Noise reduction and lossless ECG encoding

Dhar, S.; Mukhopadhyay, Sunil; Mitra, S.; Baig, Mirza Mansoor; Mitra, Madhuchhanda

doi:10.1109/ciec.2014.6959080

Cited by 4 publications

(4 citation statements)

References 21 publications

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“…Elliptic filtering and Butterworth Chebyshev type I, type II, and elliptic filtering were applied by Bhogeshwar and colleagues [ 16 ] to reduce noise from ECG signals. Once these algorithms applied to the MIT-BIH ECG database signals, the IIR elliptic filter is said to have increased SNR performance [ 17 ].…”

Section: Existing Methodologymentioning

confidence: 99%

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Stability and Phase Response Analysis of Optimum Reduced-Order IIR Filter Designs for ECG R-Peak Detection

Amhia

Wadhwani

2022

Journal of Healthcare Engineering

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Cardiovascular health and training success can be assessed using electrocardiogram (ECG) data. For over a quarter of a century, an individual’s resting heart rate is varying more. As a result, it has become the subject of inquiry and reveals the intricate relationship between the human body and its environment. The autonomic nervous system has impact on blood flow system based on the rate of heartbeats. However, heart rate variation (HRV) characteristics analysis throughout the time period has lack of physical activity information. In the presence of patient movement, ECG signal is suffering from hard artefacts. Time-varying HRV parameters can be derived from low-frequency (LF) and high-frequency (HF) domains of the correct frequency. However, sometimes it is critical to ensuring accurate detection of the R-peak position. The proposed ROIIR (reduced-order IIR) offers 8.8% improvement in peak-to-peak swing than earlier IIR filter. We present an advanced filtering algorithm that is used for R-peak detection.

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Section: Existing Methodologymentioning

confidence: 99%

“…High-frequency disturbances whose order was experimentally selected were removed by Dhar et al [ 17 ] using a Butterworth low-pass filter. To make the ECG file smaller, we compress the filtered signal using a stringent lossless approach.…”

Section: Existing Methodologymentioning

confidence: 99%

Stability and Phase Response Analysis of Optimum Reduced-Order IIR Filter Designs for ECG R-Peak Detection

Amhia

Wadhwani

2022

Journal of Healthcare Engineering

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“…In [11] Dhar et.al, proposed an efficient technique to eliminate high frequency and power line interference noise from digitized Electrocardiogram (ECG) signal. First contaminated ECG signal is passed through a Butterworth low-pass filter to reduce high frequency noises whose order is chosen on experimental basis.…”

Section: Introductionmentioning

confidence: 99%

Performance of Efficient RLS Adaptive Algorithm Used to Enhance the ECG Signal Quality in Telecardiology

Gowri¹,

Kumar²

2015

IJCA

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When acquiring the Electrocardiogram (ECG) signal from the person, it should be preprocess before sending to the analyst for taking decision of the signal, because signal should be affected with various artifacts. For numerous applications of noise cancellation in the corrupted signals, adaptive filters play important role. The various artifacts which commonly occur in the acquisition of ECG signals are physiological and non-physiological noises, those are main supply power line interference, muscle artifact, electrode motion artifact and base line wander noises. The adaptive Least Mean Square (LMS) algorithm provides a low convergence rate, so that for fast convergence rate and reduced noise, in this paper an efficient Recursive Least Square algorithm is considered, for removing of power line noise and muscle noise. For double validation of the signal, and for high Signal to Noise Ratio (SNR), fast convergence rate, is achieved by using LMS to RLS adaptive algorithm at the cost of additional computations.

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“…With the lossless compression, the reconstructed signal is identical to the original. Different methods are proposed in this axe to deal with the ECG signal [10][11][12][13][14]. The disadvantages of the lossless methods are the compression rate, which does not highly minimize the storage space comparing to the original signal [15].…”

Section: Introductionmentioning

confidence: 99%

The ECG Signal Compression Using an Efficient Algorithm Based on the DWT

B’charri¹,

Latif²,

Jenkal³

et al. 2016

ijacsa

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Abstract-The storage capacity of the ECG records presents an important issue in the medical practices. These data could contain hours of recording, which needs a large space for storage to save these records. The compression of the ECG signal is widely used to deal with this issue. The problem with this process is the possibility of losing some important features of the ECG signal. This loss could influence negatively the analyzing of the heart condition. In this paper, we shall propose an efficient method of the ECG signal compression using the discrete wavelet transform and the run length encoding. This method is based on the decomposition of the ECG signal, the thresholding stage and the encoding of the final data. This method is tested on some of the MIT-BIH arrhythmia signals from the international database Physionet. This method shows high performances comparing to other methods recently published.

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Noise reduction and lossless ECG encoding

Cited by 4 publications

References 21 publications

Stability and Phase Response Analysis of Optimum Reduced-Order IIR Filter Designs for ECG R-Peak Detection

Stability and Phase Response Analysis of Optimum Reduced-Order IIR Filter Designs for ECG R-Peak Detection

Performance of Efficient RLS Adaptive Algorithm Used to Enhance the ECG Signal Quality in Telecardiology

The ECG Signal Compression Using an Efficient Algorithm Based on the DWT

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