Wearable Arteriovenous Fistula Murmur Monitoring System Based on Embedded Wi-Fi Technology

2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)(2018)

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Abstract
Factors like the increase in the average age of the population, the malignant disease induced by bad habits, and the drug damage have led to the gradual increase of the incidence of chronic kidney disease worldwide. Most of them will develop end-stage renal disease, and eventually need renal replacement therapy. Hemodialysis is the most commonly used renal replacement therapy for end-stage renal disease patients. Autogenous arteriovenous fistula (AVF) is the main way to maintain hemodialysis. For a variety of reasons, AVF can be blocked. Once the blockage of the internal fistula is detected in time, early intervention can dredge the fistula vessels. If the blockage time is too long (> 24 hours), the probability of recanalization of the fistula will be greatly reduced. Therefore, early detection of internal fistula obstruction has important clinical significance. At present, angiography and ultrasonic technology are widely used in monitoring the flow of internal fistula. However, these methods may be invasive and require professional operation. Based on the characteristic of the sound stethoscope and the embedded Wi-Fi technology, combined with the electret pickup and its signal conditioning circuit, a portable wireless internal fistula murmurs acquisition system is designed in this study. The overall system size is 2.5cm*3cm*0.5cm, and the mass is less than 4 grams. The average power consumption of the system is less than 20mA and standby power consumption is less than 5mA. This system meets the requirements of wearable intelligent equipment. In order to effectively judge the obstruction of internal fistula, this study also proposed an algorithm for the characteristics of the time domain waveform of internal fistula murmurs, which can accurately diagnose the obstruction of internal fistula (accuracy:>95%).
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Key words
wearable smart device, embedded Wi-Fi technology, AVF murmur signal acquisition, AVF murmur characteristics, AVF blockage estimation
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