Omkar Singh

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.

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.

Empirical wavelet transform-based delineator for arterial blood pressure waveforms

Abstract Arterial blood pressure (ABP) waveforms provide plenty of pathophysiological information about the cardiovascular system. ABP pulse analysis is a routine process used to investigate the health status of the cardiovascular system. ABP pulses correspond to the contraction and relaxation phenomena of the human heart. The contracting or pumping phase of the cardiac chamber corresponds to systolic pressure, whereas the resting or filling phase of the cardiac chamber corresponds to diastolic pressure. An ABP waveform commonly comprises systolic peak, diastolic onset, dicrotic notch, and dicrotic peak. Automatic ABP delineation is extremely important for various biomedical applications. In this paper, a delineator for onset and systolic peak detection in ABP signals is presented. The algorithm uses a recently developed empirical wavelet transform (EWT) for the delineation of arterial blood pulses. EWT is a new mathematical tool used to decompose a given signal into different modes and is based on the design of an adaptive wavelet filter bank. The performance of the proposed delineator is evaluated and validated over ABP waveforms of standard databases, such as the MIT-BIH Polysomnoghaphic Database, Fantasia Database, and Multiparameter Intelligent Monitoring in Intensive Care Database. In terms of pulse onset detection, the proposed delineator achieved an average error rate of 0.11%, sensitivity of 99.95%, and positive predictivity of 99.92%. In a similar manner for systolic peak detection, the proposed delineator achieved an average error rate of 0.10%, sensitivity of 99.96%, and positive predictivity of 99.92%.

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

This paper presents a novel technique to identify heartbeats in multimodal data using electrocardiogram (ECG) and arterial blood pressure (ABP) signals. Multiple physiological signals such as ECG, ABP, and Respiration are often recorded in parallel from the activity of heart. These signals generally possess related information as they are generated by the same physical system. The ECG and ABP correspond to the same phenomenon of contraction and relaxation activity of heart. Multiple signals acquired from various sensors are generally processed independently, thus discarding the information from other measurements. In the estimation of heart rate and heart rate variability, the R peaks are generally identified from ECG signal. Efficient detection of R-peaks in electrocardiogram (ECG) is a key component in the estimation of clinically relevant parameters from ECG. However, when the signal is severely affected by undesired artifacts, this becomes a challenging task. Sometimes in clinical environment, other physiological signals reflecting the cardiac activity such as ABP signal are also acquired simultaneously. Under the availability of such multimodal signals, the accuracy of R peak detection methods can be improved using sensor-fusion techniques. In the proposed method, the sample entropy (SampEn) is used as a metric for assessing the noise content in the physiological signal and the R peaks in ECG and the systolic peaks in ABP signals are fused together to enhance the efficiency of heartbeat detection. The proposed method was evaluated on the 100 records from the computing in cardiology challenge 2014 training data set. The performance parameters are: sensitivity (Se) and positive predictivity (PPV). The unimodal R peaks detector achieved: Segross = 99.40%, PPVgross = 99.29%, Seaverage = 99.37%, PPVaverage = 99.29%. Similarly unimodal BP delineator achieved Segross = 99.93%, PPVgross = 99.99%, Seaverage = 99.93%, PPVaverage = 99.99% whereas, the proposed multimodal beat detector achieved: Segross = 99.65%, PPVgross = 99.91%, Seaverage = 99.68%, PPVaverage = 99.91%.

Beat detection algorithm for ECG and arterial blood pressure waveforms using empirical mode decomposition: a unified approach

Beat detection algorithms for electrocardiogram (ECG) and pressure signals are extremely important for clinical applications. The accurate detection of R–peak locations in ECG signal is extremely important for its further processing with regard to cardiac health monitoring and for Heart Rate Variability (HRV) studies. Beat detection algorithms for pressure signals have many clinical applications including pulse oximetry, cardiac arrhythmia detection, and cardiac output monitoring. This paper presents a reliable unified algorithm for R–peak detection in ECG and systolic peak detection in Arterial Blood Pressure (ABP) signal using empirical mode decomposition. The proposed beat detection algorithm was tested on different data records of MIT–BIH arrhythmia database, Fantasia database, self–recorded signals and MIMIC database. The proposed algorithm achieved an overall sensitivity Se = 99.98% and a positive predictivity +P = 99.89% for ECG signals and an overall sensitivity Se = 99.58% and a positive predictivity +P = 99.85% for ABP signals.

A new approach for identification of heartbeats in multimodal physiological signals

Abstract In this paper, a technique is proposed for detection of heartbeats in multimodal data. Recording of multiple physiological signals from the same subject is common practice nowadays. Multiple physiological signals are generally available but are processed separately without taking into consideration information from other relevant signals. The heartbeats are generally detected from R peaks in electrocardiogram (ECG) signal, however, if ECG is noisy, other signals reflecting the cardiac activity may be used for identifying heartbeats. This paper describes a new method for detection of heartbeats using ECG and arterial blood pressure (ABP) signals. The physiological data are segmented into various fragments and signal quality is determined using the judgment of noise level. If the ECG data fragment is noisy, heartbeats are computed from the ABP fragment. The evaluation was performed on training data set of computing in cardiology challenge 2014. The proposed methodology has resulted in better detection accuracy as compared to the unimodal methods.

Detection of Onset, Systolic Peak and Dicrotic Notch in Arterial Blood Pressure Pulses

In this paper, we proposed an effective method for detecting fiducial points in arterial blood pressure pulses. An arterial blood pressure pulse normally consists of onset, systolic peak and dicrotic notch. Detection of fiducial points in blood pressure pulses is a critical task and has many potential applications. The proposed method employs empirical wavelet transform for locating the systolic peak and onset of blood pressure pulse. The proposed method first estimates the fundamental frequency of blood pressure pulse using empirical wavelet transform and utilizes the combination of the blood pressure pulse and the estimated frequency for locating onset and systolic peak. For dicrotic notch detection, it utilizes the first-order difference of blood pressure pulse. The algorithm was validated on various open-source databases and was tested on a data set containing 12,230 beats. Two benchmark parameters such as sensitivity and positive predictivity were used for the performance evaluation. The comparison results for accuracy of the detection of systolic peak, onset and dicrotic notch are reported. The proposed method attained a sensitivity and positive predictivity of 99.95% and 99.97%, respectively, for systolic peaks. For onsets, it attained a sensitivity and predictivity of 99.88% and 99.92%, respectively. For dicrotic notches, a sensitivity and positive predictivity of 98.98% and 98.81% were achieved, respectively.