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

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.

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 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.

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.

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

Electroencephalography (EEG) is a non-invasive neuroimaging method that detects the brain's electrical signals using sensors placed on the scalp. It plays a crucial role in diagnosing several neurological conditions such as brain tumors, strokes, epilepsy, and sleep-related conditions. However, during EEG recordings, non-neural data, such as muscle artifacts, are often introduced due to muscle contractions and expansions resulting from actions like jaw clenching, speaking, sniffing, and swallowing. This additional non-neural data can significantly alter EEG signals, making it difficult to analyze brain activity accurately and potentially leading to errors in clinical diagnosis. To address this challenge, a hybrid dual-branch architecture, ResAtten-EEGNet, is integrated with Residual Convolutional Neural Networks (ResCNNs) for extracting multi-scale spatial patterns while avoiding vanishing gradient issues, Positional Encoding to retain the temporal structure of the data, and Multi-Head Self-Attention (MHSA) mechanism to acquire long-range relationships. Training and testing of the model are performed on EEGDenoiseNet dataset and its performance is assessed using Relative Root Mean Square Error (RRMSE), Percentage Reduction in Correlation Coefficient (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\lambda_{\text{CC}}$</tex>), Signal-to-Noise Ratio (SNR), Relative Error (RE), and Percentage Root Mean Square Difference (PRD) metrices. The obtained results are compared with those of existing methodologies, and eexperimental results demonstrate that ResAtten-EEGNet effectively eliminates muscle artifacts (MAs), thereby enhancing the quality of EEG signals.

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.

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.

In this paper, a novel method for removing white gaussian noise from raw ECG signals based on Stationary Wavelet Transform and Interval Thresholding is proposed. Unlike the classical existing denoising methods in the wavelet domains, that apply a simple thresholding to details coefficients, we considers the extremums of each zero-crossings interval in the wavelet domain a whole to perform a thresholding function, which can highlight preserve the most clinical information about the region of the QRS complex in the reconstructed ECG signal. The performance of the proposed SWT-IT (Stationary wavelet transform-Interval Thresholding) method is evaluated in terms of Signal-to-Noise Ratio (SNR), Root Mean Square Error (RMSE), and Percent Root Mean Square Difference (PRD) using real ECG signals acquired from the MIT-BIH Arrhythmia Database. The simulations results show that the proposed method demonstrate superior performance compared with conventional ECG denoising approaches based on the wavelet filtering.

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.

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.

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.

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) 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.

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.