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

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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 (January 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 highlights the research works published in the special issue of Computational Methods for Physiological Signal Processing and Data Analysis. The context introduces three deep learning applications in epileptic seizure detection, human exercise intensity analysis, and lung nodule CT image segmentation, respectively. The article also summarizes the research works on detection of event-related potential in the single-trial electroencephalogram (EEG) signals during the auditory tests, along with the methodology on estimating the generalized exponential distribution parameters using the simulated and real data produced under the Type I generalized progressive hybrid censoring schemes. The article concludes with perspectives and discussions on future trends in biomedical signal processing and data analysis technologies.
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Ahmad, Zeeshan, and Naimul Khan. "A Survey on Physiological Signal-Based Emotion Recognition." Bioengineering 9, no. 11 (November 14, 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 existing literature, in this paper, we review the effect of inter-subject data variance on emotion recognition, important data annotation techniques for emotion recognition and their comparison, data pre-processing techniques for each physiological signal, data splitting techniques for improving the generalization of emotion recognition models and different multimodal fusion techniques and their comparison. Finally, we discuss key challenges and future directions in this field.
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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 by using a low-pass filer, a high-pass filer, 50Hz notch filer and back amplifier circuit, finally a right wave of ECG is received. The characteristics of the system features the merits of high input impedance, high CMRR, low noise, less excursion and high SNR(signal to noise ratio), low cost and so on.
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Dhal, Chandan, and Akshat Wahi. "Psycho-physiological Training Approach for Amputee Rehabilitation." Biomedical Instrumentation & Technology 49, no. 2 (March 1, 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 such as amplification, filtering, and thresholding on an Arduino Uno board, hence removing the need for analog amplifiers and filtering circuits, which have stability issues. The conversion of time domain to frequency domain of any signal gives a detailed data of the signal set. Our main aim is to use this useful data for an alternative methodology for rehabilitation called a psychophysiological approach to rehabilitation in prosthesis, which can reduce the cost of the myoelectric arm, as well as increase its efficiency. This method allows the user to gain control over their muscle sets in a less stressful environment. Further, we also have described how our approach is viable and can benefit the rehabilitation process. We used our DSP EMG signals to play an online game and showed how this approach can be used in rehabilitation.
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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 (September 1, 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 signal analysis for non-contact physiological monitoring to improve quality of life. The radar-based sensors are used for collecting the breathing signal for each subject. The processed signal has been analyzed using continuous wavelet transform (CWT) and wavelet coherence with the Monte Carlo method. The finding shows that there is a significant difference between the three types of breathing patterns; normal, high, and slow. The findings may provide a comprehensive framework for processing and interpreting breathing signals, resulting in breakthroughs in respiratory healthcare, illness management, and overall well-being.
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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 (June 16, 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 (March 30, 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 range of 0.4 to 5Hz. Signal noise can stem from various sources, including the test subject’s muscle movement, respiration, humming, power line interference, or even from the device itself. The research methodology involves a comparison of 3 different order of Butterworth filter circuits and their impact on the signal. The test input signal is derived from an SpO$_2$ simulator, read by a standard PPG sensor, and processed by the internal 12-bit ADC of Nucleo-F429ZI. The resulting data is stored in CSV format for subsequent use in filter design simulations with SPICE. For analog circuit designers, the utilization of SPICE in the form of LTspice proves invaluable. This open software, LTspice, boasts a simple yet powerful interface, facilitating a focus on the conceptualization and performance of the design
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Coatrieux, Jean-Louis. "Signal Processing and Physiological Modeling-Part I: Surface Analysis." Critical Reviews in Biomedical Engineering 30, no. 1-3 (2002): 9–35. http://dx.doi.org/10.1615/critrevbiomedeng.v30.i123.20.

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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 spectroscopy (NIRS). The data set was examined with the aim of generating automatic diagnoses. Comparison of the rate-pressure product (blood pressure multiplied by heart rate) during the time of syncope with a recommended threshold, in addition to comparison with monitoring the fall of systolic blood pressure during prolonged tilt, yielded an 84% accuracy rate for vasovagal syndrome. The thesis reviewed the techniques used on the aforementioned time series by previous researchers, emphasising the concepts underlying "time-frequency analysis", a method for analysing nonstationary signals. Since even healthy patients experience time-varying frequency information in their haemodynamics, a transform known as the Smoothed Pseudo-Wigner Ville Distribution (SPWVD) is well suited to their analysis. This distribution was applied to RR tachograms, plots of heart period against time. After the smoothing parameters of the SPWVD were chosen based on artificial data, the optimised transform was then applied to a second artificial tachogram to calculate the LF/HF (low- to high-frequency) ratio, an indicator of heart rate variability. The computed LF/HF ratio tracked the expected value within an error margin of 3.6%. Finally, by applying the same transform to clinical data, it was proved to offer better resolution than an alternative known as the Lomb periodogram. Classical techniques from the literature predicting vasovagal syncope were found to fail on the current data set: out of 29 tests, only two yielded statistically significant differences between the two patient groups. These were compared with the author's time-frequency analysis of RR tachograms, linear regression of heart rate, and examination of NIRS oscillations and changes on tilt. Of these, the ICFV during time period P3 was found to perform best (negative predictive value: 0.86). A linear classifier was used to combine the best four predictors; it achieved an overall accuracy of 0.88. Following the data-driven approach, an analytical modelling approach was undertaken. In order to define an appropriate model that traded off simplicity with comprehensiveness, the mechanisms of vasovagal syncope were reviewed. A model of orthostasis was developed, validated, and used toward parameter estimation from patient data. Three parameters (baroreceptor operating point, cardiac effectiveness, and baroreflex gain) were gleaned from the supine baseline recording to "normalise" the model for a given patient, before four new parameters (sympathetic and parasympathetic gains at the sino-atrial node, peripheral vasoconstriction gain, and total blood volume) were estimated from the data collected in the upright position. The expectation was that this approach would improve feature extraction (and hence prediction accuracy) as well as the clinical interpretation of the results. However, the modelling approach was found to offer no significant improvement upon the data-driven signal processing results: a linear classifier on the four post-tilt parameters yielded a negative predictive value of just 0.69. This result may have been due to inaccuracies in the time series data owing to instrumentation error. It is also possible that the modelling approach was not able to provide the quality of feature extraction necessary for predicting vasovagal syncope in the elderly. Finally, methods to predict syncope during mid- to late HUT were examined. Using information derived from heart rate and baroreflex sensitivity, a technique was developed to ease patient comfort by terminating the test approximately 2 minutes before syncope was expected to occur.
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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 which uses fundamental physiological signals such as the Electrocardiograph (ECG), to analyze and predict the presence or lack of cognitive attention in individuals during task execution. The primary focus of this study is to identify the correlation between fluctuating level of attention and its implications on the physiological parameters of the body. The system is designed using only those physiological signals that can be collected easily with small, wearable, portable and non-invasive monitors and thereby being able to predict well in advance, an individual’s potential loss of attention and ingression of sleepiness. Several advanced signal processing techniques have been implemented and investigated to derive multiple clandestine and informative features. These features are then applied to machine learning algorithms to produce classification models that are capable of differentiating between the cases of a person being attentive and the person not being attentive. Furthermore, Electroencephalograph (EEG) signals are also analyzed and classified for use as a benchmark for comparison with ECG analysis. For the study, ECG signals and EEG signals of volunteer subjects are acquired in a controlled experiment. The experiment is designed to inculcate and sustain cognitive attention for a period of time following which an attempt is made to reduce cognitive attention of volunteer subjects. The data acquired during the experiment is decomposed and analyzed for feature extraction and classification. The presented results show that to a fairly reasonable accuracy it is possible to detect the presence or lack of attention in individuals with just their ECG signal, especially in comparison with analysis done on EEG signals. The continual work of this research includes other physiological signals such as Galvanic Skin Response, Heat Flux, Skin Temperature and video based facial feature analysis.
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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 for- ward cardiac modelling and inverse ECG analysis is adopted to investigate the effects of sotalol, a compound known to have pro-arrhythmic effects, on ventricular repolarisation. A computational model of sotalol and IKr, an ion channel that plays a critical role in ventricular repolarisation, was developed. This model was incorporated into a model of the human ventricular myocyte, and subsequently arranged in a 1-D fibre model of 200 cells. The model was used to assess the effect of sotalol on IKr, action potential duration and biomarkers of ventricular repolarisation derived from the simulated ECG. In parallel, an automated ECG analysis method based on machine learning, signal processing and time-frequency analysis is developed to identify a number of fiducial points in ECG waveforms so that timing intervals and a smooth T-wave segment can be extracted for morphology analysis. The approach is to train a hidden Markov model (HMM) using a data set of ECG waveforms and the corresponding expert annotations. The signal is first encoded using the undecimated wavelet transform (UWT). The UWT coefficients are used for R-peak detection, signal encoding for the HMM and a wavelet de-noising procedure. Using the Viterbi algorithm, the trained HMM is then applied to a subset of the ECG signal to infer the fiducial points for each heart beat. Furthermore, a method for deriving a confidence measure based on the trained HMM is implemented so that a level of confidence can be associated with the automated annotations. Finally, the T-wave segment is extracted from the de-noised ECG signal for morphology characterisation. This thesis contributes to the literature on automated characterisation of drug ef- fects on ventricular repolarisation in three different ways. Firstly, it investigates the mechanisms underlying the effects of drug inhibition of IKr on ventricular repolarisation as captured by the simulated ECG signal. Secondly, it shows how the combination of UWT encoding and HMM inference can be effectively used to segment 24-hour Holter ECG recordings. Evaluation of the segmentation algorithm on a clinical ECG data set demonstrates the ability of the algorithm to overcome problems associated with existing automated systems, and hence provide a more robust analysis of ECG signals. Finally, the thesis provides insight into the drug effects of sotalol on ventricular repolarisation as captured by biomarkers extracted from the surface ECG.
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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 beyond the use of traditional user performance metrics to include system intelligence measures that are based on current neuroscience theories. These advances are of paramount importance in the success and wide spread use of learning systems that are automated and intelligent. Emotion is considered an important aspect of how learning occurs, and yet estimating it and making adaptive adjustments are not part of most learning systems. In this research we focus on one specific aspect of constructing an adaptive and intelligent learning system, that is, estimation of the emotion of the learner as he/she is using the automated training system. The challenge starts with the definition of the emotion and the utility of it in human life. The next challenge is to measure the co-varying factors of the emotions in a non-invasive way, and find consistent features from these measures that are valid across wide population. In this research we use four physiological sensors that are non-invasive, and establish a methodology of utilizing the data from these sensors using different signal processing tools. A validated set of visual stimuli used worldwide in the research of emotion and attention, called International Affective Picture System (IAPS), is used. A dataset is collected from the sensors in an experiment designed to elicit emotions from these validated visual stimuli. We describe a novel wavelet method to calculate hemispheric asymmetry metric using electroencephalography data. This method is tested against typically used power spectral density method. We show overall improvement in accuracy in classifying specific emotions using the novel method. We also show distinctions between different discrete emotions from the autonomic nervous system activity using electrocardiography, electrodermal activity and pupil diameter changes. Findings from different features from these sensors are used to give guidelines to use each of the individual sensors in the adaptive learning environment.
Ph.D.
School of Electrical Engineering and Computer Science
Engineering and Computer Science
Electrical Engineering PhD
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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 fully studied using computational methods. Therefore, a system that extract and combine features from all physiological signals representative of states such as arrhythmia and loss of blood volume to predict the presence and the severity of such complications is of paramount importance for care givers. This will not only enhance diagnostic methods, but also enable physicians to make more accurate decisions; thereby the overall quality of care provided to patients will improve significantly. In the first part of the dissertation, analysis and processing of ECG signal to detect the most important waves i.e. P, QRS, and T, are described. A wavelet-based method is implemented to facilitate and enhance the detection process. The method not only provides high detection accuracy, but also efficient in regards to memory and execution time. In addition, the method is robust against noise and baseline drift, as supported by the results. The second part outlines a method that extract features from ECG signal in order to classify and predict the severity of arrhythmia. Arrhythmia can be life-threatening or benign. Several methods exist to detect abnormal heartbeats. However, a clear criterion to identify whether the detected arrhythmia is malignant or benign still an open problem. The method discussed in this dissertation will address a novel solution to this important issue. In the third part, a classification model that predicts the severity of loss of blood volume by incorporating multiple physiological signals is elaborated. The features are extracted in time and frequency domains after transforming the signals with Wavelet Transformation (WT). The results support the desirable reliability and accuracy of the system.
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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 are measured in the ear canal by means of absorbance measures using equivalent Thevenin circuit theory. The quantities are then compared to form the shift responses between the baseline (before the activation) and the post-activator effect. This project investigates the shifts in power reflectance and admittance of the middle ear caused by the stapedius-muscle contraction. The wideband characterization (0.1- 8 kHz) of these acoustic reflex-induced shifts is achieved using chirp signals as a probe and through ipsilateral broadband noise activator. The data acquisition and signal processing of the project are carried out using MATLAB software. The hardware consists of National Instruments USB-6212 data acquisition interface and low noise microphone system Etymotic Research ER-10B+. A group of 10 adults including 5 males and 5 females are recruited as the participants for the project. The measurements of the reflectance shifts indicate that the most robust frequency region affected by the acoustic reflex is up to 4 kHz whereas for the admittance shifts, this region is up to 2 kHz. In addition, it is shown that the stapedius-muscle contraction leads to the attenuation of the lowfrequency transmission into the middle ear (less than 1 kHz) consistent with a stiffnesscontrolled system in this range of frequencies. In contrast, the results imply that the activation of the stapedius muscle leads to a slight enhancement of the frequency transmission in the range of 1-4 kHz (corresponding to the speech frequency band). These findings suggest a beneficial role for the stapedius-muscle contraction in the perception of speech during vocalization. Furthermore, the implemented methods in this project  can be useful in better understanding the effect of the stapedius-muscle contraction on the speech perception both in normal hearing and hearing impaired persons.
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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 anesthetic drug effect should have a sound relationship with observations and with the selected control variable. The research objectives are: (1) to explore propofol anesthetic related neurophysiological phenomena that can be applied in the automatic assessment of CNS suppression; (2) to develop a valid control variable for this purpose; (3) by means of digital signal processing and mathematical modeling, to design and to evaluate the performance of an index that correlates with the control variable. This dissertation introduces potentially useful neurophysiological phenomena, such as changes in phase synchronization between different EEG channels due to anesthesia, and painful stimulus evoked responses during the burst suppression. Furthermore, it refines the progression of the time-frequency patterns during the induction of anesthesia and shows their relation to the instant of unresponsiveness. The presented spontaneous and evoked EEG phenomena provide complementary information about the CNS functional suppression. Most significantly, the dissertation proposes a continuous and observation based control variable (r scale) and the means to predict its values by using EEG data. The definition of the scale provides a basis for anticipating the instant of the loss of consciousness. Additionally, the phase synchronization index as an indicator of drug effect is introduced. The approximate entropy descriptor performance is evaluated and optimised with a non-stationary signal recorded during the induction of anesthesia. The results open up opportunities to improve the preciseness, scientific validity and the interpretation of information on the anesthetic effects on CNS, and therefore, to increase the reliability of the anesthesia monitoring. Further work is needed to extend and verify the results in deep anesthesia.
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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 (ECG), Blood Volume Pulse (BVP), Galvanic Skin Response (GSR), Electromyography (EMG), for the corrugator muscle, skin temperature for the finger and respiratory rate. Improvements to the state of PPR emotion detection were made by allowing for 9 different weakly induced emotional states to be detected at nearly 65% accuracy. This is an improvement in the number of states readily detectable. The work presents many investigations into numerical feature extraction from physiological signals and has a chapter dedicated to collating and trialing facial electromyography techniques. There is also a hardware device we created to collect participant self reported emotional states which showed several improvements to experimental procedure.
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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 based on KSS. From the epochs, a number of features were extracted from both physiological signals and the car’s controller area network. The most important features were selected via a feature selection step, using sequential forward floating selection. The selected features were trained and evaluated on linear SVM, Gaussian SVM, KNN, random forest and adaboost. The random forest classifier was chosen in all cases when classifying previously unseen data.The results shows that method 1 was prone to overfit. Method 2 proved to be considerably better, and did not suffer from overfitting. The test results regarding method 2 were as follows; sensitivity = 80.3%, specificity = 96.3% and accuracy = 93.5%.The most prominent features overall were found in the EEG and EOG domain together with the sleep/wake predictor feature. However indications have been made that complexities might contribute to the detection of sleepiness as well, especially the Higuchi’s fractal dimension.
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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 cardiovascular system: hypertension-based heart failure (HF) and coronary artery disease (CAD). A prospective application in cardiac resynchronization therapy (CRT) is also presented, which also includes a model of the therapy. Finally, a final application is presented for the study of the baroreflex responses in the newborn lamb. These case studies include the integration of a pulsatile heart into a global cardiovascular model that captures the short and long term regulation of the cardiovascular system with the representation of heart failure, the analysis of coronary hemodynamics and collateral circulation of patients with triple-vessel disease enduring a coronary artery bypass graft surgery, the construction of a coupled electrical and mechanical cardiac model for the optimization of atrio ventricular and intraventricular delays of a biventricular pacemaker, and a model-based estimation of sympathetic and vagal responses of premature newborn lambs.
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Books on the topic "Physiological signal processing"

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Lessard, Charles S. Signal Processing of Random Physiological Signals. Cham: 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. Singapore: 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. Boston, MA: Springer US, 2000.

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

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

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

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Yŏnʼguwŏn, Hanʼguk Chŏnja Tʻongsin, ed. Saengchʻe chŏngbo chʻŏri kiban weŏrŏbŭl sisŭtʻem kisul e kwanhan yŏnʼgu =: Development of wearable system using physiological signal processing. [Seoul]: 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. Berlin: 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. Amsterdam: North-Holland, 1988.

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S, Reisman Stanley, and Michniak Bozena B, eds. Biomedical engineering principles. Boca Raton: 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, 131–38. New York, NY: 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, 135–89. Singapore: 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, 145–78. Boca Raton: 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, 1–4. Cham: 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, 19–29. Cham: 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, 37–47. Cham: 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, 111–23. Cham: 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, 71–83. Cham: 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, 141–56. Cham: 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, 11–18. Cham: 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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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), 365–68. IEEE, 2024. http://dx.doi.org/10.1109/spic62469.2024.10691499.

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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), 1–5. 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), 393–97. 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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"Addressing Subject-dependency for Affective Signal Processing - Modeling Subjects’ Idiosyncracies." In 2nd International Conference on Physiological Computing Systems. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005330700720077.

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Ghaderi, Adnan, Javad Frounchi, and Alireza Farnam. "Machine learning-based signal processing using physiological signals for stress detection." In 2015 22nd Iranian Conference on Biomedical Engineering (ICBME). IEEE, 2015. http://dx.doi.org/10.1109/icbme.2015.7404123.

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Carmel, S., and A. J. Macy. "Physiological Signal Processing Laboratory for Biomedical Engineering Education." In 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference. IEEE, 2005. http://dx.doi.org/10.1109/iembs.2005.1616551.

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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, January 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 of plant products. Suggestions on a possible role of PPO in various aspects of chloroplast metabolism were also relevant in this context. The characterization of plant PPO genes opened a way for achieving these goals. We reasoned that "understanding PPO targeting and routing, designing ways to manipulate its expression and assessing the effects of such modifications will enable determination of the true properties of the enzyme and open the way for controlling its activity". The objective of the project was to "obtain an insight into the function and biological significance of PPOs" by examining possible function(s) of PPO in photosynthesis and plant-pest interactions using transgenic tomato plants; extending our understanding of PPO routing and assembly and the mechanism of its thylakoid translocation; preparing recombinant PPOs for use in import studies, determination of the genuine properties of PPOs and understanding its assembly and determining the effect of PPO's absence on chloroplast performance. Results obtained during work on the project made it necessary to abandon some minor objectives and devote the effort to more promising topics. Such changes are mentioned in the 'Body of the report' which is arranged according to the objectives of the original proposal. The complex expression pattern of tomato PPO gene family was determined. Individual members of the family are differentially expressed in various parts of the plant and subjected to developmentally regulated turnover. Some members are differentially regulated also by pathogens, wounding and chemical wound signals. Wounding systemically induces PPO activity and level in potato. Only tissues that are developmentally competent to express PPO are capable of responding to the systemic wounding signal by increased accumulation of PPO mRNA. Down regulation of PPO genes causes hyper susceptibility to leaf pathogens in tomato while over expression regulation of PPO expression in tomato plants is their apparent increased tolerance to drought. Both the enhanced disease resistance conferred by PPO over expression and the increased stress tolerance due to down regulation can be used in the engineering of improved crop plants. Photosynthesis rate and variable fluorescence measurements in wild type, and PPO-null and over expressing transgenic tomato lines suggest that PPO does not enable plants to cope better with stressful high light intensities or reactive oxygen species. Rather high levels of the enzyme aggravate the damage caused under such conditions. Our work suggests that PPO's primary role is in defending plants against pathogens and herbivores. Jasmonate and ethylene, and apparently also salicylate, signals involved in responses to wounding and defense against herbivores and pathogens, enhance markedly and specifically the competence of chloroplasts to import and process pPPO. The interaction of the precursor with thylakoid membranes is primarily affected. The routing of PPO shows other unusual properties: stromal processing occurs in two sites, resulting in intermediates that are translocated across thylakoids by two different mechanisms - a DpH- and a Sec-dependent one. It is suggested that the dual pattern of processing and routing constitutes a'fail safe' mechanism, reflecting the need for a rapid and flexible response to defense challenges. Many of the observations described above should be taken into consideration when manipulation of PPO expression is contemplated for use in crop improvement.
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Ron, Eliora, and Eugene Eugene Nester. Global functional genomics of plant cell transformation by agrobacterium. United States Department of Agriculture, March 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 their hosts has been previously studied by genetic-physiological methods. We wanted to make use of the new capabilities to study these interactions on a global scale, using transcription analysis (transcriptomics, microarrays) and proteomics (2D gel electrophoresis and mass spectrometry). The results provided extensive data on the functional genomics under conditions that partially mimic plant infection and – in addition - revealed some surprising and significant data. Thus, we identified the genes whose expression is modulated when Agrobacterium is grown under the acidic conditions found in the rhizosphere (pH 5.5), an essential environmental factor in Agrobacterium – plant interactions essential for induction of the virulence program by plant signal molecules. Among the 45 genes whose expression was significantly elevated, of special interest is the two-component chromosomally encoded system, ChvG/I which is involved in regulating acid inducible genes. A second exciting system under acid and ChvG/Icontrol is a secretion system for proteins, T6SS, encoded by 14 genes which appears to be important for Rhizobium leguminosarum nodule formation and nitrogen fixation and for virulence of Agrobacterium. The proteome analysis revealed that gamma aminobutyric acid (GABA), a metabolite secreted by wounded plants, induces the synthesis of an Agrobacterium lactonase which degrades the quorum sensing signal, N-acyl homoserine lactone (AHL), resulting in attenuation of virulence. In addition, through a transcriptomic analysis of Agrobacterium growing at the pH of the rhizosphere (pH=5.5), we demonstrated that salicylic acid (SA) a well-studied plant signal molecule important in plant defense, attenuates Agrobacterium virulence in two distinct ways - by down regulating the synthesis of the virulence (vir) genes required for the processing and transfer of the T-DNA and by inducing the same lactonase, which in turn degrades the AHL. Thus, GABA and SA with different molecular structures, induce the expression of these same genes. The identification of genes whose expression is modulated by conditions that mimic plant infection, as well as the identification of regulatory molecules that help control the early stages of infection, advance our understanding of this complex bacterial-plant interaction and has immediate potential applications to modify it. We expect that the data generated by our research will be used to develop novel strategies for the control of crown gall disease. Moreover, these results will also provide the basis for future biotechnological approaches that will use genetic manipulations to improve bacterial-plant interactions, leading to more efficient DNA transfer to recalcitrant plants and robust symbiosis. These advances will, in turn, contribute to plant protection by introducing genes for resistance against other bacteria, pests and environmental stress.
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