The research is all about designing and implementing a PC-Based heart-beat and ECG monitoring system for biomedical and telemedicine use. The systems’ purpose is to display the heartbeat of a patient on a LCD in terms of beats per minute (bpm). It is also used for acquiring, displaying and storing patient’s ECG signal and later transferring it to a remote site. The project is designed in a way that it uses a data acquisition system which consists of both hardware and software development. The hardware includes sensors, electrodes, amplifier, a microcontroller, LCD and filter circuits while the software development includes a Graphical User Interface to display patient’s ECG waveform, LabVIEW and ZigBee software. The developed system is able to acquire and display patient’s heart-rate in terms of bpm and ECG signal value on LCD and the ECG graph will be displayed on the PC through the ZigBee software which is used for wireless transmission between transmitter and receiver. The signal collected will then be used for further analysis and transmitted to a remote terminal through the telemedicine network. This project provides a complete fusion of healthcare with minimal mobility. Although this study attempts to track patient’s cardiac data, similarly it could be used to monitor other important factors such as body temperature, blood-sugar content, weight, breathing pattern, and lipid levels of a patient. Medical engineering support systems that are controlled by neural networks are being applied with increasing frequency in medical practice. However, there is still a need to find a solution to the problem of constructing medical support systems that can be set up by the physicians themselves without the need to have knowledge of the mathematical theories of neural networks and signal processing. On the basis of typical pathological types of ECG signals, simulated by a common simulator which is used in Europe for heart beat monitoring, we show the basic structure of normal and pathological heart beat signatures and how they can be presented in a new and readily interpretable display.

1. Bronzino, Joseph D., The Biomedical Engineering Handbook, IEEE Press, 2002.
2. Joseph J. Carr, John M. Brown, Introduction to Biomedical Equipment Technology, Prentice Hall, 1998.
3. Jon B. Olansen, Eric Rosow, Virtual Bio-Instrumentation, Prentice Hall, 2002.
4. M. Reuter, 'ECG-signatures analysis by adaptive Fdspectra in combination with fast conditional neural nets or Fuzzy classificators', TU Clausthal 1995.
5. Phillips, R.E., and Feeney, M.K., 1980, ”The Cardiac Rhythms”, Second Edition, W.B. Saunders.
6. Bennett, D.H., 1985, ”Cardiac Arrhythmias“, Second Edition, Wright, Bristol.

ECG signal Heart-beat ZigBee LCD ANN Medical instrumentation biomedical education