论文标题

消除智能手机相机捕获的指尖视频触摸错误后,从PPG信号中计算脉冲的算法

Algorithm To Calculate Pulse from PPG Signal After Eliminating Touch Errors from the Fingertip Video Captured by Smartphone Camera

论文作者

Chatterjee, Ayan, Gopalakrishnan, Sundar, Gerdes, Martin, Martinez, Santiago, Pahari, Nibedita, Khatiwada, Pankaj

论文摘要

随着全球人口中心的持续心脏问题,人们的医疗要求有望增加。心电图(ECG)是捕获心脏反应信号以评估心脏的电和肌肉功能的被证明的一种。心电图设置很昂贵,需要进行适当的培训,当然,这不是即时的。对于快速,准确的心脏参数监测,科学家会根据特定波长的光强度注意照相术信号(PPG)。具有高质量相机的Android智能手机已获得普通人的范围,并已成为当今和后代最必要,最坚固的设备之一。我们可以使用其强大的功能来捕获图像的必要数据来解决或评估心脏状态监测。移动摄像头具有发射二极管和光电探测器的照片。光源照亮了组织。光电探测器计算出与血液体积变化相关的光强度的较小变化(主要是指尖,脚趾和耳朵)。我们捕获了未使用的联系视频,以使用Android智能手机捕获PPG。然后,我们根据红色平面中的平均像素强度计数删除了一定百分比的相机触摸错误,这是这项研究中引入的一种新方法。我们使用了2阶Butterworth(IIR)带通滤波器进行噪声,FFT Hann窗口进行频率分析和泄漏降低。我们已经使用MATLAB作为开发平台开发了一种算法,以进行准确的脉冲(BPM)测量。此外,我们已经对开发算法进行了比较分析,以及用于基于PPG的脉冲计算的其他可用算法。在这项研究中,尸体休息时捕获了指尖视频

With the ongoing heart problems of the population worldwide, the medical requirements of the people are expected to increase. Electrocardiogram (ECG) is one of the proven to capture the heart response signal to assess the electrical and muscular functions of the heart. The ECG setup is expensive and needs proper training, and of course, it is not instant. For fast, accurate heart parameter monitoring, scientists pay attention to the photoplethysmogram signal (PPG), based on the light intensity of a particular wavelength. Android smartphone with a good quality camera has come to ordinary people's reach and has become one of the most necessary and rugged devices for today and future generations. We can use its powerful features to solve or assess heart state monitoring by capturing the image's necessary data. The mobile camera has a photo emitting diode and a photodetector. The light source illuminates the tissue. The photodetector calculates the small variation in light intensity associated with blood volume change in the vessels (mainly fingertips, toes, and ears). We have captured unfocused contact video to capture PPG using an Android Smartphone. Then, we removed a certain percent of camera touch errors based on average pixel intensity count in the red plane, and it is a new approach that has been introduced in this research. We used a 2nd order Butterworth (IIR) band pass filter for noise removal, FFT Hann Window for frequency analysis and leakage reduction. We have developed an algorithm using MATLAB as a development platform, for accurate pulse (BPM) measurement. Moreover, we have done a comparative analysis of developed algorithm with other available algorithms for PPG-based pulse calculation. In this study, the fingertip video was captured when the body was at rest

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