Proposed Title:

An FPGA-Based Design for Feature Extraction from Myocardial Infarction with an Improved Pre-Processing Delineation Filter

Improvement of this project :

• To remodified with Myocardial Infarction proposed architecture which using pre-processing stage to improve the accuracy in ECG signal strength for peak identifications.

• The preprocessing stage architecture present with Band-pass filter, derivative filter, squaring, integrator filter and low pass filter design.

• To synthesize this design in Xilinx Vertex-5 FPGA and prove the comparisons analysis of area, delay and power consumptions.

Software Implementation:

• Modelsim
• Xilinx

Proposed System:

Myocardial infarction (MI) is a serious cardiac problem that kills millions of people globally each year. MI progresses in various stages depending on its severity, generating significant alterations in the electrocardiogram (ECG) signal. Capturing these fluctuations is crucial, requiring continual monitoring of the patient’s ECG signal. As a result, developing a low-power VLSI design is critical for addressing the MI prediction. This paper offers a totally innovative hardware solution for pre-processing based on field programmable gate arrays (FPGA). The proposed detection system is built on the basis of myocardial feature extraction, however the detection phase employs an innovative, straightforward, and effective technique. The centered derivative, feature extraction, and intermediate value theorem are used to develop the innovative approach for determining the Q Peak, R Peak, T Peak, and S Peaks. The proposed structure was implemented on FPGA using Verilog HDL, and it was synthesized using the Vertex-5 FPGA. The proposed system’s dependability, execution time, and anticipated FPGA resources were compared to those obtained with standard detection techniques to determine its effectiveness. The datasets from the MIT-BIH arrhythmia database were utilized to validate the suggested design. Finally, compare each parameter’s area, delay, and power values.

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MInSC: A VLSI Architecture for Myocardial Infarction Stages Classifier for Wearable Healthcare Applications