Smartphone IoT-Based Point of Care Method for Arrhythmia Detection

Andrew Boggs, Hannah Chapman, B. Askarian
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Abstract

In this paper, a novel method for continuously monitoring heart rate to detect arrhythmia is proposed. According to modern trends, wearable sensors have become promising for their use in the healthcare industry due to their convenience, ubiquity for patients, and the ability to gather real-time data. Technological advancements in new heart rate monitoring devices, such as wearable sensors and wireless monitors, are needed to help improve arrhythmia detection for patients. We propose a novel non-invasive, portable, and wireless method for monitoring heart rate by using Electrocardiogram (ECG) signals gathered using a Smartphone IoT-based system. Our experimental approach uses the measurement of peak-to-peak intervals between two successive signal peaks to estimate the heart rate of a test subject. The hardware used in the experiment includes a Node MCU Arduino platform to gather the raw data that is analyzed in MATLAB. Furthermore, a combination of filtering algorithms and peak detection of Electrocardiogram (ECG) signals is performed to remove noise and process the signals appropriately. The algorithm is tested on a healthy subject for seven minutes. Statistical data analysis is performed and the performance in terms of accuracy, sensitivity, and specificity was 96.1%, 95.2%, and 94.8% respectively.
基于智能手机物联网的心律失常检测护理点方法
本文提出了一种连续监测心率以检测心律失常的新方法。根据现代趋势,可穿戴传感器由于其便利性、患者无处不在以及收集实时数据的能力,在医疗保健行业的应用前景广阔。新的心率监测设备,如可穿戴传感器和无线监测器,需要技术进步来帮助改善患者的心律失常检测。我们提出了一种新的无创、便携式和无线方法,通过使用基于智能手机物联网系统收集的心电图(ECG)信号来监测心率。我们的实验方法使用测量两个连续信号峰值之间的峰对峰间隔来估计测试对象的心率。实验中使用的硬件包括Node MCU Arduino平台,用于采集原始数据,并在MATLAB中进行分析。此外,将滤波算法与心电图信号的峰值检测相结合,以去除噪声并对信号进行适当处理。该算法在健康受试者身上测试7分钟。对数据进行统计分析,准确率为96.1%,灵敏度为95.2%,特异度为94.8%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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