A cost-effective smartphone-based device for ankle-brachial index (ABI) detection

医学 血压 动脉硬化 脚踝 蓝牙 可穿戴计算机 远程医疗 物理疗法 计算机科学 急诊医学 内科学 物理医学与康复 医疗保健 心脏病学 外科 无线 嵌入式系统 电信 经济 经济增长
作者
Zhongyun Yuan,Xinru Huang,Pei Wan,Chun Zhang,Yixia Zhang,Bo Zhang,Jianming Wang,Hongpeng Zhang,Shengbo Sang
出处
期刊:Computer Methods and Programs in Biomedicine [Elsevier BV]
卷期号:198: 105790-105790 被引量:5
标识
DOI:10.1016/j.cmpb.2020.105790
摘要

Detectors of ankle-brachial index (ABI) are commonly used in cardiovascular patients who have high-risk levels of arteriosclerosis. Increased evidences suggest that patients with arteriosclerosis possess many risks of geriatric and chronic diseases. Meanwhile, new chronic treatments trend from the hospitals toward family and community health centers, but for arteriosclerosis cases have delivered benefits far below instrument costs. Compared to traditional devices based on cuff pressure, cuffless and non-invasive measures have wider application potential in home health care, especially in the case of physically-restricted or severely symptomatic patients. In this study, we developed a simple smartphone-based device for non-invasive ABI monitoring, which consists of four wireless cuffless limbs blood sensors. By identifying and tracking blood flow waveform, a multiparameter fusion (MPF) algorithm is used to estimate blood pressure and generate ABI value. An ARM-based chip STM32 has been adopted as the microcontroller. The ABI calculating program is embedded in C++ and executed by the processor. After generating data, ABI information can be delivered to the smartphone by using Bluetooth. Relying on mobile apps to visualize the data and display on the screen, doctors can monitor cardiovascular patients in real time and analyze the risk levels of arteriosclerosis online. In this paper, the detection conducted by the classical Doppler equipment and prototype were recorded respectively. A statistical evaluation of the verification results obtained from 29 patients and 7 sub-health volunteers is given, which shows that our device can achieve 91.80% and 93.84% accuracy for patients and sub-health volunteers, respectively. In addition, the prototype can be performed stably for a continuous long time monitoring. According to our studies, the accuracy of our device is sufficient for home medical and chronic disease monitoring within a certain time interval. The smartphone-based ABI device has several apparent advantages over traditional devices, such as portability, cost-effectiveness and energy-efficiency.

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