A filter to suppress ECG baseline wander and preserve ST-segment accuracy in real-time environment

Frankel, Roddy A.; Pottala, Erik W.; Bowser, Richard W.; Bailey, John R.

doi:10.1016/s0022-0736(10)80031-7

Cited by 3 publications

(5 citation statements)

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“…6 shows that the histogram of errors is more spread for linear interpolation, while WPL interpolation's spread more closely resembles that of cubic spline interpolation. In addition, these figures comply with American Heart Association and International Electrotechnical Commission standards which allow a maximum error of 100 μV for clinical ECG systems [11]. Also, Fig.…”

Section: Resultsmentioning

confidence: 60%

Computationally efficient real‐time interpolation algorithm for non‐uniform sampled biosignals

Güven

Eftekhar

Kindt

et al. 2016

Healthcare Technology Letters

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This Letter presents a novel, computationally efficient interpolation method that has been optimised for use in electrocardiogram baseline drift removal. In the authors’ previous Letter three isoelectric baseline points per heartbeat are detected, and here utilised as interpolation points. As an extension from linear interpolation, their algorithm segments the interpolation interval and utilises different piecewise linear equations. Thus, the algorithm produces a linear curvature that is computationally efficient while interpolating non-uniform samples. The proposed algorithm is tested using sinusoids with different fundamental frequencies from 0.05 to 0.7 Hz and also validated with real baseline wander data acquired from the Massachusetts Institute of Technology University and Boston's Beth Israel Hospital (MIT-BIH) Noise Stress Database. The synthetic data results show an root mean square (RMS) error of 0.9 μV (mean), 0.63 μV (median) and 0.6 μV (standard deviation) per heartbeat on a 1 mVp–p 0.1 Hz sinusoid. On real data, they obtain an RMS error of 10.9 μV (mean), 8.5 μV (median) and 9.0 μV (standard deviation) per heartbeat. Cubic spline interpolation and linear interpolation on the other hand shows 10.7 μV, 11.6 μV (mean), 7.8 μV, 8.9 μV (median) and 9.8 μV, 9.3 μV (standard deviation) per heartbeat.

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Section: Resultsmentioning

confidence: 60%

Computationally efficient real‐time interpolation algorithm for non‐uniform sampled biosignals

Güven

Eftekhar

Kindt

et al. 2016

Healthcare Technology Letters

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“…Filtering with 1-Hz cutoff frequency gave small RMS error in previous studies; however, as Fig. 7 demonstrates, this was achieved at the cost of a relatively large distortion of cardiac complexes (8,9). The same figure shows that the distortion of QRST complexes quickly increases above the 0.7-Hz cutoff.…”

Section: Comparison With Previous Methodsmentioning

confidence: 72%

“…The first component was a sine wave with a 0.2-Hz frequency and a 400-µV peak amplitude, and the second component was a cosine wave with a 0.45-Hz frequency and a 300-µV peak amplitude. These two components result in a BW that was simulated in previous studies to span typical respiratory frequencies (8,9). In addition, a low-frequency random component was also added to the BW.…”

Section: Signals With Simulated Baseline Driftmentioning

confidence: 99%

Enhancing the Precision of ECG Baseline Correction: Selective Filtering and Removal of Residual Error

Shusterman

Shah

Beigel

et al. 2000

Computers and Biomedical Research

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“…However, in offline processing or considering the delay in responses allows acceptable results. Forward-backward or bidirectional filtering can be applied to get such linear phase results (Pottala et al, 1990) (Frankel et al, 1991). An alternative method of using linear phase low pass filter was proposed by De Pinto (1992).…”

Section: Introductionmentioning

confidence: 99%

“…The output of the low pass IIR filter was subtracted from the original signal to obtain a BW free signal. Though linear phase filtering can be achieved easily using FIR filters but as they tend to have long impulse responses, it leads to very high filter order with complex multiplications (Frankel et al, 1991;Van Alste and Schilder, 1985). Jane et al (1992) described a two stage cascaded adaptive filter which consists of an adaptive notch filter at zero frequency at first stage and an adaptive impulse correlated filter in second stage.…”

Section: Introductionmentioning

confidence: 99%

Removal of ECG Baseline Wander using Savitzky-Golay Filter Based Method

Nahiyan

Amin

2017

Bangla J Med Phys

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ECG (Electrocardiogram) is a measure of heart's electrical activity. As the body is a volume conductor, ECG signal can be recorded from the body surface. The signal while being recorded from the body surface gets corrupted by various types of noise or artifact. Among these, baseline wander is a type of noise that severely hampers the ECG signal. Baseline wander is particularly of very low frequency; it causes the ECG signal to deviate from its isoelectric line and causes the signal to ride on the lower frequency artifact. The proposed method is based on Savitzky-Golay filter, which is a moving average filter that takes into consideration the polynomial order along with moving averaging when approximating the signal. It enables to approximate the baseline wander quite efficiently. Though in some cases it distorts the ECG signal to some extent, when compared with usual polynomial fitting method, it demonstrates superiority in terms of accuracy, simplicity and generalization.

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A filter to suppress ECG baseline wander and preserve ST-segment accuracy in real-time environment

Cited by 3 publications

References 0 publications

Computationally efficient real‐time interpolation algorithm for non‐uniform sampled biosignals

Computationally efficient real‐time interpolation algorithm for non‐uniform sampled biosignals

Enhancing the Precision of ECG Baseline Correction: Selective Filtering and Removal of Residual Error

Removal of ECG Baseline Wander using Savitzky-Golay Filter Based Method

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