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Wavelet based processing of physiological signals for purposes of embedded computing

机译:基于小波的生理信号处理,用于嵌入式计算

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The Wavelet Transform in its discrete form has been applied to a wide range of biomedical signals by now. Typically, its calculation is performed off-line and calculation systems suffer from limited autonomy, bulkiness and obtrusiveness. A surge in industrial, research and academic interest into telemedicine and medical embedded systems, has been noticed recently, where miniature, low-cost, autonomous and ultra-low-power devices play a major role. Such devices are usually based on microcontrollers, which in addition to other tasks need to perform signal processing, very often in real-time. This paper presents a methodology to perform wavelet transform on general purpose microcontrollers. By using its optimized versions the electrocardiogram and photoplethysmographic signals are processed in real time for the purposes of QRS complex extraction and denoising. After the theoretical considerations on wavelets and their optimization in integer arithmetic, the embedded hardware and software computation architectures are described. The following is the presentation of obtained results during intensive tests on real signals. The same approach can be applied with other signals where the embedded implementation of wavelets can be benefitial.
机译:到目前为止,离散形式的小波变换已应用于多种生物医学信号。通常,它的计算是脱机执行的,并且计算系统具有有限的自治性,笨重性和干扰性。最近,人们注意到工业界,研究界和学术界对远程医疗和嵌入式系统的兴趣激增,其中微型,低成本,自主和超低功耗设备在其中起着主要作用。这样的设备通常基于微控制器,除其他任务外,微控制器还经常需要实时执行信号处理。本文提出了一种在通用微控制器上执行小波变换的方法。通过使用其优化版本,可以实时处理心电图和光电容积描记信号,以进行QRS复数提取和降噪。在对小波及其在整数算法中的优化进行了理论考虑之后,描述了嵌入式硬件和软件计算体系结构。以下是对真实信号进行密集测试期间获得的结果的表示。相同的方法可以应用于其他信号,其中小波的嵌入实现可能是有益的。

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