Ramesh Kumar Sunkaria

ECG signal denoising based on Empirical Mode Decomposition and moving average filter

Electrocardiogram (ECG) signal shows the electrical activity of the heart and provides useful information that helps in analyzing the patients heart condition. But different noises get contaminated with ECG signal during its acquisition and transmission, which can cause a great deal of hindrance to manual and automatic analysis of ECG signals and they may be interpreted as the abnormal heart conditions. Hence for the proper diagnosis of the heart the ECG signals must be free of noises. In this work denoising of the ECG signal is the major objective and technique used for this purpose is based on the Empirical Mode Decomposition (EMD) followed by moving average filter. The proposed method is an enhancement towards the existing EMD based denoising algorithms. EMD is an adaptive and data driven technique, thus suitable for any nonstationary signal. For denoising, the ECG signal is initially decomposed into a set of Intrinsic Mode Functions (IMFs), then high frequency noises are eliminated using lower order IMFs followed by the reconstruction of the ECG signal and it is found to be free of noises with a high degree of Signal to Error Ratio (SER). In this work white Gaussian noise is considered and results obtained by simulations show both qualitatively as well as quantitatively that the approach used here is really a very effective and promising one for denoising the ECG signals without losing its actual characteristics.

Powerline interference reduction in ECG signals using empirical wavelet transform and adaptive filtering

Abstract Separating an information-bearing signal from the background noise is a general problem in signal processing. In a clinical environment during acquisition of an electrocardiogram (ECG) signal, The ECG signal is corrupted by various noise sources such as powerline interference (PLI), baseline wander and muscle artifacts. This paper presents novel methods for reduction of powerline interference in ECG signals using empirical wavelet transform (EWT) and adaptive filtering. The proposed methods are compared with the empirical mode decomposition (EMD) based PLI cancellation methods. A total of six methods for PLI reduction based on EMD and EWT are analysed and their results are presented in this paper. The EWT-based de-noising methods have less computational complexity and are more efficient as compared with the EMD-based de-noising methods.

A comparative study on spectral parameters of HRV in yogic and non-yogic practitioners

The autonomic nervous system regulates the heart rate through its sympathetic and para-sympathetic nervous system to maintain body visceral homeostasis. The sympathetic tone enhances the heart rate whereas the para-sympathetic tone inhibits this rise. The continuous variation of heart rate in synchronism with visceral systems is termed as heart rate variability. This heart rate variability is higher in normal and healthy conditions, whereas it is reduced in case of cardiac abnormalities. The present study is regarding heart rate variability in non-yogic practitioners and yogic practitioners. The spectral parameters were evaluated in two groups, where one group is having forty two normal and healthy male subjects who are non-yogic practitioners, and the other group is also having forty two normal and healthy male subjects who are experienced yoga practitioners. The subjects in both groups are in the age group of 18-48 years. The power in low frequency (LF) has been observed to be higher in non-yogic practitioners as compared to those of yogic practitioners. Moreover, the heart rate variability in yogic practitioners has shown to be higher than the subjects who do not practise yoga.

ECG signal denoising via empirical wavelet transform

This paper presents new methods for baseline wander correction and powerline interference reduction in electrocardiogram (ECG) signals using empirical wavelet transform (EWT). During data acquisition of ECG signal, various noise sources such as powerline interference, baseline wander and muscle artifacts contaminate the information bearing ECG signal. For better analysis and interpretation, the ECG signal must be free of noise. In the present work, a new approach is used to filter baseline wander and power line interference from the ECG signal. The technique utilized is the empirical wavelet transform, which is a new method used to compute the building modes of a given signal. Its performance as a filter is compared to the standard linear filters and empirical mode decomposition.The results show that EWT delivers a better performance.

Modified approach for ECG signal denoising based on empirical mode decomposition and moving average filter

Electrocardiogram (ECG) signal shows the electrical activity of the heart and provides useful information that helps in analyzing the patients heart condition. But different noises get contaminated with ECG signal during its acquisition and transmission, which can cause a great deal of hindrance to manual and automatic analysis of ECG signals and they may be interpreted as the abnormal heart conditions. Hence for the proper diagnosis of the heart the ECG signals must be free of noises. In this work denoising of the ECG signal is the major objective and technique used for this purpose is based on the Empirical Mode Decomposition (EMD) followed by moving average filter. The proposed method is an enhancement towards the existing EMD based denoising algorithms. EMD is an adaptive and data driven technique, thus suitable for any nonstationary signal. For denoising, the ECG signal is initially decomposed into a set of Intrinsic Mode Functions (IMFs), then high frequency noises are eliminated using lower order IMFs followed by the reconstruction of the ECG signal and it is found to be free of noises with a high degree of Signal to Error Ratio (SER). In this work white Gaussian noise is considered and results obtained by simulations show both qualitatively as well as quantitatively that the approach used here is really a very effective and promising one for denoising the ECG signals without losing its actual characteristics.

Inferior myocardial infarction detection using stationary wavelet transform and machine learning approach

Early and accurate detection of myocardial infarction is imperative for reducing the mortality rate due to heart attack. Present work proposes a novel technique aiming toward accurate and timely detection of inferior myocardial infarction (IMI). Stationary wavelet transform has been used to decompose the segmented multilead electrocardiogram (ECG) signal into different sub-bands. Sample entropy, normalized sub-band energy, log energy entropy, and median slope calculated over selected bands of multilead ECG are used as features. Support vector machine (SVM) and K-nearest neighbor (KNN) have been used to classify between subjects admitted for health control (HC) and patients suffering from IMI, using attributes selected on the basis of gain ratio. The full length ECG of lead II, III, and aVF of all the subjects having IMI or admitted for HC from Physikalisch-Technische Bundesanstalt Database (PTB-DB) has been used in the present work. The proposed technique has been scrutinized under both “class-oriented,” and more practical, “subject-oriented” approach. Under the class-oriented approach, data have been divided into training and test data irrespective of the patients, whereas in subject-oriented approach, data from one patient have been used for test and training has been done on the rest of the subjects. Under the class-oriented approach, area under the receiver operating characteristic curve (Roc), sensitivity (Se%), specificity (Sp%), positive predictivity (+P%), and accuracy (Ac%) is Roc

Two-channel perfect reconstruction (PR) quadrature mirror filter (QMF) bank design using logarithmic window function and spline function

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Heartbeat detection in multimodal physiological signals using signal quality assessment based on sample entropy

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