Relevant bibliographies by topics / Biomedical signals processing

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Biomedical signals processing'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Biomedical signals processing"

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Shabbir, Chowdhury, and K. Prahlad Rao Dr. "Popularly used Signal Processing and Analysis Techniques for Biomedical Signals." Journal of Computers and Signals (JCS) 1, no. 1 (2020): 11–15. https://doi.org/10.5281/zenodo.4316515.

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Human life is precious. It is often encountered by many diseases posing the physicians' new challenges every time in their treatment, such for example COVID-19. Unless the root cause of the disease is known clearly, it is difficult to plan for its treatment. A thorough understanding is possible only when the biological signals are observed and analyzed clearly. For this purpose, technology plays an important role in the handling of the signals. Biomedical signal acquisition, processing, and analysis are the prime stages in the diagnosis of any disease. In this paper, a few advanced signal
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Lessard, Charles S. "Signal Processing of Random Physiological Signals." Synthesis Lectures on Biomedical Engineering 1, no. 1 (2006): 1–232. http://dx.doi.org/10.2200/s00012ed1v01y200602bme001.

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Barabás, Zsolt Albert, and Zoltán Germán-Salló. "Educational trends in biomedical signal processing." Acta Marisiensis. Seria Technologica 19, no. 2 (2022): 41–45. http://dx.doi.org/10.2478/amset-2022-0016.

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Abstract Biomedical Engineering programs are present at many universities all over the world with an increasing trend. New generations of biomedical engineers have to face the challenges of health care systems round the world which need a large number of professionals not only to support the present technology in the health care system but to develop new devices and services. Biomedical Engineering supports patient diagnosis and treatment by installing, testing, calibrating and repairing biomedical equipment; training users; maintaining safe operations. Also, approves new equipment by conducti
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Gu, Jie. "AI-empowered neural processing for intelligent human-machine interface and biomedical devices." Open Access Government 43, no. 1 (2024): 268–69. http://dx.doi.org/10.56367/oag-043-11463.

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AI-empowered neural processing for intelligent human-machine interface and biomedical devices Jie Gu, Associate Professor from Northwestern University, examines AI-empowered neural processing for intelligent human-machine interface and biomedical devices. Most conventional wearable devices rely on motion detection or image classifications to capture users’ activities. However, they lack the ability to decode neural signals generated by the human body. Neural signals, such as EEG, ECG, and EMG, offer a rich amount of information on a person’s physiological and psychological activities. Recognit
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DOROBANŢU (RADU), LAURA ELENA. "BIOMEDICAL SIGNAL PROCESSING IN COGNITIVE RESEARCH: BRAIN FINGERPRINTING AND POLYGRAPH TESTING." REVUE ROUMAINE DES SCIENCES TECHNIQUES — SÉRIE ÉLECTROTECHNIQUE ET ÉNERGÉTIQUE 70, no. 2 (2025): 263–68. https://doi.org/10.59277/rrst-ee.2025.2.10.

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This study examines the role of signal processing in Brain Fingerprinting (BF) and Polygraph Testing (PT), two techniques that rely on the acquisition and analysis of biomedical signals. The research examines the methods of biomedical signal processing, including filtering, feature extraction, and classification, to improve accuracy and reliability. Experimental results suggest that advanced electroencephalograph (EEG) signal processing techniques can enhance Brain Fingerprinting applications, while polygraph testing remains widely used due to its accessibility. The findings contribute to the
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Tran, Yvonne. "EEG Signal Processing for Biomedical Applications." Sensors 22, no. 24 (2022): 9754. http://dx.doi.org/10.3390/s22249754.

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Chupov, A. A., A. E. Zhdanov, F. K. Rakhmatullov, R. F. Rakhmatullov, and A. Yu Dolganov. "ECG Signals Processing by Using Wavelet Analysis: Diagnostic Capabilities." Ural Radio Engineering Journal 5, no. 4 (2021): 337–52. http://dx.doi.org/10.15826/urej.2021.5.4.001.

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The problem of recognition and classification of biomedical signals is a complex problem related to the interdisciplinary field of computer science and medicine. Within the framework of the project implementation of the development of the new defibrillation equipment, it is necessary to solve the problems of analyzing biomedical signals of the electrocardiogram to obtain a diagnostic solution with the possibility of assigning a specific condition to the pathological condition of the patient. This article presents the analysis of electrocardiogram signals, considering the technical aspects of t
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Martinek, Radek, Martina Ladrova, Michaela Sidikova, et al. "Advanced Bioelectrical Signal Processing Methods: Past, Present, and Future Approach—Part III: Other Biosignals." Sensors 21, no. 18 (2021): 6064. http://dx.doi.org/10.3390/s21186064.

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Analysis of biomedical signals is a very challenging task involving implementation of various advanced signal processing methods. This area is rapidly developing. This paper is a Part III paper, where the most popular and efficient digital signal processing methods are presented. This paper covers the following bioelectrical signals and their processing methods: electromyography (EMG), electroneurography (ENG), electrogastrography (EGG), electrooculography (EOG), electroretinography (ERG), and electrohysterography (EHG).
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Simpson, D. M., A. De Stefano, R. Allen, and M. E. Lutman. "Demystifying Biomedical Signals: A student centred approach to learning signal processing." Medical Engineering & Physics 27, no. 7 (2005): 583–89. http://dx.doi.org/10.1016/j.medengphy.2004.11.011.

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Vidaurre, Carmen, Tilmann H. Sander, and Alois Schlögl. "BioSig: The Free and Open Source Software Library for Biomedical Signal Processing." Computational Intelligence and Neuroscience 2011 (2011): 1–12. http://dx.doi.org/10.1155/2011/935364.

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BioSig is an open source software library for biomedical signal processing. The aim of the BioSig project is to foster research in biomedical signal processing by providing free and open source software tools for many different application areas. Some of the areas where BioSig can be employed are neuroinformatics, brain-computer interfaces, neurophysiology, psychology, cardiovascular systems, and sleep research. Moreover, the analysis of biosignals such as the electroencephalogram (EEG), electrocorticogram (ECoG), electrocardiogram (ECG), electrooculogram (EOG), electromyogram (EMG), or respir
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Dissertations / Theses on the topic "Biomedical signals processing"

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Ghaderi, Foad. "Signal processing techniques for extracting signals with periodic structure : applications to biomedical signals." Thesis, Cardiff University, 2010. http://orca.cf.ac.uk/55183/.

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In this dissertation some advanced methods for extracting sources from single and multichannel data are developed and utilized in biomedical applications. It is assumed that the sources of interest have periodic structure and therefore, the periodicity is exploited in various forms. The proposed methods can even be used for the cases where the signals have hidden periodicities, i.e., the periodic behaviour is not detectable from their time representation or even Fourier transform of the signal. For the case of single channel recordings a method based on singular spectrum anal ysis (SSA) of the
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Samonas, Michael C. "Pre-processing of magnetoencephalographic signals." Thesis, University of Surrey, 1996. http://epubs.surrey.ac.uk/843348/.

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This thesis is concerned with the processing of MagnetoEncephaloGraphic (MEG) signals before their further analysis for clinical purposes. An overview of recent methods that have been applied to brain signals is first presented. The area of interference elimination is covered then, as the MEG signals suffer from the heart interfering magnetic field which in the majority of the experimental situations outweighs the signal of interest. The framework of a two step algorithm which first identifies the interference and then eliminates it by orthogonal projecting it to the contaminated signal is pro
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Millette, Veronique. "Signal processing of heart signals for the quantification of non-deterministic events." Thesis, University of Ottawa (Canada), 2010. http://hdl.handle.net/10393/28579.

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The issue of cavitation in mechanical heart valve (MHV) patients was first recognized when damaged mechanical heart valves were observed. Cavitation bubble implosion can cause mechanical damage to the valve structure and blood elements, when it occurs near the surface of the MHV. Some methods have been suggested to quantify the level of cavitation present in MHV patients. Two algorithms from the literature were selected for implementation and comparison. These algorithms were selected as they have been previously proposed and implemented for in vivo heart signals. In this thesis, a rigorous cl
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Mangieri, Eduardo. "An analogue approach for the processing of biomedical signals." Thesis, University of Southampton, 2012. https://eprints.soton.ac.uk/348009/.

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Constant device scaling has signifcantly boosted electronic systems design in the digital domain enabling incorporation of more functionality within small silicon area and at the same time allows high-speed computation. This trend has been exploited for developing high-performance miniaturised systems in a number of application areas like communication, sensor network, main frame computers, biomedical information processing etc. Although successful, the associated cost comes in the form of high leakage power dissipation and systems reliability. With the increase of customer demands for smarter
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Nunes, Neuza Filipa Martins. "Algorithms for time series clustering applied to biomedical signals." Master's thesis, Faculdade de Ciências e Tecnologia, 2011. http://hdl.handle.net/10362/5666.

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Thesis submitted in the fulfillment of the requirements for the Degree of Master in Biomedical Engineering<br>The increasing number of biomedical systems and applications for human body understanding creates a need for information extraction tools to use in biosignals. It’s important to comprehend the changes in the biosignal’s morphology over time, as they often contain critical information on the condition of the subject or the status of the experiment. The creation of tools that automatically analyze and extract relevant attributes from biosignals, providing important information to the us
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Jayaraman, Vinoth, Sivakumaran Sivalingam, and Sangeetha Munian. "Analysis of Real Time EEG Signals." Thesis, Linnéuniversitetet, Institutionen för fysik och elektroteknik (IFE), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-34164.

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The recent evolution in multidisciplinary fields of Engineering, neuroscience, microelectronics, bioengineering and neurophysiology have reduced the gap between human and machine intelligence. Many methods and algorithms have been developed for analysis and classification of bio signals, 1 or 2-dimensional, in time or frequency distribution. The integration of signal processing with the electronic devices serves as a major root for the development of various biomedical applications. There are many ongoing research in this area to constantly improvise and build an efficient human- robotic syste
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Santos, Rui Pedro Silvestre dos. "Time series morphological analysis applied to biomedical signals events detection." Master's thesis, Faculdade de Ciências e Tecnologia, 2011. http://hdl.handle.net/10362/10227.

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Dissertation submitted in the fufillment of the requirements for the Degree of Master in Biomedical Engineering<br>Automated techniques for biosignal data acquisition and analysis have become increasingly powerful, particularly at the Biomedical Engineering research field. Nevertheless, it is verified the need to improve tools for signal pattern recognition and classification systems, in which the detection of specific events and the automatic signal segmentation are preliminary processing steps. The present dissertation introduces a signal-independent algorithm, which detects significant ev
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Lander, P. T. "Computer processing methods for the recovery of low amplitude ECG signals." Thesis, University of Sussex, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.373153.

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Vartak, Aniket. "BIOSIGNAL PROCESSING CHALLENGES IN EMOTION RECOGNITIONFOR ADAPTIVE LEARNING." Doctoral diss., University of Central Florida, 2010. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2667.

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User-centered computer based learning is an emerging field of interdisciplinary research. Research in diverse areas such as psychology, computer science, neuroscience and signal processing is making contributions the promise to take this field to the next level. Learning systems built using contributions from these fields could be used in actual training and education instead of just laboratory proof-of-concept. One of the important advances in this research is the detection and assessment of the cognitive and emotional state of the learner using such systems. This capability moves development
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Guilak, Farzin G. "A Spline Framework for Optimal Representation of Semiperiodic Signals." Thesis, Portland State University, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3722040.

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<p> Semiperiodic signals possess an underlying periodicity, but their constituent spectral components include stochastic elements which make it impossible to analytically determine locations of the signal's critical points. Mathematically, a signal's critical points are those at which it is not differentiable or where its derivative is zero. In some domains they represent characteristic points, which are locations indicating important changes in the underlying process reflected by the signal.</p><p> For many applications in healthcare, knowledge of precise locations of these points provides
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Books on the topic "Biomedical signals processing"

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Esposito, Anna, Marcos Faundez-Zanuy, Francesco Carlo Morabito, and Eros Pasero, eds. Quantifying and Processing Biomedical and Behavioral Signals. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-95095-2.

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Devasahayam, Suresh R. Signals and Systems in Biomedical Engineering: Signal Processing and Physiological Systems Modeling. Springer US, 2000.

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Devasahayam, Suresh R. Signals and Systems in Biomedical Engineering: Physiological Systems Modeling and Signal Processing. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3531-0.

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Devasahayam, Suresh R. Signals and Systems in Biomedical Engineering: Signal Processing and Physiological Systems Modeling. 2nd ed. Springer US, 2013.

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Semmlow, John L. Signals and systems for bioengineers: A MATLAB-based introduction. 2nd ed. Elsevier/Academic Press, 2012.

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Semmlow, John L. Circuits, signals, and systems for bioengineers: A MATLAB-based introduction. Elsevier Academic Press, 2005.

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Burke, David P. Real -time Processing of Biological Signals to Provide Multimedia Biofeedback as an Aid to Relaxation Therapy. University College Dublin, 1998.

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Manfredi, Claudia, ed. Models and analysis of vocal emissions for biomedical applications: 5th International Workshop: December 13-15, 2007, Firenze, Italy. Firenze University Press, 2007. http://dx.doi.org/10.36253/978-88-5518-027-6.

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The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies. The Workshop has the sponsorship of: Ente Cassa Risparmio di Firenze, COST Action 2103, Biomedical Signal Processing and Control Journal (Elsevi
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Obeid, Iyad, Ivan Selesnick, and Joseph Picone, eds. Biomedical Signal Processing. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67494-6.

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Naik, Ganesh, ed. Biomedical Signal Processing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-13-9097-5.

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Book chapters on the topic "Biomedical signals processing"

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Lessard, Charles S. "Biomedical Engineering Signal Analysis." In Signal Processing of Random Physiological Signals. Springer International Publishing, 2006. http://dx.doi.org/10.1007/978-3-031-01610-3_1.

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Azad, Md Khurshidul, John D’Angelo, Peshala T. Gamage, Shehab Ismail, Richard H. Sandler, and Hansen A. Mansy. "Spatial Distribution of Seismocardiographic Signals." In Biomedical Signal Processing. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67494-6_5.

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Richter, Michael M., Sheuli Paul, Veton Këpuska, and Marius Silaghi. "Biomedical Signals: ECG, EEG." In Signal Processing and Machine Learning with Applications. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-319-45372-9_25.

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Kim, Dong Kyu, and Sam Keene. "Fast Automatic Artifact Annotator for EEG Signals Using Deep Learning." In Biomedical Signal Processing. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67494-6_7.

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Devasahayam, Suresh R. "Discrete Signal Processing." In Signals and Systems in Biomedical Engineering. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-5332-1_6.

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Gomes, Juliana C., Vanessa Marques, Caio de Brito, et al. "Machine Learning for Detection and Classification of Motor Imagery in Electroencephalographic Signals." In Biomedical Signal Processing. CRC Press, 2023. http://dx.doi.org/10.1201/9781003201137-9.

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Devasahayam, Suresh R. "Discrete Signal Processing for Physiological Signals." In Signals and Systems in Biomedical Engineering: Physiological Systems Modeling and Signal Processing. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3531-0_5.

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Santana, Maíra A., Juliana C. Gomes, Arianne S. Torcate, et al. "Emotion Recognition from Electroencephalographic and Peripheral Physiological Signals Using Artificial Intelligence with Explicit Features." In Biomedical Signal Processing. CRC Press, 2023. http://dx.doi.org/10.1201/9781003201137-10.

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López, Alberto, and Francisco Ferrero. "Biomedical Signal Processing and Artificial Intelligence in EOG Signals." In Advances in Non-Invasive Biomedical Signal Sensing and Processing with Machine Learning. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-23239-8_8.

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Marchesi, Carlo, Matteo Paoletti, and Loriano Galeotti. "Data, Signals, and Information: Medical Applications of Digital Signal Processing." In Advanced Methods of Biomedical Signal Processing. John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9781118007747.ch2.

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Conference papers on the topic "Biomedical signals processing"

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Pelc, Mariusz, Dariusz Mikołajewski, Patryk Mendoń, et al. "ML-Powered Biomedical Signals Processing using Hybrid Filters." In 2024 Progress in Applied Electrical Engineering (PAEE). IEEE, 2024. http://dx.doi.org/10.1109/paee63906.2024.10701444.

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Akbari, Ali, Zahra Moradi Shahrbabak, and Mehran Jahed. "A Novel Study on Classifying Smokers in Apnea Patients Using Physiological Signals Processing." In 2024 31st National and 9th International Iranian Conference on Biomedical Engineering (ICBME). IEEE, 2024. https://doi.org/10.1109/icbme64381.2024.10895145.

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Cuartas, Santiago González, Eduardo Montoya Guevara, Juliana Moreno Rada, Luisa María Zapata Saldarriaga, and John Fredy Ochoa Gomez. "BrainView: A Novel Platform for Acquisition, Processing, and Warehouse of EEG Signals from a Portable Device." In 2024 3rd International Congress of Biomedical Engineering and Bioengineering (CIIBBI). IEEE, 2024. https://doi.org/10.1109/ciibbi63846.2024.10784751.

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Laganà, Filippo, Danilo Pratticò, Giuseppe Oliva, et al. "Developing an electronic device for the acquisition and processing of ECG signals: a Soft Computing approach." In 2024 International Workshop on Quantum & Biomedical Applications, Technologies, and Sensors (Q-BATS). IEEE, 2024. https://doi.org/10.1109/q-bats63267.2024.10874007.

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"Session TP8a2: Biomedical signal processing." In 2017 51st Asilomar Conference on Signals, Systems, and Computers. IEEE, 2017. http://dx.doi.org/10.1109/acssc.2017.8335598.

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Cox, J. R. "Recollections on the processing of biomedical signals." In ACM conference. ACM Press, 1987. http://dx.doi.org/10.1145/41526.41535.

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"Session TP8a1 Biomedical Signal Processing." In Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, 2004. IEEE, 2004. http://dx.doi.org/10.1109/acssc.2004.1399420.

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"Session TA8a1: Biomedical signal processing I." In 2015 49th Asilomar Conference on Signals, Systems and Computers. IEEE, 2015. http://dx.doi.org/10.1109/acssc.2015.7421234.

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"Session TA8b2: Biomedical signal processing II." In 2015 49th Asilomar Conference on Signals, Systems and Computers. IEEE, 2015. http://dx.doi.org/10.1109/acssc.2015.7421272.

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"Biomedical Signal Processing and Analysis II." In 2019 International Conference on Systems, Signals and Image Processing (IWSSIP). IEEE, 2019. http://dx.doi.org/10.1109/iwssip.2019.8787298.

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