Modified SPIHT wavelet compression for ECG signal

This paper presents a modified version of Set Partitioning In Hierarchical Trees (SPIHT) wavelet compression method, which has been developed for ECG signal compression. Two more steps in the existing technique have been added to achieve higher compression ratio (CR) and lower percentage rms difference (PRD). The method has been tested on selected records from the MIT-BIH arrhythmia database. Even with two more steps, the method retains its simplicity, computational efficiency and self-adaptiveness, without compromising on any other performance parameter.     

Modified SPIHT wavelet compression for ECG signal

This paper presents a modified version of Set Partitioning In Hierarchical Trees (SPIHT) wavelet compression method, which has been developed for ECG signal compression. Two more steps in the existing technique have been added to achieve higher compression ratio (CR) and lower percentage rms difference (PRD). The method has been tested on selected records from the MIT-BIH arrhythmia database. Even with two more steps, the method retains its simplicity, computational efficiency and self-adaptiveness, without compromising on any other performance parameter.     

A new hybrid algorithm for ECG signal compression based on the wavelet transformation of the linearly predicted error.

This paper describes a hybrid technique based on the combination of wavelet transform and linear prediction to achieve very effective electrocardiogram (ECG) data compression. First, the ECG signal is wavelet transformed using four different discrete wavelet transforms (Daubechies, Coiflet, Biorthogonal and Symmlet). All the wavelet transforms are based on dyadic scales and decompose the ECG signals into five detailed levels and one approximation. Then, the wavelet coefficients are linearly predicted, where the error corresponding to the difference between these coefficients and the predicted ones is minimized in order to get the best predictor. In particular, the residuals of the wavelet coefficients are uncorrelated and hence can be represented with fewer bits compared to the original signal. To further increase the compression rate, the residual sequence obtained after linear prediction is coded using a newly developed coding technique. As a result, a compression ratio (Cr) of 20 to 1 is achieved with percentage root-mean square difference (PRD) less than 4%. The algorithm is compared to an alternative compression algorithm based on the direct use of wavelet transforms. Experiments on selected records from the MIT-BIH arrhythmia database reveal that the proposed method is significantly more efficient in compression. The proposed compression scheme may find applications in digital Holter recording, in ECG signal archiving and in ECG data transmission through communication channels.     

基于改进小波阈值算法的ECG肌电信号去噪研究

为了解决传统软、硬阈值算法对肌电信号去噪后心电图（ECG）信号幅值降低和存在局部异常尖峰,导致去噪效果较差的问题。通过研究小波阈值算法的去噪原理和优化规则,基于双曲正切函数构造出一种具有连续性、结构简单、灵活性较高的可调阈值函数和改进的分层阈值,并分析得到小波分解含噪ECG信号的最佳小波基函数和分解层数,提出了一种改进的小波阈值算法。将软、硬阈值算法、相关文献中的阈值算法和本文所提改进阈值算法对含有真实肌电信号噪声的ECG信号进行去噪对比研究。实验结果表明：本文改进阈值算法能较好地去除ECG信号中的肌电信号噪声,并能更好地保持ECG信号波形特征,且Pearson相关系数值大于其他阈值算法。定性和定量结果表明,本文所提改进阈值算法对ECG肌电信号噪声具有较好的去噪效果。     

ECG signal compression by multi-iteration EZW coding for different wavelets and thresholds

The modified embedded zero-tree wavelet (MEZW) compression algorithm for the one-dimensional signal was originally derived for image compression based on Shapiro's EZW algorithm. It is revealed that the proposed codec is significantly more efficient in compression and in computation than previously proposed ECG compression schemes. The coder also attains exact bit rate control and generates a bit stream progressive in quality or rate. The EZW and MEZW algorithms apply the chosen threshold values or the expressions in order to specify that the significant transformed coefficients are greatly significant. Thus, two different threshold definitions, namely percentage and dyadic thresholds, are used, and they are applied for different wavelet types in biorthogonal and orthogonal classes. In detail, the MEZW and EZW algorithms results are quantitatively compared in terms of the compression ratio (CR) and percentage root mean square difference (PRD). Experiments are carried out on the selected records from the MIT-BIH arrhythmia database and an original ECG signal. It is observed that the MEZW algorithm shows a clear advantage in the CR achieved for a given PRD over the traditional EZW, and it gives better results for the biorthogonal wavelets than the orthogonal wavelets.     

一种基于S3C2440的嵌入式实时心电数据压缩算法

目的 为满足嵌入式移动无线终端传输高采样率心电信号的需要,设计一种实时心电数据压缩算法.方法 根据心电数据自身特点,在嵌入式S3C2440平台上,以Huffman算法、LZ77算法及LZW算法进行心电数据压缩并比较分析,在此基础上设计了一阶差分结合Huffman算法和LZ77算法的混合压缩算法.结果 心电数据的压缩结果显示,该算法压缩比达7.20,平均计算时间392 ms,与普通压缩算法相比具有更高的心电压缩比和更低的时间复杂度.结论 将该压缩算法运用到远程无线监测终端中能满足系统设计的要求.     

A real-time ECG data compression algorithm based on S3C2440

目的 为满足嵌入式移动无线终端传输高采样率心电信号的需要,设计一种实时心电数据压缩算法.方法 根据心电数据自身特点,在嵌入式S3C2440平台上,以Huffman算法、LZ77算法及LZW算法进行心电数据压缩并比较分析,在此基础上设计了一阶差分结合Huffman算法和LZ77算法的混合压缩算法.结果 心电数据的压缩结果显示,该算法压缩比达7.20,平均计算时间392 ms,与普通压缩算法相比具有更高的心电压缩比和更低的时间复杂度.结论 将该压缩算法运用到远程无线监测终端中能满足系统设计的要求.     

A chaos-based model for low complexity predictive coding scheme for compression and transmission of electroencephalogram data

A method for low complexity, low bit rate transmission of EEG (electroencephalogram) data, based on chaotic principles, is presented. The EEG data is assumed to be generated by a non-linear dynamical system of E dimensions. The E dynamical variables are reconstructed from the one-dimensional time series by the process of time-delay embedding. A model of the form X[n+1]=F(X[n], X[n−1],...X[n−p]) is fitted for the data in the E-dimensional space and this model is used as predictor in the predictive coding scheme for transmission. This model is able to give a reduction of nearly 50% of the dynamic range of the error signal to be transmitted, with a reduced complexity, when compared to the conventionally used linear prediction method. This implies that a reduced bit rate of transmission with a reduced complexity can be obtained. The effects of variation of model parameters on the complexity and bit rate are discussed.     

A chaos-based model for low complexity predictive coding scheme for compression and transmission of electroencephalogram data

A method for low complexity, low bit rate transmission of EEG (electroencephalogram) data, based on chaotic principles, is presented. The EEG data is assumed to be generated by a non-linear dynamical system of E dimensions. The E dynamical variables are reconstructed from the one-dimensional time series by the process of time-delay embedding. A model of the form X[n + 1] = F(X[n], X[n - 1], ... , X[n - p]) is fitted for the data in the E-dimensional space and this model is used as predictor in the predictive coding scheme for transmission. This model is able to give a reduction of nearly 50% of the dynamic range of the error signal to be transmitted, with a reduced complexity, when compared to the conventionally used linear prediction method. This implies that a reduced bit rate of transmission with a reduced complexity can be obtained. The effects of variation of model parameters on the complexity and bit rate are discussed.     

A hybrid ECG compression algorithm based on singular value decomposition and discrete wavelet transform

Increasing use of computerized ECG processing systems requires effective electrocardiogram (ECG) data compression techniques which aim to enlarge storage capacity and improve data transmission over phone and internet lines. This paper presents a compression technique for ECG signals using the singular value decomposition (SVD) combined with discrete wavelet transform (DWT). The central idea is to transform the ECG signal to a rectangular matrix, compute the SVD, and then discard small singular values of the matrix. The resulting compressed matrix is wavelet transformed, thresholded and coded to increase the compression ratio. The number of singular values and the threshold level adopted are based on the percentage root mean square difference (PRD) and the compression ratio required. The technique has been tested on ECG signals obtained from MIT-BIH arrhythmia database. The results showed that data reduction with high signal fidelity can thus be achieved with average data compression ratio of 25.2:1 and average PRD of 3.14. Comparison between the obtained results and recently published results show that the proposed technique gives better performance.     

A hybrid ECG compression algorithm based on singular value decomposition and discrete wavelet transform

Increasing use of computerized ECG processing systems requires effective electrocardiogram (ECG) data compression techniques which aim to enlarge storage capacity and improve data transmission over phone and internet lines. This paper presents a compression technique for ECG signals using the singular value decomposition (SVD) combined with discrete wavelet transform (DWT). The central idea is to transform the ECG signal to a rectangular matrix, compute the SVD, and then discard small singular values of the matrix. The resulting compressed matrix is wavelet transformed, thresholded and coded to increase the compression ratio. The number of singular values and the threshold level adopted are based on the percentage root mean square difference (PRD) and the compression ratio required. The technique has been tested on ECG signals obtained from MIT-BIH arrhythmia database. The results showed that data reduction with high signal fidelity can thus be achieved with average data compression ratio of 25.2:1 and average PRD of 3.14. Comparison between the obtained results and recently published results show that the proposed technique gives better performance.     

Optimisation algorithms for ECG data compression

The use of exact optimisation algorithms for compressing digital electrocardiograms (ECGs) is demonstrated. As opposed to traditional time-domain methods, which use heuristics to select a small subset of representative signal samples, the problem of selecting the subset is formulated in rigorous mathematical terms. This approach makes it possible to derive algorithms guaranteeing the smallest possible reconstruction error when a bounded selection of signal samples is interpolated. The proposed model resembles well-known network models and is solved by a cubic dynamic programming algorithm. When applied to standard test problems, the algorithm produces a compressed representation for which the distortion is about one-half of that obtained by traditional time-domain compression techniques at reasonable compression ratios. This illustrates that, in terms of the accuracy of decoded signals, existing time-domain heuristics for ECG compression may be far from what is theoretically achievable. The paper is an attempt to bridge this gap.     

一种基于小波域主成分分析的心电压缩算法

目的为降低心电信号存储和传输的数据量,并克服传统心电压缩方法只利用导联内相关性的劣势,本文提出一种基于小波域主成分分析和分层编码(w PCA_LC)的压缩方法。方法首先通过心电电极获取12通道心电数据,对所有通道的心电信号做小波变换,每个尺度下的小波系数组成小波系数矩阵,在每个系数矩阵上做主成分分析(principal component analysis,PCA),之后对小波系数小的主成分做[位置增量,数据]的编码方式,其他主成分采用霍夫曼编码,最后使用本文算法压缩圣彼得堡心率失常数据库。结果实验表明,在均方根误差为5.2%时,本文算法的压缩比为71,远高于基于稀疏分解的方法和基于小波变换阈值选择的方法。结论基于小波域主成分分析的心电压缩算法对多导联心电信号具有较好的压缩性能。     

Algorithm for noise-immune ECG signal filtration

Superhard Materials Institute, Ukrainian Academy of Sciences, Kiev. Translated from Meditsinskaya Tekhnika, No. 6, pp. 26–32, November–December, 1989.     

ECG data compression for the emergency setting

A low-cost system for digital transmission of the electrocardiogram (ECG) from a remote location to a medical facility under emergency conditions is developed. Delta threshold, Aztec and a hybrid combination of these two data compression techniques are evaluated to determine their ability to accomplish real time transmission of the ECG over a telephone system. The evaluations are performed using ten electrocardiograms representing arrhythmias commonly encountered in the emergency setting. It is shown that the delta threshold technique may cause data expansion under certain conditions. The hybrid technique is the optimum choice and real-time transmission can be accomplished over a 2400 BAUD system.     

Combined coding and wavelet transform for ECG compression

This paper presents a combined wavelet and a modified runlength encoding scheme for the compression of electrocardiogram (ECG) signals. First, a discrete wavelet transform is applied to the ECG signal. The resulting coefficients are classified into significant and insignificant ones based on the required PRD (percent root mean square difference). Second, both coefficients are encoded using a modified run-length coding method. The scheme has been tested using ECG signals obtained from the MIT-BIH Compression Database. A compression of 20:1 (which is equivalent to 150 bit per second) is achieved with PRD less than 10.     

A novel simulation algorithm for soft tissue compression

This paper presents a novel general approach to simulation of soft tissue compression. A theoretical framework of the compression algorithm has been developed and implemented, based on the concept of a simple spring. The volume subjected to compression is divided into a number of “model elements”, each one consisting of 27 nodes. The nodes are connected with springs. The mechanical properties of the tissues are assumed to be linear and isotropic. The compressed volume remains constant due to the introduced concept of spring variable equilibrium lengths. Initial settings for compression simulation are introduced in order that the algorithm converges faster. The developed compression algorithm was used to model breast compression during a standard mammography examination. Specifically, the method was applied to a high-resolution three-dimensional software breast phantom, composed to have a medium glandularity and calcification abnormalities. The compression was set to 50%. Results showed that the abnormalities maintain their shape and dimensions during the compression, while the surrounding breast tissues undergo considerable deformation and displacement. A “decompression” algorithm was also applied to test the reversibility of the model.     

Optimal wavelets for biomedical signal compression

Signal compression is gaining importance in biomedical engineering due to the potential applications in telemedicine. In this work, we propose a novel scheme of signal compression based on signal-dependent wavelets. To adapt the mother wavelet to the signal for the purpose of compression, it is necessary to define (1) a family of wavelets that depend on a set of parameters and (2) a quality criterion for wavelet selection (i.e., wavelet parameter optimization). We propose the use of an unconstrained parameterization of the wavelet for wavelet optimization. A natural performance criterion for compression is the minimization of the signal distortion rate given the desired compression rate. For coding the wavelet coefficients, we adopted the embedded zerotree wavelet coding algorithm, although any coding scheme may be used with the proposed wavelet optimization. As a representative example of application, the coding/encoding scheme was applied to surface electromyographic signals recorded from ten subjects. The distortion rate strongly depended on the mother wavelet (for example, for 50% compression rate, optimal wavelet, mean±SD, 5.46±1.01%; worst wavelet 12.76±2.73%). Thus, optimization significantly improved performance with respect to previous approaches based on classic wavelets. The algorithm can be applied to any signal type since the optimal wavelet is selected on a signal-by-signal basis. Examples of application to ECG and EEG signals are also reported.     

Combined coding and wavelet transform for ECG compression.

This paper presents a combined wavelet and a modified run-length encoding schemefor the compression of electrocardiogram (ECG) signals. First, a discrete wavelet transform is applied to the ECG signal. The resulting coefficients are classified into significant and insignificant ones based on the required PRD (percent root mean square difference). Second, both coefficients are encoded using a modified run-length coding method. The scheme has been tested using ECG signals obtained from the MIT-BIH Compression Database. A compression of 20:1 (which is equivalent to 150 bit per second) is achieved with PRD less than 10.     

Long-term ECG signal feature extraction

This paper proposes a cepstrum coefficient method applying the dynamic time warping technique to extract the feature vectors from long-term ECG signals. Utilizing this method, one can identify the characteristics hidden in an ECG signal; and then classify the signal as well as diagnose the abnormalities. To evaluate this method, the Normal and PACED BEAT data from the MIT/BIH database are used. The results show that the proposed method successfully extracts the corresponding feature vectors, distinguishes the difference and classifies both signals.