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The analysis of the electrocardiogram (ECG) signal is used extensively in the diagnosis of different heart diseases. One of the major tasks to be provided is the accurate determination of the QRS complex. Due to its characteristic shape QRS detection in an ECG signal is necessary for efficient extraction of beat-to-beat intervals (RR). In this paper, a simple and reliable Field Programmable Gate Array...
Computerized interpretation of the electrocardiogram (ECG) is increasingly used to assist clinician experts to reduce times to reperfusion for patients suspected of ST-segment elevation myocardial infarction (STEMI). In this context, we have developed a system based on Field Programmable Gate Array (FPGA) for automatic detection of STEMI, using the MicroBlaze soft processor from Xilinx. More particularly,...
In this paper, we designed and implemented the Electrocardiogram (ECG)signal preprocessing into a Field Programmable Gate Array (FPGA), and extracted ECG signal's feature. It performed ECG signal preprocessing and heart rate variability (HRV) analysis which is appropriate for remote homecare and health care applications. The hardware design was developed in Verilog hardware description language. FPGA...
This paper implements an electrocardiogram (ECG) feature extraction system onto a Field Programmable Gate Array (FPGA). The algorithm extracts ECG features including R peaks, QRS onsets, Q peaks, S peaks, QRS offsets, P onsets, P peaks, P offsets, T onsets, T peaks, and T offsets based on multi-scale analysis in Gabor Wavelet Transform (GWT); and estimates the amplitudes of P, R, T peaks and Q, S...
Commonly sleep stages detection can be done using electroencephalogram (EEG) that is recorded in hospitals using Polysomnography (PSG) systems. PSG not only records brain signal but also electrocardiogram (ECG). In this paper an automatic sleep stages detection using FNGLVQ algorithm based solely on ECG signal is reported. We have compared two neural network algorithms' accuracies and implemented...
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