Size Constraint to Limit Interference in DRL-Free Single-Ended Biopotential Measurements

Purpose: In this work, it is shown that small, battery-powered wireless devices are so robust against electromagnetic interference that single-ended amplifiers can become a viable alternative for biopotential measurements, even without a Driven Right Leg (DRL) circuit. Methods: A power line interference analysis is presented for this case showing that this simple circuitry solution is feasible, and presenting the constraints under which it is so: small-size devices with dimensions less than 40 mm × 20 mm. Results: A functional prototype of a two-electrode wireless acquisition system was implemented using a single-ended amplifier. This allowed validating the power-line interference model with experimental results, including the acquisition of electromyographic (EMG) signals. The prototype, built with a size fulfilling the proposed guidelines, presented power-line interference voltages below 1.2 µVPP when working in an office environment. Conclusion: It can be concluded that a single-ended biopotential amplifier can be used if a sufficiently large isolation impedance is achieved with small-size wireless devices. This approach allows measurements with only two electrodes, a very simple front-end design, and a reduced number of components.

A powerful notch filter for PLI cancelation

<div>Abstract—In this work, we present a powerful notch filter for power-line interference (PLI) cancelation from biomedical signals. This filter has a unit gain and a zero-phase response. Moreover, the filter can be implemented adaptively to adjust its bandwidth based on the signal-to-noise ratio. To realize this filter, a dynamic model is defined for PLI based on its sinusoid property. Then, a constrained least square error estimation is used to emerge the PLI based on the observations while the constraint is the PLI dynamic. At last, the estimated PLI is subtracted from recordings. The proposed filter is assessed using synthetic data and real biomedical recordings in different noise levels. The results demonstrate this filter as a very powerful and effective means for canceling the PLI out.</div>

A powerful notch filter for PLI cancelation

<div>Abstract—In this work, we present a powerful notch filter for power-line interference (PLI) cancelation from biomedical signals. This filter has a unit gain and a zero-phase response. Moreover, the filter can be implemented adaptively to adjust its bandwidth based on the signal-to-noise ratio. To realize this filter, a dynamic model is defined for PLI based on its sinusoid property. Then, a constrained least square error estimation is used to emerge the PLI based on the observations while the constraint is the PLI dynamic. At last, the estimated PLI is subtracted from recordings. The proposed filter is assessed using synthetic data and real biomedical recordings in different noise levels. The results demonstrate this filter as a very powerful and effective means for canceling the PLI out.</div>

Adaptive algorithms: a bibliographic review

The analysis of a research work developed in the company C.V.G CARBONORCA of Venezuela is presented, which has two gas purification plants for the cooking area, designed to purify the gas that comes from the cooking ovens. Each plant is made up of solenoid valves, pneumatic valves, transmitters, process mimic panel and a supervisory system. All these elements are governed by a SIEMENS S5-115U PLC which is in a state of obsolescence, which is why the replacement of these automata by ALLEN BRADLEY ContolLogix automata was designed, in order to guarantee continuity in operations in plant. The research was done with a descriptive design of the field experimental type. A code for each gas treatment plant was obtained in RSLOGIX 5000 v17.00.00 and the update of the database of the supervisory system. The operation of the program was also verified through a simulation of the plant in a supervisory system, the deployment of which was designed for this purpose. Keywords: Automation, Modernization, ControlLogix, Supervisory System, Mimic Panel References [1]M. Simao, N. Mendes, O. Gibaru y P. Neto, «A Review on Electromyography Decoding and Pattern Recognition for Human-Machine Interaction,» IEEE Access, vol. 7, pp. 39564 - 39582, 2019. [2]Instituto de Estadística de la Organización de las Naciones Unidas para la Educación, la Ciencia y la Tecnología, «Clasificación Internacional Normalizada de la Educación CINE,» UNESCO Institute for Statistics, Montréal, 2011. [3]Y. Zheng y H. Xiaogang, «Interference Removal From Electromyography Based on Independent Component Analysis,» IEEE Trans Neural Syst Rehabil Eng, vol. 27, nº 5, pp. 887-894, Mayo 2019. [4]B. Afsharipour, F. Petracca, M. Gasparini y R. Merletti, «Spatial distribution of surface EMG on trapezius and lumbar muscles of violin and cello players in single note playing,» Journal Electromyography Kinesiology, vol. 31, pp. 144 - 153, 2016. [5]M. Niegowski, M. Zivanovic, M. Gómez y P. Lecumberri, «Unsupervised learning technique for surface electromyogram denoising from power line interference and baseline wander,» de 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italia, 2015. [6]S. D. Soedirdjo, K. Ullah y R. Merletti, «Power line interference attenuation in multi-channel sEMG signals: Algorithms and analysis,» de Annu. Int. Conf. IEEE Eng. Med. Biol. Soc., 2015. [7]A. Phinyomark, F. Quaine, S. Charbonnier, C. Serviere, F. Tarpin-Bernard y Y. Laurillau, «Feature extraction of the first difference of EMG time series for EMG pattern recognition,» Computer Methods and Programs in Biomedicine, vol. 117, nº 2, pp. 247-256, Noviembre 2014. [8]M. Malboubi, F. Razzazi, M. Aliyari y A. DAvari, «Power line noise elimination from EMG signals using adaptive Laguerre filter with fuzzy step size,» de 17th Iranian Conference of Biomedical Engineering (ICBME), Isfahan, Iran, 2010. [9]C. Luca, L. Gilmore, M. Kuznetsov y S. Roy, «Filtering the surface EMG signal: Movement artifact and baseline noise contamination,» J. Biomech, pp. 1573-1582, 28 Mayo 2010. [10]R. Mello, L. Oliveira y J. Nadal, «Digital Butterworth filter for subtracting noise from low magnitude surface electromyogram,» Comput Methods Programs Biomed, vol. 1, nº 87, pp. 28-35, 2007. [11]A. Botter y T. Vieira, «Filtered virtual reference: A new method for the reduction of power line interference with minimal distortion of monopolar surface EMG,» IEEE Transactions on Biomedical Engineering, vol. 62, nº 11, pp. 2638 - 2647, 2015. [12]J. R. Potvin y S. H. Brown, «Less is more: high pass filtering, to remove up to 99% of the surface EMG signal power, improves EMG-based biceps brachii muscle force estimates,» J. Electromyogr. Kinesiol., vol. 14, nº 3, pp. 389-399, 2004. [13]D. T. Mewett, K. J. Reynolds y H. Nazeran, «Reducing power line interference in digitised electromyogram recordings by spectrum interpolation,» Med. Biol. Eng. Comput., vol. 4, nº 42, pp. 524-531, 2004. [14]D. T. Mewett, H. Nazeran y K. J. Reynolds, «Removing power line noise from recorded EMG,» de 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Istanbul, Turkey, 2001.

Parallel Distributed Framework for State Space Adaptive Filter for Removal of PLI from Cardiac Signals

Cardiac signals are often corrupted by artefacts like power line interference (PLI) which may mislead the cardiologists to correctly diagnose the critical cardiac diseases. The cardiac signals like high resolution electrocardiogram (HRECG), ultra-high frequency ECG (UHF-ECG) and intracardiac electrograms are the specialized techniques in which higher frequency component of interest up to 1 KHz are observed. Therefore, a state space recursive least square (SSRLS) adaptive algorithm is applied for the removal of PLI and its harmonics. The SSRLS algorithm is an effective approach which extracts the desired cardiac signals from the observed signal without any need of reference signal. However, SSRLS is inherited computational heavy algorithm; therefore, filtration of increased number of PLI harmonics bestow an adverse impact on the execution time of the algorithm. In this paper, a parallel distributed SSRLS (PD-SSRLS) algorithm is introduced which runs the computationally expensive SSRLS adaptive algorithm parallely. The proposed architecture efficiently removes the PLI along with its harmonics even the time alignment among the contributing nodes is not the same. Furthermore, the proposed PD-SSRLS scheme provides less computational cost as compared to sequentially operated SSRLS algorithm. A comparison has been drawn between the proposed PD-SSRLS algorithm and sequentially operated SSRLS algorithm in term of qualitative and quantitative performances. The simulation results show that the proposed PD-SSRLS architecture provides almost same qualitative and quantitative performances than that of sequentially operated SSRLS algorithm with less computational cost.

Performance Evaluation of Wavelet De-Noising Schemes for Suppression of Power Line Noise in Electrocardiogram Signals

Power line noise introduces distortions to recorded electrocardiogram (ECG) signals. These distortions compromise the integrity and negatively affect the interpretation of the ECG signals. Despite the fact that the amplifiers used in biomedical signal processing have high common mode rejection ratio (CMRR), ECG recordings are still often corrupted with residual Power Line Interference (PLI) noise. Further improvement in the hardware solutions do not have significant achievements in PLI noise suppression but rather introduce other adverse effects. Software approach is necessary to refine ECG data. Evaluation of PLI noise suppression in ECG signal in the wavelet domain is presented. The performance of the Hard Threshold Shrinkage Function (HTSF), the Soft Threshold Shrinkage Function (STSF), the Hyperbola Threshold Shrinkage Function (HYTSF), the Garrote Threshold Shrinkage Function (GTSF), and the Modified Garrote Threshold Shrinkage Function (MGTSF) for the suppression of PLI noise are evaluated and compared with the aid of an algorithm. The optimum tuning constant for the Modified Garrote Threshold Shrinkage Function (MGTSF) is found to be 1.18 for PLI noise. GTSF is found to have best performance closely followed by MGTSF in term of filtering Gain. HTSF recorded the lowest Gain. Filtering against PLI noise in the wavelet domain preserves the key features of the signal such as the QRS complex.

An Improved Denoising of Electrocardiogram Signals Based on Wavelet Thresholding

Electrocardiogram (ECG) is the most important signal in the biomedical field for the diagnosis of Cardiac Arrhythmia (CA). ECG signal often interrupted with various noises due to non-stationary nature which leads to poor diagnosis. Denoising process helps the physicians for accurate decision making in treatment. In many papers various noise elimination techniques are tried to enhance the signal quality. In this paper a novel hybrid denoising technique using EMD-DWT for the removal of various noises such as Additive White Gaussian Noise (AWGN), Baseline Wander (BW) noise, Power Line Interference (PLI) noise at various concentrations are compared to the conventional methods in terms of Root Mean Square Error (RSME), Signal to Noise Ratio (SNR), Peak Signal to Noise Ratio (PSNR), Cross-Correlation (CC) and Percent Root Square Difference (PRD). The average values of RMSE, SNR, PSNR, CC and PRD are 0.0890, 9.8821, 14.4464, 0.9872 and 10.9036 for the EMD approach, respectively, and 0.0707, 10.7181, 16.2824, 0.9874 and 10.7245 for the proposed EMD-DWT approach, respectively, by removing AWGN noise. Similarly BW noise and PLI are removed from the ECG signal by calculating the same quality metrics. The proposed methodology has lower RMSE and PRD values, higher SNR, PSNR and CC values than the conventional methods.