The Research And Design Of Wearable Heart Rate Variability System

New research shows that heart rate variability(HRV) to assess the human nervous, humoral factors for cardiac sinus node may have some regulatory function, but also reflects the people of the autonomic nervous system, sympathetic activity and vagal activity and related balance coordination of internal relations, can be diagnosed by heart rate variability associated with cardiovascular disease(such as vascular plaque, myocardial infarction). This paper uses a wearable way to continuously detect heart rate variability to prevent sudden illness. Using photoplethysmography end human finger sensor to obtain pulse wave physiological signals; by second-order Bessel lowpass, highpass filter pulse wave signal preliminary filtering; 430 ultra-low-power microprocessor cores MSP430FG437 and 12 bit analog to digital(ADC) is an analog pulse wave signal is sampled and analog to digital conversion. In the IAR environment to MSP430FG437 microprocessor transplant multitasking embedded operating system μC / OS-II, can be realized in constant time sampling ADC, serial communication, multi-tasking operations. Using the most advanced Bluetooth low energy(BLE) wireless communication technology in order to achieve the MSP430FG437 get the pulse wave data transfer to the PC platform and smart phones. QI standards designed to meet the convenience of wireless charging technology rechargeable button battery(LIR2032) charge. PPG data obtained after the PC platform, use MATLAG conduct preliminary analysis and research algorithms; and further research good algorithm ported to smartphones practical application; heart rate variability analysis algorithm is mainly used Hilbert-Huang transform, which includes empirical Mode Decomposition(EMD) and HT, on the PPG signal filtering. Use IMF2, IMF3, IMF4 the signal to reconstruct a new signal filtered by the second derivative signal novelty, access to the maxima. Get crest after averaging(MEAN) by statistical methods, the overall standard deviation(SDNN), the mean standard deviation(SDANN) and the difference between the average root mean square(r-MSSD) come on heart rate variability(HRV) medical evaluation, and finally after acute myocardial infarction in patients with a risk assessment carried out. This paper implements the PPG signal data acquisition hardware design, and the corresponding software development, as well as to study the variability HRV algorithms on a PC, and the feasibility of the algorithm is ported to Android smartphones, in order to achieve weight reduction system and intelligent. Using wearable heart rate variability of the system can be easily cardiovascular disease rapid assessment, which can achieve the prevention and early detection of disease, as a contribution towards their future medical wisdom.