ECG denoising using weiner filter and adaptive least mean square algorithm

Sharma, Bharati; Suji, R. Jenkin

doi:10.1109/rteict.2016.7807781

Cited by 16 publications

(6 citation statements)

References 7 publications

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“…(13) and Eq. 14respectively as follows: [26] 48.389 0.00006993 Butterworth low pass filter [25] 38.4849 0.00060880 Adaptive filtering [5] 23.4495 0.00250200 Figure 8 shows Dependence of improvement SNR on input SNR, the average SNR improvement corresponding AWWF using LSMU threshold decreases from 24 to 6 dB with increasing input signal SNR while for proposed combination AWW+ NLMS filtering it decreases from 28 to 11 dB with increasing input signal SNR, where it is the better.…”

Section: Subsection 2 Resultsmentioning

confidence: 99%

“…EMG noise is the most difficult type of broadband myopotentials noise to remove because it causes widening of the QRS complex, cropping of peaks in QRS complexes and completely mask PQ and ST intervals, the P-and T-waves [1,2]. There are many methods that have been implemented to eliminate the noise from noisy signal such as nonlinear filter banks [3], adaptive filtering [4,5], principal component analysis (PCA) and independent component analysis (ICA) [6], Genetic Particle Filtering [7], wavelet transform [8,9], Empirical mode decomposition (EMD) and non-local mean (NLM) technique [10]. The filters possess disadvantages that they remove important frequency components in the vicinity of cut-off frequency, adaptive filters have the ability to adjust their parameters automatically and don't need a prior knowledge of signal or noise description.…”

Section: Introductionmentioning

confidence: 99%

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Denoising Electrocardiogram Signal from Electromyogram Noise Using Adaptive Filter Combination

Khiter¹,

Adamou‐Mitiche²,

Mitiche³

2020

RIA

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The electrocardiogram (ECG) signal is the most important diagnostic test of heart disease detection; it is characterized by low frequency and weak amplitude which makes it susceptible to different kinds of noises such as high/ low-frequency noises. Thus, the diagnostic quality is reduced. This paper introduces a new filter that take the output of wavelet wiener based filter to design a normalized Least Mean Square (NLMS) based filter in order to reduce the broadband mypotentials (EMG) noise in ECG signal. Testing is performed by taking ECG signal from standard MIT/BIH arrhythmia database sampled at 360 Hz-per second and the model of the EMG noise is generated by shaping white Gaussian noise before we add it to a clean ECG signal to get the noisy signal. The proposed method gives best noise reduction and preserves the ECG waves shape. In addition, the combination shows better results through increasing SNR and reducing Mean Square Error (MSE) compared to other existing techniques.

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Section: Subsection 2 Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Denoising Electrocardiogram Signal from Electromyogram Noise Using Adaptive Filter Combination

Khiter¹,

Adamou‐Mitiche²,

Mitiche³

2020

RIA

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“…The signal which includes the horse's cardiac activity and motion artefacts and the signal which contains only motion signals from the reference sensor could be used as inputs for an adaptive algorithm which can suppress the interference in the useful signal. These adaptive algorithms have proved useful in the past in processing human biological signals, such as BCG and ECG in adults [118][119][120], fetal ECG [121][122][123], speech signals [124,125], or signals used in telecommunications [126].…”

Section: Discussionmentioning

confidence: 99%

Alternative measurement systems for recording cardiac activity in animals: a pilot study

et al. 2022

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Monitoring and assessing cardiac activity in animals, especially heart rate variability, has been gaining importance in the last few years as an indicator of animal health, well-being and physical condition. This pilot study tested the sensors based on ballistocardiography sensing the mechanical vibrations caused by the animal’s cardiovascular system, which have proved useful in measuring cardiac activity in humans. To verify the accuracy of these measurement systems, the conventional measurements based on electrocardiography were carried out and the outcomes were compared. The main objectives were to verify the suitability of these sensors in measuring cardiac activity in animals, to determine the advantages and disadvantages of these sensors, and to identify future challenges. Measurements were performed on various animals, specifically a goat, a cow, a horse, and a sheep. Electrocardiographic measurement, which has demonstrated high accuracy in procedures for animals, was used as the study’s gold standard. A disadvantage of this method, however, is the long time required to prepare animals and shear spots to attach electrodes. The accuracy of a ballistocardiographic sensor was compared to reference electrocardiographic signals based on Bland–Altman plots which analysed the current heart rate values. Unfortunately, the ballistocardiographic sensor was highly prone to poor adhesion to the animal’s body, sensor movement when the animal was restless, and motion artefacts. Ballistocardiographic sensors were shown only to be effective with larger animals, i.e., the horse and the cow, the size of these animals allowing sufficient contact of the sensor with the animal’s body. However, this method’s most significant advantage over the conventional method based on electrocardiography is lower preparation time, since there is no need for precise and time-demanding fixation of the sensor itself and the necessity of shaving the animal’s body.

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“…However, the notch filters suffer from their impulse response's ringing effect due to narrow bandwidth and distorted frequency spectrum. 10 Further, a tunable notch filter was introduced, which could tune its frequency but could not trial variable frequency. 11 Thus, an ANF was designed to modify the tunable notch filter.…”

Section: Related Workmentioning

confidence: 99%

Extended Kalman Filter-Based Power Line Interference Canceller for Electrocardiogram Signal

et al. 2022

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Cardiac diseases constitute a major root of global mortality and they are likely to persist. Electrocardiogram (ECG) is widely opted in clinics to detect countless heart illnesses. Numerous artifacts interfere with the ECG signal, and their elimination is vital to allow medical specialists to acquire valuable statistics from the ECG. The utmost artifact that is added to the ECG signal is power line interference (PLI). Numerous filtering methods have been employed in the literature to eliminate PLI from noisy ECG. This article proposes an extended Kalman filter (EKF)-based adaptive noise canceller (ANC) that comprises PLI frequency as a distinct model parameter. Thus, it is capable of tracking PLI with drifting frequency. The proposed canceller's performance is compared with state-space recursive least squares (SSRLSs) filter-based PLI canceling. The evaluation is carried out for four cases of PLI, that is, PLI with known amplitude and frequency, PLI with unknown amplitude and frequency, PLI with drifting amplitude and frequency, and PLI removal from a real-time ECG recording. The samples of the Massachusetts Institude of Technology (MIT)-Boston's Beth Israel Hospital (BIH) arrhythmia database are considered for the first three cases, whereas, for the fourth case, real ECG signal is taken from armed forces institude of cardiology, the national institude of heart diseases (AFIC/NIHD), Pakistan. Mean square error, frequency spectrum, and noise reduction are selected as performance metrics for comparison. Simulation results depict that the presented EKF-based ANC system outperforms the SSRLS-based ANC system and effectively eliminates PLI from ECG under all four investigated scenarios.

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ECG denoising using weiner filter and adaptive least mean square algorithm

Cited by 16 publications

References 7 publications

Denoising Electrocardiogram Signal from Electromyogram Noise Using Adaptive Filter Combination

Denoising Electrocardiogram Signal from Electromyogram Noise Using Adaptive Filter Combination

Alternative measurement systems for recording cardiac activity in animals: a pilot study

Extended Kalman Filter-Based Power Line Interference Canceller for Electrocardiogram Signal

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