Electrocardiographic (ECG) signal is significant to diagnose cardiac arrhythmia among various biological signals. The accurate analysis of noisy Electrocardiographic (ECG) signal is very motivating challenge. Prior to automated analysis, the noises present in ECG signal need to be eliminated for accurate diagnosis. Many researchers have been reported different methods for denoising the ECG signal in recent years. In this paper, an optimized threshold mechanism is proposed for wavelet based medical signal noise reduction. This scheme is based on a variable step size firefly algorithm (VSSFA) in dual tree complex wavelet scheme, in which the VSSFA is utilized for threshold optimization. This approach is evaluated on several normal and abnormal ECG signals of MIT/BIH arrhythmia database, by artificially adding white Gaussian noises with variation of 5dB and 10dB. Simulation result illustrate that the proposed system is well performance in various noise level, and obtains better visual quality compare with other methods.

http://www.inass.org/share/2016093012.pdf

An Efficient Threshold Prediction Scheme for Wavelet Based ECG Signal Noise Reduction Using Variable Step Size Firefly Algorithm

ECG signal enhancement based on improved denoising auto-encoder

Optimization of signal quality over comfortability of textile electrodes for ECG monitoring in fog computing based medical applications

Parallel-type fractional zero-phase filtering for ECG signal denoising

http://ieeexplore.ieee.org/iel5/5/31345/01457393.pdf

An introduction to the analysis and processing of signals

In this study, we focused on the reduction of broadband myopotentials (EMG) in ECG signals using the wavelet Wiener filtering with noise-free signal estimation. We used the dyadic stationary wavelet transform (SWT) in the Wiener filter as well as in estimating the noise-free signal. Our goal was to find a suitable filter bank and to choose other parameters of the Wiener filter with respect to the signal-to-noise ratio (SNR) obtained. Testing was performed on artificially noised signals from the standard CSE database sampled at 500 Hz. When creating an artificial interference, we started from the generated white Gaussian noise, whose power spectrum was modified according to a model of the power spectrum of an EMG signal. To improve the filtering performance, we used adaptive setting parameters of filtering according to the level of interference in the input signal. We were able to increase the average SNR of the whole test database by about 10.6 dB. The proposed algorithm provides better results than the classic wavelet Wiener filter.

Adaptive Wavelet Wiener Filtering of ECG Signals

Voltage-sensitive dyes (VSDs) are used in touch-less recording of electrical activity from animal hearts. Previously, VSDs has been successfully used in myocardial ischemia studies. However, our previous Langendorff perfused heart experiments have suggested changes in ECG signals in time and time-frequency domain caused by application of VSD during optical mapping. Detailed analysis of changes is needed to reveal impact to particular fractions of a heart cycle. Dynamic time warping (DTW) is applied to P-Q segments, QRS complexes, and ST-T segments of selected consecutive heart cycles in each experiment period. An optimal path resulting from DTW based on minimum distance between the control segments (before the application of VSD) and segments recorded in next two experiment steps is computed

/pdf/analysis-of-dynamic-changes-in-ecg-signals-during-optical-2cs8o2xog6.pdf

Analysis of dynamic changes in ECG signals during optical mapping by dynamic time warping

Background: Automatic detection and classification of cardiac abnormalities in ECG is one of the basic and often solved problems. The aim of this paper is to present a proposed algorithm for ECG classification into 19 classes. This algorithm was created within PhysioNet/CinC Challenge 2020, name of our team was HITTING. Methods: Our algorithm detects each pathology separately according to the extracted features and created rules. Signals from the 6 databases were used. Detector of QRS complexes, T-waves and P-waves including detection of their boundaries was designed. Then, the most common morphology of the QRS was found in each record. All these QRS were averaged. Features were extracted from the averaged QRS and from intervals between detected points. Appropriate features and rules were set using classification trees. Results: Our approach achieved a challenge validation score of 0.435, and full test score of 0.354, placing us 11 out of 41 in the official ranking. Conclusion: The advantage of our algorithm is easy interpretation. It is obvious according to which features algorithm decided and what thresholds were set.

/pdf/cardiac-pathologies-detection-and-classification-in-12-lead-2gj6dlxlrz.pdf

Cardiac Pathologies Detection and Classification in 12-lead ECG

This contribution present a new approach for ultrasound image segmentation, based on the fuzzy theory. The segmentation is performed on the 2D envelope of radiofrequency (RF) signals instead of the B-mode image. The segmented image is then used to create the sector image.

Fuzzy Approach in Ultrasound Image Segmentation

The paper deals with simple approach to digital time-varying ECG filtering. The theoretical presuppositions of the filter performance are described as well as the method of the filter design.

http://ieeexplore.ieee.org/iel5/1028/5761012/05761080.pdf

Jiri Kozumplik

Papers

Adaptive Wavelet Wiener Filtering of ECG Signals

Analysis of dynamic changes in ECG signals during optical mapping by dynamic time warping

Cardiac Pathologies Detection and Classification in 12-lead ECG

Fuzzy Approach in Ultrasound Image Segmentation

Time — Varying filters for ECG processing