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面向移动数字健康诊疗系统的生理信号处理方法研究
中文摘要

健康是一个困扰人们千百年的永恒话题,随着技术手段的不断发展,人们也发展出了各式各样的全新技术来应对挑战。随着老龄化社会的到来,无论是个人家庭还是我国政府在未来的数十年里都在将面临沉重经济和人力负担。 本论文面向移动健康医疗应用,研究典型生理信号(脉搏波、心电)的降噪、特征点标定、压缩以及还原架构等信号处理方法,旨在为生理数据的提取提供具备鲁棒性和高处理精度的通用信号处理框架。本论文的工作包括以下几方面内容: 1.针对脉搏波信号容易受伪迹干扰这一现实问题,本文基于经验模态分解提出了一种自动化的高精度信号重建框架,其中包括一种全新的无窗信号评估预处理算法,一个基于主成分分析的噪声干扰程度评估测量,以及一个首次被报道的,应对经验模态分解随机性的二维信号分量筛选器。通过将三者紧密结合的方式,实现对脉搏波波形的精确重建。使用标准数据库验证,以心率估算精度为评价标准,实现了平均绝对误差1.07,标准差1.87,第一、第三四分位点分别为0.12和1.41的重构精度。 2.针对心电图在压缩感知流程中,存在区域定位不准,重构精度增强仍有提升空间的问题,提出了一种对能量敏感的重点区域动态压缩重构框架,一方面提出了对信号能量分布敏感的重点区域检测算法,另一方面则提出了基于对重点区域采取冗余压缩的多重压缩比方案。实现了在取得相近压缩比的条件下,对QRS波簇的更高精度重构。以MIT-BIH数据库验证,本文所提出的框架。 3.本文还对现有的皮肤接触纸电极做出了工艺改良,提高了电极制备的成品率和其在长期佩戴方面的可靠性。随后基于商业芯片实现了一款穿戴式的心电采集前端装置。实测结果说明,由该电极和前端所组成的柔性穿戴式系统基本能够满足日常生活中对心电信号采集的需求。 关键词:生理信号,波形重建,特征提取,压缩感知,柔性可穿戴

英文摘要

Health is an eternal topic that has plagued people for thousands of years. With the continuous development of technology, people have also developed various new technologies to meet the challenges. As an aging society is arriving, both individual families and our government will be facing a heavy economic and human burden in the coming decades. This dissertation is intended for mobile health medical applications and studies on the de-noising, feature point definition, compression as well as the reconstruction of typical physiological signals (pulse wave, ECG) aiming at providing a universal signal processing framework with robustness and high precision for the extraction of physiological data. The work of this thesis includes the following contents: 1.In view of the fact that the pulse wave signal is easily disturbed by artifacts, this paper presents an automated high-precision signal reconstruction framework based on empirical mode decomposition, including a new windowless signal evaluation pre-processing algorithm, a measurement of noise Interference Degree Based on Principal Component Analysis, and a first-mentioned two-dimensional signal component filter that counters to the randomness of Empirical Mode Decomposition. By integrating the three closely, an accurate reconstruction of the pulse waveform is achieved. Using standard database as verification and heart rate estimation as the accuracy evaluation criteria, the framework achieves a reconstruction accuracy that has an average absolute error of 1.07, a standard deviation of 1.87 and the first and third quartiles are respectively 0.12 and 1.41. 2.For ECG in the process of compressive sensing, there is a problem of regional positioning inaccuracy, and there is still room for improvement of the reconstruction accuracy, this chapter proposes an energy-sensitive dynamic compression and reconstruction framework for key regions。On the one hand, this framework proposes a key region detection algorithm sensitive to signal energy distribution. On the other hand, it proposes a multiple compression ratio scheme based on redundant compression of key regions. This achieves a more accurate reconstruction of the QRS wave cluster with similar compression ratios. 3.This article also made a technological improvement to the existing skin contact paper electrode, which improved the yield of electrode preparation and its reliability in long-term wear. Subsequently, a wearable ECG acquisition front-end device was implemented based on a commercial chip. The actual measurement results showed that the flexible wearable system composed of the electrode and the front-end can basically meet the demand for ECG signal collection in daily life. Key Words: Physiological Signal, Waveform Reconstruction, Feature Extraction, Compressed Sensing, Flexible Wearable

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