac arrhythmia from ECG data, using Logistic Model Tree (LMT) classifier, validating the choice and combined use of the current popular techniques (DWT and HRV) for cardiac arrHythmia classification.
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