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雷达卡
摘要翻译：
光电体积描记(PPG)测量容易受到运动伪影(MA)的影响。本文提出了一种新的方法来识别MA破坏的PPG拍子，然后利用人工神经网络(ANN)对拍子形态进行校正。首先，进行节拍质量评估，通过预先训练的反馈神经网络识别干净的PPG节拍，为每个人生成参考节拍模板。利用主成分分析(PCA)对PPG数据进行分解，并利用固定能量保留进行重构。为每个PPG样本分配一个权重系数，这样当它们相乘时，修改的拍形态与参考模板匹配。利用基于粒子群优化(PSO)的方法选择最优权重向量系数来调整另一个反馈神经网络，该反馈神经网络由自动编码器从PCA重构数据中生成一组重要特征。为了实时实现，这个预先训练的神经网络在前馈模式下操作，直接为PPG的任何后续测量生成权重向量。该方法用55名人体受试者的PPG数据进行了验证。平均RMSE为0.28，信噪比提高14.54dB，波峰时间和收缩舒张峰高比的测量精度分别提高36%和47%。在IEEE Signal Processing Cup 2015挑战赛数据库中，PPearson估计的PPG与ECG导出的心率之间的相关系数为0.990。所提出的方法可用于个人健康监测应用。
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英文标题：
《Improving Photoplethysmographic Measurements under Motion Artifacts
using Artificial Neural Network for Personal Healthcare》
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作者：
Monalisa Singha Roy, Rajarshi Gupta, Jayanta K. Chandra, Kaushik Das
Sharma, and Arunansu Talukdar
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最新提交年份：
2018
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分类信息：
一级分类：Electrical Engineering and Systems Science 电气工程与系统科学
二级分类：Signal Processing 信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
Photoplethysmographic (PPG) measurements are susceptible to motion artifacts (MA) due to movement of the peripheral body parts. In this paper, we present a new approach to identify the MA corrupted PPG beats and then rectify the beat morphology using artificial neural network (ANN). Initially, beat quality assessment was done to identify the clean PPG beats by a pre-trained feedback ANN to generate a reference beat template for each person. The PPG data was decomposed using principal component analysis (PCA) and reconstructed using fixed energy retention. A weight coefficient was assigned for each PPG samples in such a way that when they are multiplied , the modified beat morphology matches the reference template. A particle swarm optimization (PSO) based technique was utilized to select the best weight weight vector coefficients to tune another feedback ANN, fed with a set of significant features generated by an auto encoder from PCA reconstructed data. For real time implementation, this pre-trained ANN was operated in feed-forward mode to directly generate the weight vectors for any subsequent measurements of PPG. The method was validated with PPG data collected from 55 human subjects. An average RMSE of 0.28 and SNR improvement of 14.54 dB was obtained, with an average improvement of 36% and 47% measurement accuracy on crest time and systolic to diastolic peak height ratio respectively. With IEEE Signal Processing Cup 2015 Challenge database, Pearson's correlation coefficient between PPG estimated and ECG derived heart rate was 0.990. The proposed method can be useful for personal health monitoring applications.
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PDF链接：
https://arxiv.org/pdf/1807.05331
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