Relevant bibliographies by topics / Physiological signal processing

Contents

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

Academic literature on the topic 'Physiological signal processing'

Author: Grafiati
Published: 8 February 2025
Last updated: 25 July 2025

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Journal articles on the topic "Physiological signal processing"

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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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Wu, Yunfeng, Sridhar Krishnan, and Behnaz Ghoraani. "Computational Methods for Physiological Signal Processing and Data Analysis." Computational and Mathematical Methods in Medicine 2022 (August 10, 2022): 1–4. http://dx.doi.org/10.1155/2022/9861801.

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Biomedical signal processing and data analysis play pivotal roles in the advanced medical expert system solutions. Signal processing tools are able to diminish the potential artifact effects and improve the anticipative signal quality. Data analysis techniques can assist in reducing redundant data dimensions and extracting dominant features associated with pathological status. Recent computational methods have greatly improved the effectiveness of signal processing and data analysis, to support the efficient point-of-care diagnosis and accurate medical decision-making. This editorial article h
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Ahmad, Zeeshan, and Naimul Khan. "A Survey on Physiological Signal-Based Emotion Recognition." Bioengineering 9, no. 11 (2022): 688. http://dx.doi.org/10.3390/bioengineering9110688.

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Physiological signals are the most reliable form of signals for emotion recognition, as they cannot be controlled deliberately by the subject. Existing review papers on emotion recognition based on physiological signals surveyed only the regular steps involved in the workflow of emotion recognition such as pre-processing, feature extraction, and classification. While these are important steps, such steps are required for any signal processing application. Emotion recognition poses its own set of challenges that are very important to address for a robust system. Thus, to bridge the gap in the e
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Ma, Jing, Jun Xu, Hai Bo Xu, Yu Wang, and Sheng Xu Yin. "Design of ECG Signal Acquisition and Processing Circult." Applied Mechanics and Materials 236-237 (November 2012): 856–61. http://dx.doi.org/10.4028/www.scientific.net/amm.236-237.856.

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ECG signal is, as a vital method performed on the heart study and clinical diagnosis of cardiovascular diseases, an important human physiological signal, containing the human cardiac conduction system of physiological and pathological information. Aiming at the weak low frequency characteristic of ECG signals, the core circuit based on the AD620 and LM324 amplifier is given. After analyzing the major components of the ECG signal and the frequency range of interference, weak ECG signal collected by the electrodes is amplified by the preamplifier circuit, and the interference then is wiped out b
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Dhal, Chandan, and Akshat Wahi. "Psycho-physiological Training Approach for Amputee Rehabilitation." Biomedical Instrumentation & Technology 49, no. 2 (2015): 138–43. http://dx.doi.org/10.2345/0899-8205-49.2.138.

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Electromyography (EMG) signals are very noisy and difficult to acquire. Conventional techniques involve amplification and filtering through analog circuits, which makes the system very unstable. The surface EMG signals lie in the frequency range of 6Hz to 600Hz, and the dominant range is between the ranges from 20Hz to 150Hz.1 Our project aimed to analyze an EMG signal effectively over its complete frequency range. To remove these defects, we designed what we think is an easy, effective, and reliable signal processing technique. We did spectrum analysis, so as to perform all the processing suc
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Bota, Patrícia, Rafael Silva, Carlos Carreiras, Ana Fred, and Hugo Plácido da Silva. "BioSPPy: A Python toolbox for physiological signal processing." SoftwareX 26 (May 2024): 101712. http://dx.doi.org/10.1016/j.softx.2024.101712.

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Razman, Nur Fatin Shazwani Nor, Haslinah Mohd Nasir, Suraya Zainuddin, Noor Mohd Ariff Brahin, Idnin Pasya Ibrahim, and Mohd Syafiq Mispan. "Signal processing for abnormalities estimation analysis." International Journal of Advances in Applied Sciences 13, no. 3 (2024): 600. http://dx.doi.org/10.11591/ijaas.v13.i3.pp600-610.

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Pneumonia, asthma, sudden infant death syndrome (SIDS), and the most recent epidemic, COVID-19, are the most common lung diseases associated with respiratory difficulties. However, existing health monitoring systems use large and in-contact devices, which causes an uncomfortable experience. The difficulty in acquiring breathing signals for non-stationary individuals limits the use of ultra-wideband radar for breathing monitoring. This is due to ineffective signal clutter removal and body movement removal algorithms for collecting accurate breathing signals. This paper proposes a breathing sign
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Nur, Fatin Shazwani Nor Razman, Mohd Nasir Haslinah, Zainuddin Suraya, Mohd Ariff Brahin Noor, Pasya Ibrahim Idnin, and Syafiq Mispan Mohd. "Signal processing for abnormalities estimation analysis." International Journal of Advances in Applied Sciences (IJAAS) 13, no. 3 (2024): 600–610. https://doi.org/10.11591/ijaas.v13.i3.pp600-610.

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Pneumonia, asthma, sudden infant death syndrome (SIDS), and the most recent epidemic, COVID-19, are the most common lung diseases associated with respiratory difficulties. However, existing health monitoring systems use large and in-contact devices, which causes an uncomfortable experience. The difficulty in acquiring breathing signals for non-stationary individuals limits the use of ultra-wideband radar for breathing monitoring. This is due to ineffective signal clutter removal and body movement removal algorithms for collecting accurate breathing signals. This paper proposes a breathing sign
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Istomin, Andrey, and Egor Demidchenko. "DIGITAL PROCESSING OF THE ELECTROMYOGRAM SIGNAL." Modern Technologies and Scientific and Technological Progress 2020, no. 1 (2020): 111–12. http://dx.doi.org/10.36629/2686-9896-2020-1-111-112.

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As a result of the study of physiological processes occurring in the human hand, data were obtained that are subject to analysis and statistical processing in the environment for solving engineering and scientific problems of Matlab
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Pandi and Tomy Abuzairi. "Effect of Filters in Photoplethysmography Analog Signals Using Open-Source LTspice Software." International Journal of Electrical, Computer, and Biomedical Engineering 2, no. 1 (2024): 88–100. http://dx.doi.org/10.62146/ijecbe.v2i1.32.

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Analog signal processing plays a crucial role in the realm of biomedical signal analysis. This study investigates the application of analog signal processing techniques in the domain of biomedical signals, focusing on enhancing the quality and reliability of recorded physiological data. The primary emphasis is on the implementation of analog filters and amplifiers to address challenges such as noise reduction, signal conditioning, and overall signal improvement. The processing of physiological signals, such as photoplethysmography (PPG), necessitates the use of amplifiers and filters within a
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Dissertations / Theses on the topic "Physiological signal processing"

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Ebden, Mark. "Predicting orthostatic vasovagal syncope with signal processing and physiological modelling." Thesis, University of Oxford, 2006. http://ora.ox.ac.uk/objects/uuid:f6e4b491-76b4-4f99-b95d-cae30fa704f5.

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Orthostatic vasovagal syncope is the sudden loss of consciousness resulting from a temporary impairment of cerebral blood flow, within approximately an hour of standing. Patients who suffer from this problem have "vasovagal syndrome". The purpose of this thesis was to devise a method to detect the syndrome following the assumption of upright position. Data from 106 syncopal patients undergoing head-up tilt table testing (HUT) were acquired, including electrical activity of the heart (electrocardiogram), blood pressure, oxygen saturation, and cerebral perfusion parameters from near-infrared spe
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Belle, Ashwin. "A Physiological Signal Processing System for Optimal Engagement and Attention Detection." VCU Scholars Compass, 2012. http://scholarscompass.vcu.edu/etd/394.

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In today’s high paced, hi-tech and high stress environment, with extended work hours, long to-do lists and neglected personal health, sleep deprivation has become common in modern culture. Coupled with these factors is the inherent repetitious and tedious nature of certain occupations and daily routines, which all add up to an undesirable fluctuation in individuals’ cognitive attention and capacity. Given certain critical professions, a momentary or prolonged lapse in attention level can be catastrophic and sometimes deadly. This research proposes to develop a real-time monitoring system wh
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Brennan, Thomas Patrick. "Signal processing methods for characterisation of ventricular repolarisation using the surface electrocardiogram." Thesis, University of Oxford, 2009. http://ora.ox.ac.uk/objects/uuid:39ae285a-b8dd-4aae-b60e-36f95fb84f37.

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This thesis investigates the mechanisms underlying drug-induced arrhythmia and pro- poses a new approach for the automated analysis of the electrocardiogram (ECG). The current method of assessing the cardiac safety of new drugs in clinical trials is by the measurement and analysis of the QT interval. However, the sensitivity and specificity of the QT interval has been questioned and alternative biomarkers based on T-wave mor- phology have been proposed in the literature. The mechanisms underlying drug effects on T-wave morphology are not clearly understood. Therefore, a combined approach of fo
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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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Bsoul, Abed Al-Raoof. "PROCESSING AND CLASSIFICATION OF PHYSIOLOGICAL SIGNALS USING WAVELET TRANSFORM AND MACHINE LEARNING ALGORITHMS." VCU Scholars Compass, 2011. http://scholarscompass.vcu.edu/etd/258.

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Over the last century, physiological signals have been broadly analyzed and processed not only to assess the function of the human physiology, but also to better diagnose illnesses or injuries and provide treatment options for patients. In particular, Electrocardiogram (ECG), blood pressure (BP) and impedance are among the most important biomedical signals processed and analyzed. The majority of studies that utilize these signals attempt to diagnose important irregularities such as arrhythmia or blood loss by processing one of these signals. However, the relationship between them is not yet fu
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Ghaffari, Ghazaleh. "Estimation of Stapedius-Muscle Activation using Ear Canal Absorbance Measurements : An Application of Signal Processing in Physiological Acoustics." Thesis, Linköpings universitet, Institutionen för medicinsk teknik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-98992.

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The stapedius muscle, which is located in the middle ear, goes into contraction when the ear is exposed to high sound intensities. This muscle activation is called ‘the acoustic reflex’. Measurement of the acoustic reflex is clinically of importance since it can reveal diagnostic information about the middle ear’s pathologies. Moreover, this muscle-activation alters the acoustic characteristics of the middle ear (i.e. the acoustic impedance and the power reflectance), which in turn, can significantly manipulate one’s perception of sounds. In the present study, these acoustic characteristics ar
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Koskinen, M. (Miika). "Automatic assessment of functional suppression of the central nervous system due to propofol anesthetic infusion:from EEG phenomena to a quantitative index." Doctoral thesis, University of Oulu, 2006. http://urn.fi/urn:isbn:9514281756.

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Abstract The rationale for automatically monitoring anesthetic drug effects on the central nervous system (CNS) is to improve possibilities to gain objective information on a patient's state and to adjust the medication individually. Although monitors have shown their usefulness in practice, there are still a number of unclear issues, especially with respect to the scientific foundations and validity of CNS monitoring techniques, and in monitoring the light hypnotic levels. Current monitors are, for example, often based on heuristics and ad hoc solutions. However, a quantitative index for anes
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Creemers, Warren. "On the Recognition of Emotion from Physiological Data." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2013. https://ro.ecu.edu.au/theses/680.

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This work encompasses several objectives, but is primarily concerned with an experiment where 33 participants were shown 32 slides in order to create ‗weakly induced emotions‘. Recordings of the participants‘ physiological state were taken as well as a self report of their emotional state. We then used an assortment of classifiers to predict emotional state from the recorded physiological signals, a process known as Physiological Pattern Recognition (PPR). We investigated techniques for recording, processing and extracting features from six different physiological signals: Electrocardiogram (E
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Keelan, Oliver, and Henrik Mårtensson. "Feature Engineering and Machine Learning for Driver Sleepiness Detection." Thesis, Linköpings universitet, Institutionen för medicinsk teknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-142001.

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Falling asleep while operating a moving vehicle is a contributing factor to the statistics of road related accidents. It has been estimated that 20% of all accidents where a vehicle has been involved are due to sleepiness behind the wheel. To prevent accidents and to save lives are of uttermost importance. In this thesis, given the world’s largest dataset of driver participants, two methods of evaluating driver sleepiness have been evaluated. The first method was based on the creation of epochs from lane departures and KSS, whilst the second method was based solely on the creation of epochs ba
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Ojeda, Avellaneda David. "Multi-resolution physiological modeling for the analysis of cardiovascular pathologies." Phd thesis, Université Rennes 1, 2013. http://tel.archives-ouvertes.fr/tel-01056825.

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This thesis presents three main contributions in the context of modeling and simulation of physiological systems. The first one is a formalization of the methodology involved in multi-formalism and multi-resolution modeling. The second one is the presentation and improvement of a modeling and simulation framework integrating a range of tools that help the definition, analysis, usage and sharing of complex mathematical models. The third contribution is the application of this modeling framework to improve diagnostic and therapeutic strategies for clinical applications involving the cardiovascul
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Books on the topic "Physiological signal processing"

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

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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. Springer US, 2000.

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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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Naik, Ganesh R. Applications, challenges, and advancements in electromyography signal processing. Medical Information Science Reference, 2014.

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Bronzino, Joseph D., Peterson Donald R, and Hualou Liang. Biosignal processing: Principles and practices. Taylor & Francis, 2012.

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Yŏnʼguwŏn, Hanʼguk Chŏnja Tʻongsin, ред. Saengchʻe chŏngbo chʻŏri kiban weŏrŏbŭl sisŭtʻem kisul e kwanhan yŏnʼgu =: Development of wearable system using physiological signal processing. Chŏngbo Tʻongsinbu, 2008.

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NATO Advanced Research Workshop on Molecular and Cellular Processes Underlying Desensitization and Adaptation to Signal Molecules (1986 Noordwijkerhout, Netherlands). Molecular mechanisms of desensitization to signal molecules. Springer-Verlag, 1987.

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IFIP-IMIA Working Conference on Progress in Biological Function Analysis by Computer Technologies (1987 Berlin, Germany). Progress in computer-assisted function analysis: Proceedings of the IFIP-IMIA Working Conference on Progress in Biological Function Analysis by Computer Technologies, Berlin, GDR, 19-23 May, 1987. North-Holland, 1988.

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S, Reisman Stanley, and Michniak Bozena B, eds. Biomedical engineering principles. Taylor & Francis, 2005.

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Book chapters on the topic "Physiological signal processing"

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Degoulet, Patrice, and Marius Fieschi. "Physiological Signal Processing." In Introduction to Clinical Informatics. Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4612-0675-0_10.

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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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Lin, James C. "System Analysis and Signal Processing." In Noninvasive Physiological Measurement. CRC Press, 2024. http://dx.doi.org/10.1201/9781003315223-6.

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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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Lessard, Charles S. "Basis Functions and Signal Representation." In Signal Processing of Random Physiological Signals. Springer International Publishing, 2006. http://dx.doi.org/10.1007/978-3-031-01610-3_4.

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Lessard, Charles S. "Sampling Theory and Analog-to-Digital Conversion." In Signal Processing of Random Physiological Signals. Springer International Publishing, 2006. http://dx.doi.org/10.1007/978-3-031-01610-3_6.

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Lessard, Charles S. "Digital Filters." In Signal Processing of Random Physiological Signals. Springer International Publishing, 2006. http://dx.doi.org/10.1007/978-3-031-01610-3_11.

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Lessard, Charles S. "Correlation Functions." In Signal Processing of Random Physiological Signals. Springer International Publishing, 2006. http://dx.doi.org/10.1007/978-3-031-01610-3_9.

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Lessard, Charles S. "Fast Fourier Transform." In Signal Processing of Random Physiological Signals. Springer International Publishing, 2006. http://dx.doi.org/10.1007/978-3-031-01610-3_13.

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Lessard, Charles S. "Classification of Signals." In Signal Processing of Random Physiological Signals. Springer International Publishing, 2006. http://dx.doi.org/10.1007/978-3-031-01610-3_3.

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Conference papers on the topic "Physiological signal processing"

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Madaan, Alisha, and Amritpal Singh. "From Sensors to Insight: A Review of Physiological Signal Processing for Stress Prediction." In 2025 AI-Driven Smart Healthcare for Society 5.0. IEEE, 2025. https://doi.org/10.1109/ieeeconf64992.2025.10962872.

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Gui, Jiemiao, Lincong Zhang, and Hongbo Zhu. "An Enhanced Autoencoder for Lossy Compression of Auxiliary Physiological Data." In 2024 2nd International Conference on Signal Processing and Intelligent Computing (SPIC). IEEE, 2024. http://dx.doi.org/10.1109/spic62469.2024.10691499.

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Liu, Anbang, Shanlin Xiao, and Wenming Zheng. "Unsupervised Motion-Robust Self-Distillation Framework for Remote Physiological Measurement." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10888538.

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Bütefür, Judith, and Elsa Kirchner. "Preliminary Technical Test of Different Physiological Modalities to Detect Workload in Humans in Microgravity." In 18th International Conference on Bio-inspired Systems and Signal Processing. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013092600003911.

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S, Shymala Gowri, Niranjana RS, Wael Suliman, Hema Priya N, and VinayaKumar Ravi. "A Comparative Analysis of Physiological Signal Processing and Classification: Advances in EEG, EMG, and EOG Modalities." In 2024 6th International Symposium on Advanced Electrical and Communication Technologies (ISAECT). IEEE, 2024. https://doi.org/10.1109/isaect64333.2024.10799879.

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Haule, Hollan, Ian Piper, Patricia Jones, Tsz-Yan Milly Lo, and Javier Escudero. "Collaborative Learning of Common Latent Representations in Routinely Collected Multivariate ICU Physiological Signals." In 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW). IEEE, 2024. http://dx.doi.org/10.1109/icasspw62465.2024.10627040.

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"Session MA8b4: Physiological signal processing." In 2014 48th Asilomar Conference on Signals, Systems and Computers. IEEE, 2014. http://dx.doi.org/10.1109/acssc.2014.7094422.

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"Physiological Signal Processing for Emotional Feature Extraction." In International Conference on Physiological Computing Systems. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0004727500400047.

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Perez-Rosero, Maria S., Behnaz Rezaei, Murat Akcakaya, and Sarah Ostadabbas. "Decoding emotional experiences through physiological signal processing." In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2017. http://dx.doi.org/10.1109/icassp.2017.7952282.

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Ferdi, Youcef. "Improved lowpass differentiator for physiological signal processing." In 2010 7th International Symposium on Communication Systems, Networks & Digital Signal Processing (CSNDSP 2010). IEEE, 2010. http://dx.doi.org/10.1109/csndsp16145.2010.5580319.

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Reports on the topic "Physiological signal processing"

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Steffens, John C., and Eithan Harel. Polyphenol Oxidases- Expression, Assembly and Function. United States Department of Agriculture, 1995. http://dx.doi.org/10.32747/1995.7571358.bard.

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Polyphenol oxidases (PPOs) participate in the preparation of many plant products on the one hand and cause considerable losses during processing of plant products on the other hand. However, the physiological functions of plant PPO were still a subject of controversy at the onset of the project. Preliminary observations that suggested involvement of PPOs in resistance to herbivores and pathogens held great promise for application in agriculture but required elucidation of PPO's function if modulation of PPO expression is to be considered for improving plant protection or storage and processing
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Ron, Eliora, and Eugene Eugene Nester. Global functional genomics of plant cell transformation by agrobacterium. United States Department of Agriculture, 2009. http://dx.doi.org/10.32747/2009.7695860.bard.

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The aim of this study was to carry out a global functional genomics analysis of plant cell transformation by Agrobacterium in order to define and characterize the physiology of Agrobacterium in the acidic environment of a wounded plant. We planed to study the proteome and transcriptome of Agrobacterium in response to a change in pH, from 7.2 to 5.5 and identify genes and circuits directly involved in this change. Bacteria-plant interactions involve a large number of global regulatory systems, which are essential for protection against new stressful conditions. The interaction of bacteria with
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