Modified Soft Rough Set Based ECG Signal Classification for Cardiac Arrhythmias

2015 ◽  
pp. 445-470
Author(s):  
S. Senthil Kumar ◽  
H. Hannah Inbarani
Keyword(s):  
Rough Set ◽  
Cardiac Arrhythmias ◽  
Signal Classification ◽  
Ecg Signal ◽  
Soft Rough Set

Arrhythmia Diagnosis through ECG Signal Classification Using A Hybrid CNN-LSTM Model: Comparison with current Deep Learning Approaches (Preprint)

2020 ◽  
Author(s):  
Nicos Maglaveras ◽  
Georgios Petmezas ◽  
Vassilis Kilintzis ◽  
Leandros Stefanopoulos ◽  
Andreas Tzavelis ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cardiac Arrhythmias ◽  
Model Comparison ◽  
Temporal Dynamics ◽  
Experimental Studies ◽  
Signal Classification ◽  
Ecg Signal ◽  
Learning Approaches ◽  
Different Types

BACKGROUND Electrocardiogram (ECG) recording and interpretation is the most common method used for the diagnosis of cardiac arrhythmias, nonetheless this process requires significant expertise and effort from the doctors’ perspective. Automated ECG signal classification could be a useful technique for the accurate detection and classification of several types of arrhythmias within a short timeframe. OBJECTIVE To review current approaches using state-of-the-art CNNs and deep learning methodologies in arrhythmia detection via ECG feature classification techniques and propose an optimised architecture capable of different types of arrhythmia diagnosis using publicly existing annotated arrhythmia databases from the MIT-BIH databases available at PHYSIONET (physionet.org) . METHODS A hybrid CNN-LSTM deep learning model is proposed to classify beats derived from two large ECG databases. The approach is proposed after a systematic review of current AI/DL methods applied in different types of arrhythmia diagnosis using the same public MIT-BIH databases. In the proposed architecture the CNN part carries out feature extraction and dimensionality reduction, and the LSTM part performs classification of the encoded ECG beat signals. RESULTS In experimental studies conducted with the MIT-BIH Arrhythmia and the MIT-BIH Atrial Fibrillation Databases average accuracies of 96.82% and 96.65% were noted respectively. CONCLUSIONS The proposed system can be used for arrhythmia diagnosis in clinical and mHealth applications managing a number of prevalent arrhythmias such as VT, AFIB, LBBB etc. The capability of CNNs to reduce the ECG beat signal’s size and extract its main features can be effectively combined with the LSTMs’ capability to learn the temporal dynamics of the input data for the accurate and automatic recognition of several types of cardiac arrhythmias. CLINICALTRIAL Not applicable.

scholarly journals ECG signal classification for the detection of cardiac arrhythmias using a convolutional recurrent neural network

2018 ◽  
Vol 39 (9) ◽  
pp. 094006 ◽  
Author(s):  
Zhaohan Xiong ◽  
Martyn P Nash ◽  
Elizabeth Cheng ◽  
Vadim V Fedorov ◽  
Martin K Stiles ◽  
...  
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Cardiac Arrhythmias ◽  
Signal Classification ◽  
Ecg Signal

scholarly journals On Soft Rough Topology with Multi-Attribute Group Decision Making

Mathematics ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 67 ◽  
Author(s):  
Muhammad Riaz ◽  
Florentin Smarandache ◽  
Atiqa Firdous ◽  
Atiqa Fakhar
Keyword(s):  
Decision Making ◽  
Rough Set ◽  
Group Decision Making ◽  
Topological Structure ◽  
Group Decision ◽  
Continuous Mapping ◽  
Product Topology ◽  
Closed Sets ◽  
Open Set ◽  
Soft Rough Set

Rough set approaches encounter uncertainty by means of boundary regions instead of membership values. In this paper, we develop the topological structure on soft rough set ( SR -set) by using pairwise SR -approximations. We define SR -open set, SR -closed sets, SR -closure, SR -interior, SR -neighborhood, SR -bases, product topology on SR -sets, continuous mapping, and compactness in soft rough topological space ( SRTS ). The developments of the theory on SR -set and SR -topology exhibit not only an important theoretical value but also represent significant applications of SR -sets. We applied an algorithm based on SR -set to multi-attribute group decision making (MAGDM) to deal with uncertainty.

scholarly journals Soft -Rough Set and Its Applications in Decision Making of Coronavirus

2022 ◽  
Vol 70 (1) ◽  
pp. 267-285
Author(s):  
M. A. El Safty ◽  
Samirah Al Zahrani ◽  
M. K. El-Bably ◽  
M. El Sayed
Keyword(s):  
Decision Making ◽  
Rough Set ◽  
Soft Rough Set
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