Compression of ECG Signal Using Hybrid Technique

In this paper, a transform based methodology is presented for compression of electrocardiogram (ECG) signal. The methodology employs different transforms such as Discrete Wavelet Transform (DWT), Fast Fourier Transform (FFT) and Discrete Cosine Transform (DCT). A comparative study of performance of different transforms for ECG signal is made in terms of Compression ratio (CR), Percent root mean square difference (PRD), Mean square error (MSE), Maximum error (ME) and Signal-to-noise ratio (SNR). The simulation results included illustrate the effectiveness of these transforms in biomedical signal processing. When compared, Discrete Cosine Transform and Fast Fourier Transform give better compression ratio, while Discrete Wavelet Transform yields good fidelity parameters with comparable compression ratio.

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).

In this paper, a new approach to remove noise present in ECG signal is proposed. Baseline wander and high frequency noise is eliminated by using computationally efficient linear phase filter ie. interpolated finite impulse response (IFIR) filter. The IFIR filter is designed by using Kaiser window function to achieve high stop band attenuation. As compared to other methods, the technique presented could achieve a reduction in computational complexity by 80.14 percent. Data compression is also performed in this study using wavelet packet decomposition along with Run-length encoding. Run-length encoding is used to improve the compression performance. For evaluation of the performance of IFIR filter, computational cost reduction (CRC) parameter is used, which directly depended on multipliers and adders. Different fidelity factors are considered to evaluate the performance of the proposed data compression method, viz., compression ratio (CR), signal to noise ratio (SNR), retained energy (RE) and percent root mean square difference (PRD), their magnitude being 25.13, 38.93, 99.10 and 1.75, respectively. MIT-BIH arrhythmia database has been utilized to judge the entire set of computations mentioned above noise removal and ECG signal compression. This work also includes beat detection of original and reconstructed signals. Simulated results show that decompressed signal is a replica of the input signal.

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

Remote Healthcare Monitoring Systems (RHMs) that use ECG signals are very effective tools for the early diagnosis of various heart conditions. However, these systems are still confronted with a problem that reduces their efficiency, such as energy consumption in wearable devices because they are battery-powered and have limited storage. This paper presents a novel algorithm for the compression of ECG signals to reduce energy consumption in RHMs. The proposed algorithm uses discrete Krawtchouk moments as a feature extractor to obtain features from the ECG signal. Then the accelerated Ant Lion Optimizer (AALO) selects the optimum features that achieve the best-reconstructed signal. Our proposed algorithm is extensively validated using two benchmark datasets: MIT-BIH arrhythmia and ECG-ID. The proposed algorithm provides the average values of compression ratio (CR), percent root mean square difference (PRD), signal to noise ratio (SNR), Peak Signal to noise ratio (PSNR), and quality score (QS) are 15.56, 0.69, 44.52, 49.04 and 23.92, respectively. The comparison demonstrates the advantages of the proposed compression algorithm on recent algorithms concerning the mentioned performance metrics. It also tested and compared against other existing algorithms concerning Processing Time, compression speed and computational efficiency. The obtained results show that the proposed algorithm extremely outperforms in terms of (Processing Time = 6.89 s), (compression speed = 4640.19 bps) and (computational efficiency = 2.95). The results also indicate that the proposed compression algorithm reduces energy consumption in a wearable device by decreasing the wake-up time by 3600 ms.

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.

Beta wavelet based ECG signal compression using lossless encoding with modified thresholding

Wavelet energy based diagnostic distortion measure for ECG

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.

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.

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.

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.

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 ECG compression using USZZQ and Huffman coding of DSM

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.