Electrocardiogram Data Compression Techniques for Cardiac Healthcare Systems: A Methodological Review

Objective: Globally, cardiovascular diseases (CVDs) are one of the most leading causes of death. In medical screening and diagnostic procedures of CVDs, electrocardiogram (ECG) signals are widely used. Early detection of CVDs requires acquisition of longer ECG signals. It has triggered the development of personal healthcare systems which can be used by cardio-patients to manage the disease. These healthcare systems continuously record, store, and transmit the ECG data via wired/wireless communication channels. There are many issues with these systems such as data storage limitation, bandwidth limitation and limited battery life. Involvement of ECG data compression techniques can resolve all these issues.Method: In the past, numerous ECG data compression techniques have been proposed. This paper presents a methodological review of different ECG data compression techniques based on their experimental performance on ECG records of the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database.Results: It is observed that experimental performance of different compression techniques depends on several parameters. The existing compression techniques are validated using different distortion measures.Conclusion: This study elaborates advantages and disadvantages of different ECG data compression techniques. It also includes different validation methods of ECG compression techniques. Although compression techniques have been developed very widely but the validation of compression methods is still a prospective research area to accomplish an efficient and reliable performance.     

ECG Data Compression Using Modified Run Length Encoding of Wavelet Coefficients for Holter Monitoring

ObjectiveIn cardiac patient-care, compression of long-term ECG data is essential to minimize the data storage requirement and transmission cost. Hence, this paper presents a novel electrocardiogram data compression technique which utilizes modified run-length encoding of wavelet coefficients.MethodFirst, wavelet transform is applied to the ECG data which decomposes it and packs maximum energy to less number of transform coefficients. The wavelet transform coefficients are quantized using dead-zone quantization. It discards small valued coefficients lying in the dead-zone interval while other coefficients are kept at the formulated quantized output interval. Among all the quantized coefficients, an average value is assigned to those coefficients for which energy packing efficiency is less than 99.99%. The obtained coefficients are encoded using modified run-length coding. It offers higher compression ratio than conventional run-length coding without any loss of information.ResultsCompression performance of the proposed technique is evaluated using different ECG records taken from the MIT-BIH arrhythmia database. The average compression performance in terms of compression ratio, percent root mean square difference, normalized percent mean square difference, and signal to noise ratio are 17.18, 3.92, 6.36, and 28.27 dB respectively for 48 ECG records.ConclusionThe compression results obtained by the proposed technique is better than techniques recently introduced by others. The proposed technique can be utilized for compression of ECG records of Holter monitoring.     

Rethinking Causation for Data-intensive Biology: Constraints,Cancellations, and Quantized Organisms

Complex organisms thwart the simple rectilinear causality paradigm of “necessary and sufficient,” with its experimental strategy of “knock down and overexpress.” This Essay organizes the eccentricities of biology into four categories that call for new mathematical approaches; recaps for the biologist the philosopher's recent refinements to the causation concept and the mathematician's computational tools that handle some but not all of the biological eccentricities; and describes overlooked insights that make causal properties of physical hierarchies such as emergence and downward causation straightforward. Reviewing and extrapolating from similar situations in physics, it is suggested that new mathematical tools for causation analysis incorporating feedback, signal cancellation, nonlinear dependencies, physical hierarchies, and fixed constraints rather than instigative changes will reveal unconventional biological behaviors. These include “eigenisms,” organisms that are limited to quantized states; trajectories that steer a system such as an evolving species toward optimal states; and medical control via distributed “sheets” rather than single control points.     

Cross-validation of protein structural class prediction using statistical clustering and neural networks.

We present an approach to predicting protein structural class that uses amino acid composition and hydrophobic pattern frequency information as input to two types of neural networks: (1) a three-layer back-propagation network and (2) a learning vector quantization network. The results of these methods are compared to those obtained from a modified Euclidean statistical clustering algorithm. The protein sequence data used to drive these algorithms consist of the normalized frequency of up to 20 amino acid types and six hydrophobic amino acid patterns. From these frequency values the structural class predictions for each protein (all-alpha, all-beta, or alpha-beta classes) are derived. Examples consisting of 64 previously classified proteins were randomly divided into multiple training (56 proteins) and test (8 proteins) sets. The best performing algorithm on the test sets was the learning vector quantization network using 17 inputs, obtaining a prediction accuracy of 80.2%. The Matthews correlation coefficients are statistically significant for all algorithms and all structural classes. The differences between algorithms are in general not statistically significant. These results show that information exists in protein primary sequences that is easily obtainable and useful for the prediction of protein structural class by neural networks as well as by standard statistical clustering algorithms.     

Quantization of chloroplast DNA using dot blot filter hybridization with double-stranded probes

Summary Hybridization characteristics of purified chloroplast DNA, immobilized in dot blots on nitrocellulose filters using radiolabeled chloroplast DNA restriction fragments or recombinant DNA probes were investigated. Conditions are described which provide a near linear relationship between amounts of hybridization and amounts of immobilized DNA. A standard curve constructed using such data provided a simple means for quantizing specific chloroplast DNA sequences in partially purified total DNA from protoplast extracts. Using this technique, DNA sequences corresponding to about 0.01 % of the total immobilized DNA could be detected.     

基于Mel倒谱系数和矢量量化的昆虫声音自动鉴别

为了给生产单位害虫管理的普通技术人员提供简便易操作的昆虫种类鉴别方法, 本研究把人类语音识别领域的先进技术应用于昆虫识别, 提出了一种新颖的昆虫声音自动鉴别方法, 用声音参数化技术为昆虫声纹识别设计了一种简单易行的方案。声音信号经过预处理、分段得到一系列的声音样本, 从声音样本提取Mel倒谱系数（MFCC）, 并用Linde-Buzo-Gray（LBG）算法对提取的MFCC进行矢量量化(VQ), 所得码字作为声音样本的特征模型。特征参数之间的匹配用搜索最近邻的方法实现。本文方法在包含70种昆虫声音的库中进行了试验, 取得了超过96%的识别率和理想的时间性能。试验结果证明了该方法的有效性。     

岭南槭叶色表现与色叶性状研究

岭南槭自然分布于泛珠三角低山丘陵区,春季新叶与秋冬变叶期色彩艳丽,可广泛用于岭南地区城镇绿化。选取广东省南岭国家级自然保护区、黑石顶省级自然保护区和深圳大鹏半岛国家地质公园七娘山的岭南槭天然群落为对象,开展其色叶期物候观测,量化其年周期叶片色彩值,比较研究其最佳观赏期,探究影响叶色表达的关键因子。结果显示：（1）不同地区岭南槭色叶期具有差异,南岭最佳观赏期为11月份,黑石顶为11月上旬~12月上旬,七娘山为12月份;（2）岭南槭色叶特征和土壤理化性质不同样地间差异显著（P<0.05）;（3）岭南槭红叶始期与经度、土壤速效K含量显著正相关,最大色差值与速效K呈极显著正相关,最佳观赏期持续时间与海拔、有效Cu显著负相关,最佳观赏期的最大色差值与纬度极显著负相关,红叶期持续时间与全K呈显著负相关,与有效P、有效Cu呈极显著负相关;（4）岭南槭红叶最佳观赏期由北到南逐渐延迟,海拔、经度、纬度较低,土壤中全K、有效P、有效Cu的含量较低,速效K含量较高,有利于岭南槭红叶景观的表达。     

肺癌细胞形态量化和结构特征的抽取

研究了形态十分复杂的肺癌细胞的结构特征,建立了肺癌细胞图像的抽取方法和算法以及相应的ＣＡＮＣＥＲ．ＣＥＬＬ自动分析软件。采用数学形态学的多种逻辑运算和灰度梯度法,并建立高分辨率掩模数字图像扫描方法。该方法可有效地抑制图像中的噪声和干扰,并较好地保持图像的清晰度。应用结果表明所建立的方法提高了肺癌细胞的分析速度和精度,并获得了五种类型肺癌细胞形态的量化结果。为肺癌细胞自动识别的研究提供了一技术手段     

基于小波变换的混合二维ECG数据压缩方法

提出了一种新的基于小波变换的混合二维心电(electrocardiogram,ECG)数据压缩方法。基于ECG数据的两种相关性,该方法首先将一维ECG信号转化为二维信号序列。然后对二维序列进行了小波变换,并利用改进的编码方法对变换后的系数进行了压缩编码:即先根据不同系数子带的各自特点和系数子带之间的相似性,改进了等级树集合分裂(setpartitioninghierarchicaltrees,SPIHT)算法和矢量量化(vectorquantization,VQ)算法;再利用改进后的SPIHT与VQ相混合的算法对小波变换后的系数进行了编码。利用所提算法与已有具有代表性的基于小波变换的压缩算法和其他二维ECG信号的压缩算法,对MIT/BIH数据库中的心律不齐数据进行了对比压缩实验。结果表明:所提算法适用于各种波形特征的ECG信号,并且在保证压缩质量的前提下,可以获得较大的压缩比。