基于有损/无损的脑电数据混合压缩技术 压缩算法

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摘要翻译：
由于采样率高，记录的脑电图(EEG)数据具有较大的数据量。因此，脑电数据的存储和传输需要较大的空间和带宽。因此，对脑电数据进行预处理和压缩是一个非常重要的环节，以便在较小的带宽和空间内有效地传输和存储脑电数据。本文的目的是开发一个高效的脑电数据压缩系统。该系统首先在预处理单元中对记录的脑电数据进行预处理。该单元对脑电数据进行标准化和分割。然后，将得到的脑电数据传递到压缩单元。压缩单元由有损压缩算法和无损压缩算法组成。有损压缩算法将随机性脑电数据转换为高冗余数据。然后，加入一个无损压缩算法来研究数据的高冗余度，从而在不增加任何额外损失的情况下获得高压缩比(CR)。本文提出了离散余弦变换(DCT)和离散小波变换(DWT)作为有损压缩算法。此外，还提出了算术编码和游程编码(RLE)作为无损压缩算法。我们计算了总压缩和重建时间(T)、均方根误差(RMSE)和CR来评价所提出的系统。仿真结果表明，在DCT算法后加入RLE在压缩比和复杂度方面具有最好的性能。将DCT作为有损压缩算法，然后将RLE作为无损压缩算法，在RMSE=0.14时，其CR=90%；在RMSE=0.2时，其CR=95%以上。
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英文标题：
《Hybrid Compression Techniques for EEG Data Based on Lossy/Lossless
  Compression Algorithms》
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作者：
Madyan Alsenwi, Tawfik Ismail, and M. Saeed Darweesh
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最新提交年份：
2019
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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：
  The recorded Electroencephalography (EEG) data comes with a large size due to the high sampling rate. Therefore, large space and more bandwidth are required for storing and transmitting the EEG data. Thus, preprocessing and compressing the EEG data is a very important part in order to transmit and store it efficiently with less bandwidth and less space. The objective of this paper is to develop an efficient system for EEG data compression. In this system, the recorded EEG data are firstly preprocessed in the preprocessing unit. Standardization and segmentation of EEG data are done in this unit. Then, the resulting EEG data are passed to the compression unite. The compression unit composes of a lossy compression algorithm followed by a lossless compression algorithm. The lossy compression algorithm transforms the randomness EEG data into data with high redundancy. Subsequently, A lossless compression algorithm is added to investigate the high redundancy of the resulting data to get high Compression Ratio (CR) without any additional loss. In this paper, the Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) are proposed as a lossy compression algorithm. Furthermore, Arithmetic Encoding and Run Length Encoding (RLE) are proposed as a lossless compression algorithm. We calculate the total compression and reconstruction time (T), Root Mean Square Error (RMSE), and CR in order to evaluate the proposed system. Simulation results show that adding RLE after the DCT algorithm gives the best performance in terms of compression ratio and complexity. Using the DCT as a lossy compression algorithm followed by the RLE as a lossless compression algorithm gives CR=90% at RMSE=0.14 and more than 95% of CR at RMSE=0.2.
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PDF链接：
https://arxiv.org/pdf/1804.02713

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关键词：Applications Optimization Segmentation Construction compression DCT 空间 进行 algorithm 压缩比