Suppression of Baseline Wander Artifact in Magnetocardiogram Using Breathing Sensor

Patel, Rajesh; Gireesan, K.; Senthil‐Nathan, Sengottayan; Janawadkar, M. P.; Radhakrishnan, T. S.

doi:10.1007/s40846-017-0274-9

Cited by 8 publications

(8 citation statements)

References 20 publications

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“…A thermistor sensor (TMP102, Texas Instruments, Dallas, TX) with a resolution of 0.06 °C was fixed below the nostrils of the subjects for measuring local changes in temperature during inhalation and exhalation. 22 A microelectromechanical-based three-axis accelerometer (ADXL335, Analog Devices, Norwood, MA) 23 with a measurement range of ±3 g and sensitivity of 300 mV/g was configured to measure vertical movements ( Z axis) of the abdomen by fixing the accelerometer over a belt attached to the waist of subjects. The sensor measured acceleration proportional to body movements that are on pace with respiration or any other voluntary or involuntary motions.…”

Section: Methodsmentioning

confidence: 99%

“…Cardiac artifacts in the as-recorded EGG traces were removed by applying the wavelet transform technique. 21,22 The 16-channel data of all the subjects in each measurement condition were subjected to the wavelet decomposition procedure, with typically nine levels of decomposition using the “db 10” wavelet. The approximation coefficient of the wavelet decomposition, which usually contains the lowermost frequency content of any given signal, was used to identify the baseline envelope of the ECG, while the detailed coefficients of wavelet decomposition, which contain cardiac features and other high-frequency noise components in the as-recorded data, were eliminated, as shown in Figure 2a .…”

Section: Methodsmentioning

confidence: 99%

“…The efficacy of each method in denoising gastric slow waves is evaluated using a metric, namely, the signal-to-error ratio (SER), 22 which is the ratio of the power of raw signal to the power of the residual signal at various frequencies. The residue (error) is calculated by taking the difference between the raw signal and the denoised signal using each method.…”

Section: Methodsmentioning

confidence: 99%

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A Combined Methodology to Eliminate Artifacts in Multichannel Electrogastrogram Based on Independent Component Analysis and Ensemble Empirical Mode Decomposition

et al. 2018

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Cutaneous measurements of electrogastrogram (EGG) signals are heavily contaminated by artifacts due to cardiac activity, breathing, motion artifacts, and electrode drifts whose effective elimination remains an open problem. A common methodology is proposed by combining independent component analysis (ICA) and ensemble empirical mode decomposition (EEMD) to denoise gastric slow-wave signals in multichannel EGG data. Sixteen electrodes are fixed over the upper abdomen to measure the EGG signals under three gastric conditions, namely, preprandial, postprandial immediately, and postprandial 2 h after food for three healthy subjects and a subject with a gastric disorder. Instantaneous frequencies of intrinsic mode functions that are obtained by applying the EEMD technique are analyzed to individually identify and remove each of the artifacts. A critical investigation on the proposed ICA-EEMD method reveals its ability to provide a higher attenuation of artifacts and lower distortion than those obtained by the ICA-EMD method and conventional techniques, like bandpass and adaptive filtering. Characteristic changes in the slow-wave frequencies across the three gastric conditions could be determined from the denoised signals for all the cases. The results therefore encourage the use of the EEMD-based technique for denoising gastric signals to be used in clinical practice.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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A Combined Methodology to Eliminate Artifacts in Multichannel Electrogastrogram Based on Independent Component Analysis and Ensemble Empirical Mode Decomposition

et al. 2018

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“…The Arduino Uno microcontroller board was used in this setup, which served two purposes: it allowed one to derive the input power supply since it has an in-built linear voltage regulator, 27 and it also allowed one to measure additional basic functions of the human body, such as temperature near the nostrils for the purpose of correcting baseline wander in the recorded MCG by eliminating the breathing artifact. 28,29…”

Section: Single-supply One-channel Ecg Amplifiermentioning

confidence: 99%

Designing a Low-Cost, Single-Supply ECG System for Suppression of Movement Artifact from Contaminated Magnetocardiogram

et al. 2018

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Measurement of the late potentials and His-bundle activity is crucial for many clinical studies using the noncontact and noninvasive magnetocardiography (MCG) technique; these weak signals are extracted by averaging many cardiac cycles aligned using the R-peak of the cardiac cycle identified using an electrocardiography (ECG) lead. ECG is measured simultaneously with MCG using a conventional dual-supply ECG amplifier, which requires either two separate batteries or a single battery with a switching voltage inverter circuit for its proper operation. The ECG circuitry based on two separate batteries requires a relatively large voltage supply (-18 to +18 V). The single-supply (low voltage: 0-9 V) ECG circuitry may be implemented using a switching voltage inverter; however, this mode of operation introduces switching noise in the system. The objective of the present work is to overcome these problems by carefully designing a low-voltage, single-supply ECG system, which can be used simultaneously with the MCG setup without introducing a significant level of additional noise in the MCG measurement system.

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“…Baseline drift and random noise are common degradation problems in signal detection [5, 6]. Several methods based on various theories have been developed to solve these two problems.…”

Section: Introductionmentioning

confidence: 99%

A general-purpose signal processing algorithm for biological profiles using only first-order derivative information

Liu

Lin

2019

BMC Bioinformatics

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BackgroundAutomatic signal-feature extraction algorithms are crucial for profile processing in bioinformatics. Both baseline drift and noise seriously affect the position and peak area of signals. An efficient algorithm named the derivative passing accumulation (DPA) method for simultaneous baseline correction and signal extraction is presented in this article. It is an efficient method using only the first-order derivatives which are obtained through taking the simple differences.ResultsWe developed a new signal feature extracting procedure. The vector representing the discrete first-order derivative was divided into negative and positive parts and then accumulated to build a signal descriptor. The signals and background fluctuations are easily separated according to this descriptor via thresholding. In addition, the signal peaks are simultaneously located by checking the corresponding intervals in the descriptor. Therefore, the eternal issues of parsing the 1-dimensional output of detectors in biological instruments are solved together. Thereby, the baseline is corrected, and the signal peaks are extracted.ConclusionsWe have introduced a new method for signal peak picking, where baseline computation and peak identification are performed jointly. The testing results of both authentic and artificially synthesized data illustrate that the new method is powerful, and it could be a better choice for practical processing.

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Suppression of Baseline Wander Artifact in Magnetocardiogram Using Breathing Sensor

Cited by 8 publications

References 20 publications

A Combined Methodology to Eliminate Artifacts in Multichannel Electrogastrogram Based on Independent Component Analysis and Ensemble Empirical Mode Decomposition

A Combined Methodology to Eliminate Artifacts in Multichannel Electrogastrogram Based on Independent Component Analysis and Ensemble Empirical Mode Decomposition

Designing a Low-Cost, Single-Supply ECG System for Suppression of Movement Artifact from Contaminated Magnetocardiogram

A general-purpose signal processing algorithm for biological profiles using only first-order derivative information

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