Application of Machine Learning in Cardiac Arrhythmia

2021 ◽  
pp. 115-124
Author(s):  
Gresha S. Bhatia ◽  
Shefali Athavale ◽  
Yogita Bhatia ◽  
Tanya Mohanani ◽  
Akanksha Mittal
Keyword(s):  
Machine Learning ◽  
Cardiac Arrhythmia

scholarly journals Detection of Cardiac Arrhythmia using Machine Learning Algorithms

2019 ◽  
Vol 8 (4) ◽  
pp. 11704-11707
Keyword(s):  
Machine Learning ◽  
Cardiac Arrhythmia ◽  
Sinus Node ◽  
Research Work ◽  
Heart Rhythm ◽  
The Body ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Medical Attention ◽  
Advantages And Disadvantages

Cardiac Arrhythmia is a type of condition a human being suffers from abnormal heart rhythm. This is experienced due to the malfunctioning of electrical impulses that coordinate the heartbeat. When this happens the heartbeats slow/ fast more precisely irregularly. The rhythm of the heart is controlled by a major node called the sinus node which is present at the top of the heart, triggers the electrical pulses which make the heart to beat and pumping of blood to the body. Some of the symptoms of Cardiac Arrhythmia are fainting, unconsciousness, shortness of breath, unexpected functioning of the heart. It leads to death in minutes if medical attention is not provided. To diagnose this doctor, require to study the heart recordings evaluate heartbeats from different parts of the body accurately. It takes a lot of time to evaluate so based on the research work contributed in this field we try to propose a different approach to the same. In this paper, we compare different machine learning techniques and algorithms proposed by different authors and understand the advantages and disadvantages of the system and to bring a new system in place of the existing system where all have used the same ECG recordings from the same database of MIT-BIH. With the initial research work done by us we found out that the use of Phonocardiogram Recordings (PCG) provides more fidelity and accurate compared to ECG recordings. With the initial stage of work, we take the PCG recordings dataset and convert it to a spectrogram image and apply a convolutional neural network to predict the normal or abnormal heartbeat

scholarly journals Expert-enhanced machine learning for cardiac arrhythmia classification

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0261571
Author(s):  
Sebastian Sager ◽  
Felix Bernhardt ◽  
Florian Kehrle ◽  
Maximilian Merkert ◽  
Andreas Potschka ◽  
...  
Keyword(s):  
Machine Learning ◽  
Atrial Flutter ◽  
Cardiac Arrhythmia ◽  
Domain Knowledge ◽  
Expert Knowledge ◽  
Mathematical Optimization ◽  
Regression Problem ◽  
Additional Advantage ◽  
Inverse Simulation

We propose a new method for the classification task of distinguishing atrial fibrillation (AFib) from regular atrial tachycardias including atrial flutter (AFlu) based on a surface electrocardiogram (ECG). Recently, many approaches for an automatic classification of cardiac arrhythmia were proposed and to our knowledge none of them can distinguish between these two. We discuss reasons why deep learning may not yield satisfactory results for this task. We generate new and clinically interpretable features using mathematical optimization for subsequent use within a machine learning (ML) model. These features are generated from the same input data by solving an additional regression problem with complicated combinatorial substructures. The resultant can be seen as a novel machine learning model that incorporates expert knowledge on the pathophysiology of atrial flutter. Our approach achieves an unprecedented accuracy of 82.84% and an area under the receiver operating characteristic (ROC) curve of 0.9, which classifies as “excellent” according to the classification indicator of diagnostic tests. One additional advantage of our approach is the inherent interpretability of the classification results. Our features give insight into a possibly occurring multilevel atrioventricular blocking mechanism, which may improve treatment decisions beyond the classification itself. Our research ideally complements existing textbook cardiac arrhythmia classification methods, which cannot provide a classification for the important case of AFib↔AFlu. The main contribution is the successful use of a novel mathematical model for multilevel atrioventricular block and optimization-driven inverse simulation to enhance machine learning for classification of the arguably most difficult cases in cardiac arrhythmia. A tailored Branch-and-Bound algorithm was implemented for the domain knowledge part, while standard algorithms such as Adam could be used for training.

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