نهمین کنفرانس بین المللی فناوری و مدیریت انرژی

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کد مقاله icemt9-1161

عنوان FPGA based designing Central processing unit of Implantable Cardiac Defibrillators with low energy consumption by using CNN deep neural network

نویسندگان Alirea Keyanfar - Reza Ghaderi - Soheila Nazari

چکیده The heart is one of the most important organs of the human body. Diagnosis and timely treatment of cardiac arrhythmias are important issues in the construction of medical equipment that protects the heart. It is critical to diagnose and treat cardiac arrhythmias such ventricular fibrillation (VF) and ventricular tachycardia (VT) as soon as they are noticed. An implantable cardioverter-defibrillator (ICD) is a device designed to detect and treat ventricular tachycardia (VT) and ventricular fibrillation (VF). Signal processing and arrhythmia detection of implant defibrillator devices is one of the most important parts of these devices and should be optimal in terms of detection time and detection accuracy. In this paper, an artificial neural network based on deep learning has been designed for use in signal processing and arrhythmia detection sections of ICD defibrillator devices. The designed convolution neural network is in good condition in terms of accuracy and is also in optimal condition in terms of the number of parameters. The optimal number of parameters can increase network speed in signal processing and arrhythmia detection and can also be useful in reducing battery consumption. Finally, the designed CNN network hardware was implemented. zynq chips have the ability to process in parallel and can be useful in increasing the processing speed, so zynq chips were selected for the hardware target. After the hardware implementation stage, it is possible to proceed from the IP Core produced to design other parts of the defibrillator in the Vivado software.

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