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.

ECG Analysis for HRV Detection

In this paper an algorithm is proposed for estimation of HRV with better accuracy and results. We are making use of Auto Regressive Model (AR Model) for the estimation. Since ECG wave is also contaminated with a lot of noise such as Power Line Interference (PLI), EMG and just some common artifacts like breathing disturbance’s, so to filter out all this noise from the wave we are using Cumulant based AR model for filtering the wave. Using IoT we will later use real time ECG waves to estimate HRV.