Non-Invasive Heart State Monitoring an Article on Latest PPG Processing
Ayan Chatterjee1
and Uttam Kumar Roy2
1Research Associate, Master of Engineering, Department of IT Jadavpur University, Kolkata, India.
2Department of IT Jadavpur University, Kolkata, India.
Corresponding Author E-mail: ayan1.c2@gmail.com
Abstract: Health Monitoring has become one of the most important task of this century with a change in population demography to build a smart healthcare system to give proper treatment to the correct patient with reduced cost, more consistently for better living. Heart & it's related parameters are very important for good health condition. Statistics from Centers for Disease Control and Prevention, in 2008, around 616K people died of heart disease and 25% cause of total death and in 2010 the percentage grew up to 31%. High blood pressure, high cholesterol, diabetes, smoking, overweight are some of the real cause of heart disease. To determine heart state, ECG is a proven and well accepted system. But, the device is expensive and requires training. ECG sensor measures the bio-potential generated by the electrical signals that is responsible to control the expansion and contraction of heart chambers. In this article, we have focused literature review on Non-Invasive cardiovascular monitoring researches undertaken so far to provide new possibilities and research trends so that we can monitor our health better and take precautions earlier with the use and advancement of Computer Science & Technology. Here we have primarily focused on PPG signal and its application to measure important blood parameters like Glucose, HB, SP02 that indirectly or directly can provide us a status of our health when required. Recent report suggests that PPG is very useful for measuring heart rates, arterial age (with PPG derivatives), blood pressure, oxygen saturation, emotion detection, respiratory rate etc. Accurate measurement of PPG can open up new possibilities in non-invasive computer aided cardiac research for smart care-giving.
Keywords: ECG; FPS; FFT; Filter; HRV; ICA; Machine Learning; PPG; Sampling; SPO2; Spectometry; Wavelet Back to TOC