Neural network based ECG arrhythmia performance optimization

Karim, Jamshed; Abedin, Md. Shajjadul; Hoque, Yasir

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dc.contributor.author	Karim, Jamshed
dc.contributor.author	Abedin, Md. Shajjadul
dc.contributor.author	Hoque, Yasir
dc.date.accessioned	2021-09-08T05:20:46Z
dc.date.available	2021-09-08T05:20:46Z
dc.date.issued	2013-11-15
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An approach to cardiac arrhythmia analysis using hidden Markov models. IEEE Trans. Biomed. Eng. 1990; 37: 826 - 835. 40. Y. H. Hu, S. Palreddy, and W. J. Tompkins, “A patient-adaptable ECG beat classifier using a mixture of experts approach,” IEEE Trans. Biomed. Eng., vol. 44, pp. 891-900, 1997.	en_US
dc.identifier.uri	http://hdl.handle.net/123456789/869
dc.description	Supervised by Dr. Mohammad Rakibul Islam, Professor, Department of Electrical and Electronic Engineering, Islamic University of Technology (IUT), A Subsidiary Organ of OIC, Dhaka, Bangladesh.	en_US
dc.description.abstract	Any disturbance in the activity of heart that can cause irregular heart rhythm is known as cardiac arrhythmia. Electrocardiogram (ECG) is one of the most promising tools for detecting different types of arrhythmia, which is necessary until it goes fatal and causes loss of life. For detecting ECG arrhythmia Different techniques have been used till now. But one problem that most of the detection methods have faced is the trade-off between speed and accuracy. So in this thesis work we have tried to create a bridge among these two. We have tried to maintain a good processing speed to detect the ECG abnormality by maintaining a good accuracy rate. In this thesis work we have mainly chosen Neural Network based algorithm to detect arrhythmia but before choosing that we have also worked with two other techniques which are Wavelet packet transform and Non-linear based model. In the detection of the abnormality of ECG beat Leverberg-Marquardt algorithm in the Neural Network model has been used as it is the mostly appraised algorithm for its high processing speed. From this thesis work, the final result creates a balance between the speed and accuracy in the detection of ECG arrhythmia which can be improved in future through more research. So this Neural Network based thesis work comes with an option of choosing a method which balances the detection speed and accuracy as well as comes with a great scope of improvement	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Electrical and Electronic Engineering, Islamic University of Technology (IUT), Board Bazar, Gazipur-1704, Bangladesh	en_US
dc.title	Neural network based ECG arrhythmia performance optimization	en_US
dc.type	Thesis	en_US