ECG Signal Compression Using Different Techniques

The new method proposed here consists of a combination of two transforms. The hybrid technique consists of Discrete Cosine Transform (DCT) and 1D or 2D discrete wavelet Transform (DWT). The method is suitable for compression of ECG signals. In this research, different records of MIT–BIH data base are used. The performance measure of the technique is done with the help of Compression Ratio (CR) and Percent Root Mean Square Difference (PRD). The threshold based technique is used to achieve better CR. The threshold value is selected based on the R peak. The QRS complex is detected in order to select the R peak. The threshold level is selected as 1, 0.5, and 0.1 % of the R peak. Further improvement in the CR is achieved by the DWT decomposition method. The level of decomposition is carefully selected to achieve improved CR.

This paper introduces a study based on quality controlled discrete cosine transform (DCT) and lifting wavelet transform (LWT) based compression method for blood pressure signal compression in salt sensitive Dahl rat. The transformed coefficients are thresholded using the bisection algorithm to match the predefined user specified percentage root mean square difference (PRD) within the tolerance. Then, the binary lookup table is made to store the position map for zero and non-zero coefficients (NZC). The NZC are quantised by Max-Lloyd quantiser followed by arithmetic coding. Lookup table is encoded by Huffman coding. The results are presented on different blood pressure signals of varying characteristic. There is no significant difference in before quantisation PRD (BPRD) and after quantisation PRD (QPRD) in various signals in both transforms. Mean compression ratio increases with an increase in user define PRD (UPRD).

A new Modified Discrete Wavelets Packets Transform (MDWPT) based method for the compression of Surface EMG signal (s-EMG) data is presented. A Modified Discrete Wavelets Packets Transform (MDWPT) is applied to the digitized s-EMG signal. A Discrete Cosine Transforms (DCT) is applied to the MDWPT coefficients (only on detail coefficients). The MDWPT+ DCT coefficients are quantized with a Uniform Scalar Dead-Zone Quantizer (USDZQ). An arithmetic coder is employed for the entropy coding of symbol streams. The proposed approach was tested on more than 35 actuals S-EMG signals divided into three categories. The proposed approach was evaluated by the following parameters: Compression Factor (CF), Signal to Noise Ratio (SNR), Percent Root mean square Difference (PRD), Mean Frequency Distortion (MFD) and the Mean Square Error (MSE). Simulation results show that the proposed coding algorithm outperforms some recently developed s-EMG compression algorithms.

In recent years, Electrocardiogram (ECG) plays an imperative role in heart disease diagnostics, Human Computer Interface (HCI), stress and emotional states assessment, etc. In general, ECG signals affected by noises such as baseline wandering, power line interference, electromagnetic interference, and high frequency noises during data acquisition. In order to retain the ECG signal morphology, several researches have adopted using different preprocessing methods. In this work, the stroop color word test based mental stress inducement have done and ECG signals are acquired from 10 female subjects in the age range of 20 years to 25 years. We have considered the Discrete Wavelet Transform (DWT) based wavelet denoising have incorporated using different thresholding techniques to remove three major sources of noises from the acquired ECG signals namely, power line interference, baseline wandering, and high frequency noises. Three wavelet functions (db4, coif5 and sym7) and four different thresholding methods are used to denoise the noise in ECG signals. The experimental result shows the significant reduction of above considered noises and it retains the ECG signal morphology effectively. Four different performance measures were considered to select the appropriate wavelet function and thresholding rule for efficient noise removal methods such as, Signal to Interference Ratio (SIR), noise power, Percentage Root Mean Square Difference (PRD) and finally periodogramof Power Spectral Density (PSD). The experimental result shows the coif5 wavelet andrigrsurethresholding rule is optimal for unknown Signal to Noise Ratio (SNR) in the real time ECG signals.

Electrocardiogram (ECG) is the graphical illustration of the heart’s electrical activity and it is acquired through linking electrodes to body surface. It is useful in determining cardiac diseases non-invasively. The general concerns in ECG signal processing is the restrain of noise. This paper has been encouraged by the necessary to recognize the ability of Savitzky-Golay (SG) filter for denoising the ST segment of ECG signals. It essentially implicates the extraction of essential cardiac components by refusing the background noise with the aid of filtering technique. The simulation is carried out using MATLAB and the experiments are performed on physionet database. Peak Signal-to-Noise Ratio (PSNR), Signal-to-Noise Ratio (SNR), Mean Square Error (MSE) and Percentage Root mean square Difference (PRD) are used as performance evaluation metrics. This work uses SG filter to denoise the ECG signal and comparison is provided for SG filter with Median, Butterworth and SOS filters. The comparison results states the performance of the SG filter is better than that of the other filters.

This paper introduces an effective hybrid scheme for the denoising of electrocardiogram (ECG) signals corrupted by non-stationary noises using genetic algorithm (GA) and wavelet transform (WT). We first applied a wavelet denoising in noise reduction of multi-channel high resolution ECG signals. In particular, the influence of the selection of wavelet function and the choice of decomposition level on efficiency of denoising process was considered. Selection of a suitable wavelet denoising parameters is critical for the success of ECG signal filtration in wavelet domain. Therefore, in our noise elimination method the genetic algorithm has been used to select the optimal wavelet denoising parameters which lead to maximize the filtration performance. The efficiency performance of our scheme is evaluated using percentage root mean square difference (PRD) and signal to noise ratio (SNR). The experimental results show that the introduced hybrid scheme using GA has obtain better performance than the other reported wavelet thresholding algorithms as well as the quality of the denoising ECG signal is more suitable for the clinical diagnosis.

Electrocardiogram (ECG) compression can significantly reduce the storage and transmission burden in telemedicine applications. In this paper, an improved ECG data compression method using ASCII character encoding is proposed. A QRS detection algorithm firstly applied to separate QRS and non-QRS region. Then, the QRS regions of ECG signal are compressed using lossless method and non-QRS regions are compressed using lossy method, which improves the compression ratio and minimise the reconstruction error between original and reconstructed ECG signal. All tested ECG records are selected from the PTB database. Statistical results show that the compression performance of the proposed method is better than both lossless and lossy ECG data compression technique. The average compression ratio (CR) and percent root mean square difference (PRD) obtained using proposed ECG data compression method are 27.40 and 4.753 respectively, which is better than other existing ECG data compression method.

An electrocardiogram (ECG) signal is an important diagnostic tool for cardiologists to detect the abnormality. In continuous monitoring, an ambulatory huge amount of ECG data is involved. This leads to high storage requirements and transmission costs. Hence, to reduce the storage and transmission cost, there is a requirement for an efficient compression or coding technique. One of the most promising compression techniques is Compressive Sensing (CS) which makes efficient compression of signals. By this methodology, a signal can easily be reconstructed if it has a sparse representation. This paper presents the Block Sparse Bayesian Learning (BSBL)-based multiscale compressed sensing (MCS) method for the compression of ECG signals. The main focus of the proposed technique is to achieve a reconstructed signal with less error and more energy efficiency. The ECG signal is sparsely represented by wavelet transform. MIT-BIH Arrhythmia database is used for testing purposes. The Huffman technique is used for encoding and decoding. The signal recovery is appropriate up to 75% of compression. The quality of the signal is ascertained using the standard performance measures such as signal-to-noise ratio (SNR) and Percent root mean square difference (PRD). The quality of the reconstructed ECG signal is also validated through the visual method. This method is most suitable for telemedicine applications.

Wavelet threshold based TDL and TDR algorithms for real-time ECG signal compression

Removing noise from ECG signals is still a very frequently discussed topic. There are many challenges that make this problem more complex. These challenges are driven by the widespread use of ECG devices such as monitoring a heart disease of patients and a heart rate of athletes namely when reading and sending ECG signal data is done, there is a lot of noise that contaminates ECG signals, one of which is white Gaussian noise. In this research, noise elimination is done by using Variation mode decomposition (VMD) and Nonlocal Means (NLM) method. Denoising using the NLM technique approach is very effective in removing white Gaussian noise but it suffers from the issue of under-averaging in the high-frequency QRS-complex region and the VMD method which is the development of the Empirical Mode Decomposition (EMD) method has better capability to decompose the signal into several modes. By using the advantages of each method, we are capable of achieving good performance in removing noise from the ECG signals. Testing of the proposed method was performed using MIT-BIH arrhythmias database, which was given Gaussian white noise with different SNR levels. Measurements were made using three levels of measurement matrices i.e. Signal for Ratio Noise Enhancement (SNRimp), Mean Square Error (MSE) and Percentage root mean square difference (PRD). In addition, we may use methods that are compatible with state-of-the-art noise removal methods on ECG signals i.e. the DWT thresholding, EMD soft thresholding, EMD + Wavelet, NLM, M-EMD+NLM, and DWT+NLM. The end result is a method that is capable of producing a slightly higher SNRimp, a lower MSE and PRD value.

This work presents a proficient approach of designing an M-band Near Perfect Reconstruction (NPR) Cosine Modulated Filter Bank (CMFB) where the NPR condition is built upon to satisfy the power complementary property and the pass-edge frequency is repetitively varied to adjust the filter-coefficients to reduce the reconstruction error occurred. Using this optimal value of the pass-edge frequency, the prototype filter is designed and the filter bank is considered from that single prototype filter. Thus the primary goal of designing the CMFB comes down to the issue of implementing efficient structure of this prototype filter. Now the prototype filter is designed with Interpolated Finite Impulse Response (IFIR) technique where numbers of hardware multipliers are reduced by cascading model and image suppression filter compared to that of single prototype filter and furthermore the designed filter coefficients are replaced with Canonical Signed Digit (CSD) to get lesser hardware complexity. To make the system practically realizable, an Electrocardiogram (ECG) signal is processed through this system and reconstructed back again. Some quality measurement parameters such as Signal-to-Noise Ratio (SNR), Mean Square Error (MSE) and Percentage Root Mean Square Difference (PRD) are analyzed to see the effectiveness of the CMFB.

Wavelet energy based diagnostic distortion measure for ECG

Baseline wander and power line interference removal from ECG signals using eigenvalue decomposition

In this article, we make a comparative study for a new approach compression between discrete cosine transform (DCT) and discrete wavelet transform (DWT). We seek the transform proper to vector quantization to compress the EMG signals. To do this, we initially associated vector quantization and DCT, then vector quantization and DWT. The coding phase is made by the SPIHT coding (set partitioning in hierarchical trees coding) associated with the arithmetic coding. The method is demonstrated and evaluated on actual EMG data. Objective performance evaluations metrics are presented: compression factor, percentage root mean square difference and signal to noise ratio. The results show that method based on the DWT is more efficient than the method based on the DCT.

In this paper are presented compression results of ECG signal by using three time-frequency transformations: Discrete Wavelet Transform, Wavelet Packets and Modified Cosine Transform. By using transforms mentioned, samples of signals are transformed to appropriate groups of transformation coefficients. Almost all coefficients below the determined threshold are rounded to zero values and by inverse transform the similar signal to original one is created. By using run-length coder, consecutive zero value coefficients can be replaced by single value that shows how many consecutive coefficients with zero value exists. In this way small number of coefficients is stored, and compression is obtained. Depending on transform used, different number of coefficients is rounded to zero in different positions, hence the reconstructed signal is more or less similar to the original one. In general there exists measures that show how much reconstructed signal is similar to the original one, and the most used is Percentage Root mean square Difference (PRD). Comparison of compression is performed in obtaining the larger compression ratio for the smaller PRD.