Design and Development of a Virtual Multifunctional ECG Using LabVIEW

This paper presents the design and development of a virtual multifunction meter, capable of reading ECG signals. This meter has two modules, a data acquisition and signal processing (DASP) module and an advanced artificial intelligence and machine learning-based advanced diagnostic (AD) module. For processing, a novel technique developed by combining the Pan-Tomkins Algorithm and Discrete Wavelet Transform has been applied for accurate and precise diagnosis. In addition to reading and processing ECG signals, the developed virtual meter has the capability of measuring the blood pressure of a patient. The unique feature of this meter is the Advanced Diagnostics Module, where the results obtained from the patients have been used to predict and diagnose the health of the heart and identify advanced forms of arrhythmia using Machine Learning and Artificial Intelligence. The developed virtual instrument could be directly connected to a computer or run headless, which could read, process and diagnose the collected data and log the data onto a remote server. LabView, a graphical user interface-based (GUI) software is preferred, as programs could be run on FGPA-based headless control system called myRIO, which is faster and cheaper than traditional PC-based virtual Instruments.