Measuring the heart rate is one critical piece of information that a health professional uses to diagnose the health state of an individual. Electrocardiogram (ECG/EKG) is essentially responsible for patient monitoring and diagnosis. The extracted feature from the ECG signal plays a vital role in diagnosis of cardiac disease. Therefore, this paper presents how to design, build, and test a cost-effective prototyping tool for ECG feature extraction and recognition. When testing a real ECG from a human subject, the developed tool can preserve useful ECG information while removing unwanted noise and interference components by adaptively determining the filtering values that directly translate to a real time analog circuit for rapid prototyping. Then a decisionmaking model which is based on the peak detection strategy is applied for automated heart rate state recognition in real-time.
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