scholarly journals Discrete Cosine Transform based ECG Signal Analysis and Processing

Webology ◽  
2021 ◽  
Vol 18 (2) ◽  
pp. 519-539
Author(s):  
Aqeel Mohsin Hamad
Keyword(s):  
Discrete Cosine Transform ◽  
Threshold Value ◽  
Ecg Signal ◽  
Qrs Complex ◽  
Threshold Method ◽  
Basic Part ◽  
Cosine Transform ◽  
Specificity And Sensitivity ◽  
Reconstructed Signal ◽  
Frequency Components

Cardiovascular disease (CADs) is considered the primary leading cause of death. Irregular activity of heart, these disease can be detected and classified by Electrocardiogram (ECG), which is constructed from using electrodes placed on human skin to record the electrical activity of the heart. Because QRS complex represents the basic part of the ECG signal, these components should be recognized in order to analysis the other characteristics of the signal. Different methods and algorithms are proposed to analysis and processing the ECG signal. In this paper, a new QRS complex recognition method are proposed based on discrete cosine transform (DCT) with variable adaptive threshold method, which is used to determine threshold based on characteristic of each ECG signal to detect upper and lower levels of threshold to detect the peak of the signal. At first, the DCT is applied to the ECG signal to isolate it into different coefficients and eliminate or reduce the noises of the signal based on processing of high frequency components of DCT coefficients, which have less information, then the ECG is reconstructed by cropping the most important coefficients to be used in threshold determination. The basic idea is that the reconstructed signal have high differences between the components of the signal, and this facilitates the process of calculating the threshold value, which is used later to find peaks of ECG signal. The proposed method is tested and its performance are determined based on three different datasets, which are MITBIH Arrhythmia dataset, (LTSTDB) and (EDB) and the performance are evaluated using different metrics, which are Detection rate, accuracy, specificity and sensitivity. The experimental results show that the proposed method is performed or outperformed other works, therefore it can be used in peak detection applications.

QRS COMPLEX DETECTION USING OPTIMAL DISCRETE WAVELET

2009 ◽  
Vol 08 (02) ◽  
pp. 97-109
Author(s):  
R. SHANTHA SELVA KUMARI ◽  
S. BHARATHI ◽  
V. SADASIVAM
Keyword(s):  
Maximum Error ◽  
Perfect Reconstruction ◽  
Discrete Wavelet ◽  
Ecg Signal ◽  
Qrs Complex ◽  
Time Frequency ◽  
Qrs Complex Detection ◽  
Reconstructed Signal ◽  
Complex Detection ◽  
Electrocardiogram Ecg

Wavelet transform has emerged as a powerful tool for time frequency analysis of complex nonstationary signals such as the electrocardiogram (ECG) signal. In this paper, the design of good wavelets for cardiac signal is discussed from the perspective of orthogonal filter banks. Optimum wavelet for ECG signal is designed and evaluated based on perfect reconstruction conditions and QRS complex detection. The performance is evaluated by using the ECG records from the MIT-BIH arrhythmia database. In the first step, the filter coefficients (optimum wavelet) is designed by reparametrization of filter coefficients. In the second step, ECG signal is decomposed to three levels using the optimum wavelet and reconstructed. From the reconstructed signal, the range of error signal is calculated and it is compared with the performance of other suitable wavelets already available in the literature. The optimum wavelet gives the maximum error range as 10-14–10-11 which is better than that of other wavelets existing in the literature. In the third step, the baseline wandering is removed from the ECG signal for better detection of QRS complex. The optimum wavelet detects all R peaks of all records. That is using optimum wavelet 100% sensitivity and positive predictions are achieved. Based on the performance, it is confirmed that optimum wavelet is more suitable for ECG signal.

scholarly journals DCT image codec using variance of sub-regions

2015 ◽  
Vol 5 (1) ◽  
pp. 13-21
Author(s):  
Pooneh Bagheri Zadeh ◽  
Akbar Sheikh Akbari ◽  
Tom Buggy
Keyword(s):  
Spatial Frequency ◽  
Discrete Cosine Transform ◽  
Image Coding ◽  
High Frequency ◽  
Image Transmission ◽  
Threshold Value ◽  
Input Image ◽  
Cosine Transform ◽  
Coding Scheme ◽  
Frequency Index

AbstractThis paper presents a novel variance of subregions and discrete cosine transform based image-coding scheme. The proposed encoder divides the input image into a number of non-overlapping blocks. The coefficients in each block are then transformed into their spatial frequencies using a discrete cosine transform. Coefficients with the same spatial frequency index at different blocks are put together generating a number of matrices, where each matrix contains coefficients of a particular spatial frequency index. The matrix containing DC coefficients is losslessly coded to preserve its visually important information. Matrices containing high frequency coefficients are coded using a variance of sub-regions based encoding algorithm proposed in this paper. Perceptual weights are used to regulate the threshold value required in the coding process of the high frequency matrices. An extension of the system to the progressive image transmission is also developed. The proposed coding scheme, JPEG and JPEG2000were applied to a number of test images. Results show that the proposed coding scheme outperforms JPEG and JPEG2000 subjectively and objectively at low compression ratios. Results also indicate that the proposed codec decoded images exhibit superior subjective quality at high compression ratios compared to that of JPEG, while offering satisfactory results to that of JPEG2000.

scholarly journals ECG signal de-noising based on deep learning auto encoder and discrete wavelet transform

2020 ◽  
Vol 9 (2) ◽  
pp. 415
Author(s):  
Aqeel M.Hamad alhussainy ◽  
Ammar D. Jasim
Keyword(s):  
Deep Learning ◽  
Wavelet Transform ◽  
Threshold Value ◽  
Discrete Wavelet ◽  
Ecg Signal ◽  
Threshold Method ◽  
Wavelet Filters ◽  
Threshold Functions ◽  
Two Stages ◽  
Signal Approximation

ECG is very important tool for diagnosis of heart disease, this signal is suffered from different types of noises such as baseline wander (BW), muscle artifact (MA) and electrode motion (EM) , which lead to wrong interpretation. In order to prevent or reduce the effect of these noises, different approaches have been applied to enhance the ECG signal. In this paper, we have proposed a new method for ECG signal de-noising based on deep learning Auto encoder (DL-DAE) and wavelet transform named as (WT-DAE). The proposed system (WT-DAE) is constructed from two stages, in the first stage, the wavelet transform is used to isolate the most significant coefficient of the signal (approximation sub-band) from de-tails coefficients (details sub-band). The details coefficients is fed to new proposed threshold method , which is used to evaluate the threshold value according to the feature of ECG signal, this threshold value is used to threshold the detail coefficients, in order to remove the details noise that is contained as high frequencly component , then invers wavelet transform is used to reconstruct the signal . Different wavelet filters and threshold functions are applied in this stage. The second stage of signal de-noising is performed by using DAE method, which is designed for reconstruct the de-noised sig-nal. The proposed DAE model is constructed from 14 layers of convolutional, relu and max_ pooling layer with different parameters. We perform training and testing the model with MIT-BIH ECG database and the performance of the pro-posed system is evaluated by terms of MSE, RMSE, PRD and PSNR. The experimental results are compared with other approaches and show that, the proposed system demonstrated the superiority for de-noising ECG signal. 

Dual-tree complex wavelet transform technique-based optimal threshold tuning system to deliver denoised ECG signal

2020 ◽  
Vol 42 (4) ◽  
pp. 854-869
Author(s):  
Navdeep Prashar ◽  
Meenakshi Sood ◽  
Shruti Jain
Keyword(s):  
Distribution Function ◽  
Noise Reduction ◽  
Performance Metrics ◽  
Research Work ◽  
Threshold Value ◽  
Distribution Functions ◽  
Threshold Function ◽  
Ecg Signal ◽  
Reconstructed Signal ◽  
Reduction Approach

The primary output for any health monitoring system that offers telecardiology services is the recovery of the electrocardiographic (ECG) signal from the noised signal. The mechanized investigation of the ECG signal is the most inspiring challenge for accurate detection of cardiac disease. This could be accomplished by eliminating the various noises from the acquired signal. In this paper, a noise reduction approach employing DTCWT is executed on an ECG signal by proposing a noise estimator along with a detailed assessment of the effect of the choice of the threshold value, threshold algorithm and distribution function. The thresholding technique is executed by varying the threshold value ( γ) and its function ( fn) applied to the proposed estimator (α*). The proposed estimator is scaled by 2n factor to study its impact on performance metrics and the nature of the reconstructed signal utilizing different distribution functions. The best combination of threshold function with threshold value selection has been chosen in this work from eight different sets of threshold value selection rules along with six distinct threshold functions. The experimental results show that the proposed noise reduction approach using a universal modified threshold level-dependent threshold with non-negative garrote threshold function for normal distribution with n = 3 delivers 80.72dB SNR with a subsequent reduction in MSE and PRD as compared with other standard techniques. An elaborate empirical analysis for selecting the distribution function for obtaining the best possible threshold function and technique is the prime objective and novelty of this research work.

scholarly journals A new method of electrodes placement to improve QRS detection in real-time stress ECG acquisition

2015 ◽  
Vol 18 (2) ◽  
pp. 159-163
Author(s):  
Dang Cao Le ◽  
Tan Hoang Nguyen ◽  
Nam Hoai Phan ◽  
Quoc Minh Thai
Keyword(s):  
Real Time ◽  
New Method ◽  
Dynamic Threshold ◽  
Ecg Signal ◽  
Qrs Complex ◽  
Threshold Method ◽  
Baseline Drift ◽  
Different Types ◽  
Detecting Method ◽  
Real Time Application

In dynamic threshold method to detect QRS complex from ECG signal, especially in real-time application, there are two main issues: baseline drift and noise. This paper introduces an improved QRS complex detecting method using dynamic threshold algorithm combined with a new method of electrodes placement to minimize baseline drift and different types of noise in real-time ECG acquisition with moving patients. Our method proved to be more effective in detecting QRS complex with less error due to minimized baseline drift and noise in original ECG signal.

LWT-DCT based Image Watermarking Scheme using Normalized SVD

2020 ◽  
Vol 13 ◽  
Author(s):  
Rahul Dixit ◽  
Amita Nandal ◽  
Arvind Dhaka ◽  
Vardan Agarwal ◽  
Yohan Varghese
Keyword(s):  
Wavelet Transform ◽  
Singular Value Decomposition ◽  
Discrete Cosine Transform ◽  
Image Watermarking ◽  
Singular Value ◽  
Cover Image ◽  
Lifting Wavelet Transform ◽  
Cosine Transform ◽  
Lifting Wavelet ◽  
Value Decomposition

Background: Nowadays information security is one of the biggest issues of social networks. The multimedia data can be tampered with, and the attackers can then claim its ownership. Image watermarking is a technique that is used for copyright protection and authentication of multimedia. Objective: We aim to create a new and more robust image watermarking technique to prevent illegal copying, editing and distribution of media. Method : The watermarking technique proposed in this paper is non-blind and employs Lifting Wavelet Transform on the cover image to decompose the image into four coefficient matrices. Then Discrete Cosine Transform is applied which separates a selected coefficient matrix into different frequencies and later Singular Value Decomposition is applied. Singular Value Decomposition is also applied to the watermarking image and it is added to the singular matrix of the cover image which is then normalized followed by the inverse Singular Value Decomposition, inverse Discrete Cosine Transform and inverse Lifting Wavelet Transform respectively to obtain an embedded image. Normalization is proposed as an alternative to the traditional scaling factor. Results: Our technique is tested against attacks like rotation, resizing, cropping, noise addition and filtering. The performance comparison is evaluated based on Peak Signal to Noise Ratio, Structural Similarity Index Measure, and Normalized Cross-Correlation. Conclusion: The experimental results prove that the proposed method performs better than other state-of-the-art techniques and can be used to protect multimedia ownership.

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