Heart Arrhythmia Detection using Deep Learning (LSTM)

Using the resources Deep Learning and Medical has to offer, a model is created which is capable enough to detect the abnormalities in the sinusoidal rhythm of the heart which will be detected using Neural Networking (creating different layers and assigning different variables to them, so to get the...

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Bibliographic Details
Main Authors:	Agrawal, Nikhil M, Cheitanya, Hd Bhanu, Chawla, Paras, Singh, Ranjana, Mahajan, Shubham, Pandit, Amit Kant, Sharma, Sanjay
Format:	Conference Proceeding
Language:	eng
Subjects:	Analytical models Arrhythmia Bi Directional LSTM Communication systems Deep learning ECG/EKG Heart Heart Arrhythmia Neural networks Predictive models
Online Access:	Request full text
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Summary:	Using the resources Deep Learning and Medical has to offer, a model is created which is capable enough to detect the abnormalities in the sinusoidal rhythm of the heart which will be detected using Neural Networking (creating different layers and assigning different variables to them, so to get the desired outcome at a higher accuracy). The use of Neural Network will enable us to circumvent any hinderance in the outcome as medical results might affect the entropy at a personal level. This will predict the outcome after analysing the R sinusoidal wave, which then to complicate the model, then will be supplemented with P, Q and S sinusoidal waves further to enhance the result and in exchange reducing the accuracy a little bit.
ISSN:	2473-5655