Abstract
An increasing number of people are suffering from cardiac diseases, diabetes, high or low blood pressure, and many more. There are many devices available in the commercial market to measure heart rate, blood pressure, and temperature of the human body. Old fashioned blood pressure meters are cuff based and inconvenient for daily monitoring. The aim of this project is to design a platform to optimally estimate blood pressure using electrocardiogram (ECG), photoplethysmogram (PPG), Pulse Transit Time (PTT), wavelet transform and artificial neural network (ANN). This kind of blood pressure assessment may not be satisfactorily correct because the regulation of blood pressure inside the human body is complex, multivariate biological procedure. Predefined data is used to get ECG and PPG. 70% data is used for training and 30% data is used for testing in ANN to get accuracy of the system.
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