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Journal articles on the topic 'Physiological signal processing'
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1
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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Waleed, Rasha. "REAL-TIME PROCESSING STAGES OF ELECTROCARDIOGRAM SIGNAL: A REVIEW." Journal of Modern Technology and Engineering 9, no. 1 (April 30, 2024): 39–54. http://dx.doi.org/10.62476/jmte9139.
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Signal analysis is a multidisciplinary field that combines various processes to create robust pipelines for automating data analysis. Within the medical field, its application revolves around physiological signals. Electrocardiogram (ECG) signal provides vital information concerning various cardiac conditions affecting the human heart. ECG analysis is a central pillar of medical research with the goal of detecting and preventing potentially fatal cardiac events. This review article aims to provide a comprehensive analysis of real-time processing techniques for electrocardiogram signals. It discusses the different methods and algorithms used for detecting and analyzing ECG signals in real-time, with a focus on their effectiveness and efficiency. Where, it evaluates the use of various techniques such as ECG signal preprocessing (denoising), ECG fiducial points detecting and ECG signal classification. It also highlights the challenges faced in real-time ECG signal processing, such as High-fidelity signal acquisition and noise reduction, and computational efficiency and resource constraints. Furthermore, the article presents an overview of the existing QRS-peak detection strategies, including methods such as Haar wavelet transform, and modified MaMeMi Filter.
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Raghavendra, U., U. Rajendra Acharya, and Hojjat Adeli. "Artificial Intelligence Techniques for Automated Diagnosis of Neurological Disorders." European Neurology 82, no. 1-3 (2019): 41–64. http://dx.doi.org/10.1159/000504292.
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Background: Authors have been advocating the research ideology that a computer-aided diagnosis (CAD) system trained using lots of patient data and physiological signals and images based on adroit integration of advanced signal processing and artificial intelligence (AI)/machine learning techniques in an automated fashion can assist neurologists, neurosurgeons, radiologists, and other medical providers to make better clinical decisions. Summary: This paper presents a state-of-the-art review of research on automated diagnosis of 5 neurological disorders in the past 2 decades using AI techniques: epilepsy, Parkinson’s disease, Alzheimer’s disease, multiple sclerosis, and ischemic brain stroke using physiological signals and images. Recent research articles on different feature extraction methods, dimensionality reduction techniques, feature selection, and classification techniques are reviewed. Key Message: CAD systems using AI and advanced signal processing techniques can assist clinicians in analyzing and interpreting physiological signals and images more effectively.
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Zhan, Hua Qun, Bin Xu, Yong Ping Cao, and Quan Jiang Liu. "Physiological Signal Detection Instrument Based on FPGA." Advanced Materials Research 850-851 (December 2013): 576–79. http://dx.doi.org/10.4028/www.scientific.net/amr.850-851.576.
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This paper introduces a kind of physiological signal detection instrument based on FPGA. The system uses FPGA as the core, SC0073 as the sensor, the signal amplification, filtering, A/D conversion, and finally the signal does a digital signal processing in FPGA , and under the control of FPGA, displayed on the LCD screen in time. Results show that the system is stable and have a good performance.
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Lin, Chia‐Hung, Jian‐Xing Wu, Neng‐Sheng Pai, Pi‐Yun Chen, Chien‐Ming Li, and Ching Chou Pai. "Intelligent physiological signal infosecurity: Case study in photoplethysmography (PPG) signal." IET Signal Processing 16, no. 3 (December 2, 2021): 267–80. http://dx.doi.org/10.1049/sil2.12089.
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Pinto, Gisela, João M. Carvalho, Filipa Barros, Sandra C. Soares, Armando J. Pinho, and Susana Brás. "Multimodal Emotion Evaluation: A Physiological Model for Cost-Effective Emotion Classification." Sensors 20, no. 12 (June 21, 2020): 3510. http://dx.doi.org/10.3390/s20123510.
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Emotional responses are associated with distinct body alterations and are crucial to foster adaptive responses, well-being, and survival. Emotion identification may improve peoples’ emotion regulation strategies and interaction with multiple life contexts. Several studies have investigated emotion classification systems, but most of them are based on the analysis of only one, a few, or isolated physiological signals. Understanding how informative the individual signals are and how their combination works would allow to develop more cost-effective, informative, and objective systems for emotion detection, processing, and interpretation. In the present work, electrocardiogram, electromyogram, and electrodermal activity were processed in order to find a physiological model of emotions. Both a unimodal and a multimodal approach were used to analyze what signal, or combination of signals, may better describe an emotional response, using a sample of 55 healthy subjects. The method was divided in: (1) signal preprocessing; (2) feature extraction; (3) classification using random forest and neural networks. Results suggest that the electrocardiogram (ECG) signal is the most effective for emotion classification. Yet, the combination of all signals provides the best emotion identification performance, with all signals providing crucial information for the system. This physiological model of emotions has important research and clinical implications, by providing valuable information about the value and weight of physiological signals for emotional classification, which can critically drive effective evaluation, monitoring and intervention, regarding emotional processing and regulation, considering multiple contexts.
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Lin, Wenqian, Chao Li, and Yunmian Zhang. "Model of Emotion Judgment Based on Features of Multiple Physiological Signals." Applied Sciences 12, no. 10 (May 15, 2022): 4998. http://dx.doi.org/10.3390/app12104998.
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The model of emotion judgment based on features of multiple physiological signals was investi-gated. In total, 40 volunteers participated in the experiment by playing a computer game while their physiological signals (skin electricity, electrocardiogram (ECG), pulse wave, and facial electromy-ogram (EMG)) were acquired. The volunteers were asked to complete an emotion questionnaire where six typical events that appeared in the game were included, and each volunteer rated their own emotion when experiencing the six events. Based on the analysis of game events, the signal data were cut into segments and the emotional trends were classified. The correlation between data segments and emotional trends was built using a statistical method combined with the questionnaire responses. The set of optimal signal features was obtained by processing the data of physiological signals, extracting the features of signal data, reducing the dimensionality of signal features, and classifying the emotion based on the set of signal data. Finally, the model of emotion judgment was established by selecting the features with a significance of 0.01 based on the correlation between the features in the set of optimal signal features and emotional trends.
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Karlsson, J. Stefan, Karin Roeleveld, Christer Grönlund, Andreas Holtermann, and Nils Östlund. "Signal processing of the surface electromyogram to gain insight into neuromuscular physiology." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 367, no. 1887 (October 30, 2008): 337–56. http://dx.doi.org/10.1098/rsta.2008.0214.
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A surface electromyogram (sEMG) contains information about physiological and morphological characteristics of the active muscle and its neural strategies. Because the electrodes are situated on the skin above the muscle, the sEMG is an easily obtainable source of information. However, different combinations of physiological and morphological characteristics can lead to similar sEMG signals and sEMG recordings contain noise and other artefacts. Therefore, many sEMG signal processing methods have been developed and applied to allow insight into neuromuscular physiology. This paper gives an overview of important advances in the development and applications of sEMG signal processing methods, including spectral estimation, higher order statistics and spatio-temporal processing. These methods provide information about muscle activation dynamics and muscle fatigue, as well as characteristics and control of single motor units (conduction velocity, firing rate, amplitude distribution and synchronization).
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Djara, Tahirou, Abdoul Matine Ousmane, and Antoine Vianou. "Emotional State Recognition Using Facial Expression, Voice, and Physiological Signal." International Journal of Robotics Applications and Technologies 6, no. 1 (January 2018): 1–20. http://dx.doi.org/10.4018/ijrat.2018010101.
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Emotion recognition is an important aspect of affective computing, one of whose aims is the study and development of behavioral and emotional interaction between human and machine. In this context, another important point concerns acquisition devices and signal processing tools which lead to an estimation of the emotional state of the user. This article presents a survey about concepts around emotion, multimodality in recognition, physiological activities and emotional induction, methods and tools for acquisition and signal processing with a focus on processing algorithm and their degree of reliability.
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Singh, Omkar. "Physiological Time Series Processing via Empirical Wavelet Transform." Advanced Science, Engineering and Medicine 12, no. 5 (May 1, 2020): 582–87. http://dx.doi.org/10.1166/asem.2020.2557.
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This paper presents the efficacy of empirical wavelet transform (EWT) for physiological time series processing. At first, EWT is applied to multivariate heterogeneous physiological time series. Secondly, EWT is used for the removal of fast temporal scales in multiscale entropy analysis. Empirical mode decomposition is an adaptive data analysis method in the sense that it does not require prior information about the signal statistics and tend to decompose a signal into various constituent modes. The utility of Standard EMD algorithm is however limited to single channel data as it suffers from the problems of mode alignment and mode mixing when applied channel wise for multivariate data. The standard EMD algorithm was extended to multivariate Empirical mode decomposition (MEMD) that can be used analyze a multivariate data. The MEMD can only be applied to multivariate data in which all the channels have equal data length. EWT is another adaptive technique for mode extraction in a signal using empirical scaling and wavelet functions. The multiscale entropy (MSE) algorithm is generally used to quantify the complexity of a time series. The original MSE approach utilizes a coarse-graining process for the removal of fast temporal scales in a time series which is equivalent to applying a finite impulse response (FIR) moving average filter. In Refined Multiscale entropy (RMSE), the FIR filter was replaced with a low pass Butterworth filter which exhibits a better frequency response than that of a FIR filter. In this paper we have presented a new approach for the removal of fast temporal scales based on empirical wavelet transform. The empirical wavelet transform is also used as an innovative filtering approach in multiscale entropy analysis.
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Xu, Wei, Jingxin Wang, Simin Cheng, and Xiaomin Xu. "Flexible organic transistors for neural activity recording." Applied Physics Reviews 9, no. 3 (September 2022): 031308. http://dx.doi.org/10.1063/5.0102401.
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Flexible electronics capable of interacting with biological tissues, and acquiring and processing biological information, are increasingly demanded to capture the dynamic physiological processes, understand the living organisms, and treat human diseases. Neural interfaces with a high spatiotemporal resolution, extreme mechanical compliance, and biocompatibility are essential for precisely recording brain activity and localizing neuronal patterns that generate pathological brain signals. Organic transistors possess unique advantages in detecting low-amplitude signals at the physiologically relevant time scales in biotic environments, given their inherent amplification capabilities for in situ signal processing, designable flexibility, and biocompatibility features. This review summarizes recent progress in neural activity recording and stimulation enabled by flexible and stretchable organic transistors. We introduce underlying mechanisms for multiple transistor building blocks, followed by an explicit discussion on effective design strategies toward flexible and stretchable organic transistor arrays with improved signal transduction capabilities at the transistor/neural interfaces.
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Appriou, Aurélien, Léa Pillette, David Trocellier, Dan Dutartre, Andrzej Cichocki, and Fabien Lotte. "BioPyC, an Open-Source Python Toolbox for Offline Electroencephalographic and Physiological Signals Classification." Sensors 21, no. 17 (August 26, 2021): 5740. http://dx.doi.org/10.3390/s21175740.
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Research on brain–computer interfaces (BCIs) has become more democratic in recent decades, and experiments using electroencephalography (EEG)-based BCIs has dramatically increased. The variety of protocol designs and the growing interest in physiological computing require parallel improvements in processing and classification of both EEG signals and bio signals, such as electrodermal activity (EDA), heart rate (HR) or breathing. If some EEG-based analysis tools are already available for online BCIs with a number of online BCI platforms (e.g., BCI2000 or OpenViBE), it remains crucial to perform offline analyses in order to design, select, tune, validate and test algorithms before using them online. Moreover, studying and comparing those algorithms usually requires expertise in programming, signal processing and machine learning, whereas numerous BCI researchers come from other backgrounds with limited or no training in such skills. Finally, existing BCI toolboxes are focused on EEG and other brain signals but usually do not include processing tools for other bio signals. Therefore, in this paper, we describe BioPyC, a free, open-source and easy-to-use Python platform for offline EEG and biosignal processing and classification. Based on an intuitive and well-guided graphical interface, four main modules allow the user to follow the standard steps of the BCI process without any programming skills: (1) reading different neurophysiological signal data formats, (2) filtering and representing EEG and bio signals, (3) classifying them, and (4) visualizing and performing statistical tests on the results. We illustrate BioPyC use on four studies, namely classifying mental tasks, the cognitive workload, emotions and attention states from EEG signals.
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Valenza, Gaetano, Nicola Toschi, and Riccardo Barbieri. "Uncovering brain–heart information through advanced signal and image processing." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 374, no. 2067 (May 13, 2016): 20160020. http://dx.doi.org/10.1098/rsta.2016.0020.
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Through their dynamical interplay, the brain and the heart ensure fundamental homeostasis and mediate a number of physiological functions as well as their disease-related aberrations. Although a vast number of ad hoc analytical and computational tools have been recently applied to the non-invasive characterization of brain and heart dynamic functioning, little attention has been devoted to combining information to unveil the interactions between these two physiological systems. This theme issue collects contributions from leading experts dealing with the development of advanced analytical and computational tools in the field of biomedical signal and image processing. It includes perspectives on recent advances in 7 T magnetic resonance imaging as well as electroencephalogram, electrocardiogram and cerebrovascular flow processing, with the specific aim of elucidating methods to uncover novel biological and physiological correlates of brain–heart physiology and physiopathology.
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Hurr, Chansol, Caiyan Li, and Heng Li. "Feature Extraction and Recognition of Human Physiological Signals Based on the Convolutional Neural Network." Mobile Information Systems 2022 (July 18, 2022): 1–10. http://dx.doi.org/10.1155/2022/8982881.
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Human physiological signal processing is one of the research fields widely used in recent years. Research on human physiological signals plays a vital role in predicting human health and detecting and classifying certain disease outbreaks. The network of human physiological signals is difficult to determine because it contains a lot of information about human activities. To this end, a variety of feature extraction, feature selection, and classification algorithms have been implemented in the anomaly prediction process. However, it has the main disadvantage of classification results, using a large number of features and increasing complexity. In order to solve these problems, this paper proposes a convolutional neural network-based extraction technique for human physiological signal features and uses an MPL classifier to detect whether the ECG signal is normal or not, taking the ECG signal as an example. In this paper, the signal preprocessing method based on wavelet transform and morphological filtering is adopted, and the high-frequency signal is removed by wavelet transform, and the low-frequency signal is removed by morphological filtering. A wide range of tests on ECG signals obtained from the MIT-BIH-AR databank and INCART database showed that the method has good detection performance with sensitivity Sen = 99.54%, positive prediction rate PPR = 99.65%, detecting mistake ratio DER = 0.35% and precision Acc = 99.55%, which is an improved performance compared to other techniques, proving the superiority of the present technique.
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Thammasan, Nattapong, Ivo V. Stuldreher, Elisabeth Schreuders, Matteo Giletta, and Anne-Marie Brouwer. "A Usability Study of Physiological Measurement in School Using Wearable Sensors." Sensors 20, no. 18 (September 20, 2020): 5380. http://dx.doi.org/10.3390/s20185380.
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Measuring psychophysiological signals of adolescents using unobtrusive wearable sensors may contribute to understanding the development of emotional disorders. This study investigated the feasibility of measuring high quality physiological data and examined the validity of signal processing in a school setting. Among 86 adolescents, a total of more than 410 h of electrodermal activity (EDA) data were recorded using a wrist-worn sensor with gelled electrodes and over 370 h of heart rate data were recorded using a chest-strap sensor. The results support the feasibility of monitoring physiological signals at school. We describe specific challenges and provide recommendations for signal analysis, including dealing with invalid signals due to loose sensors, and quantization noise that can be caused by limitations in analog-to-digital conversion in wearable devices and be mistaken as physiological responses. Importantly, our results show that using toolboxes for automatic signal preprocessing, decomposition, and artifact detection with default parameters while neglecting differences between devices and measurement contexts yield misleading results. Time courses of students’ physiological signals throughout the course of a class were found to be clearer after applying our proposed preprocessing steps.
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Malakhov, D. G., V. A. Orlov, S. I. Kartashov, L. I. Skiteva, M. V. Kovalchuk, Y. I. Alexandrov, and Y. I. Kholodny. "Optimization of Signal Processing Parameters in Psychophysiological Studies on the Example of GSR and PPG." Experimental Psychology (Russia) 16, no. 1 (April 21, 2023): 62–86. http://dx.doi.org/10.17759/exppsy.2023160104.
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<p>When analyzing physiological signals, the problem of setting data processing parameters arises due to the blurring of the boundary between signal and noise properties, as well as the fundamental lack of objective criteria for the quality of data processing in psychophysiology. This paper describes an approach to optimizing processing parameters on the example of galvanic skin response (GSR) and photoplethysmogram (PPG), based on the use of stimuli that are significant for a person, selected on the basis of biographical data, which can be considered as criteria validation. As a metric for the optimization, we used the frequency of coincidence of the stimuli identified as a result of the analysis with the a priori given ones (human names, including the name of the volunteer, and also visit cards selected by the volunteer). GSR and PPG signals were recorded using an MRI-compatible polygraph under conditions of functional magnetic resonance imaging (N=46 volunteers). In the first part of the work, optimization of frequency filters and analysis intervals (epochs) was performed. It has been established that the following processing parameters are optimal for analyzing the amplitude properties of the GSR signal: first-order Butterworth filters, frequency range is 0.025-0.25 Hz, interval of analysisis1-7 s from a stimulus. To analyze the PPG signal using the length of the curve, the following processing parameters are optimal: second-order Butterworth filters, frequency range is 1.25—12.5 Hz, interval of analysis is 3—10 s from a stimulus. Using the same criterion, several alternative signal processing methods were tested: change in the amplitude of the GSR signal over the analysis interval compared to the classical method by the amplitude maximum relative to the baseline; several types of ranking of reactions within a block of stimuli compared to simple averaging of all responses. The parameters and methods of processing of the GSR and PPG signals obtained in the work demonstrate universality in relation to the variety of initial data and could be applicable in applied and fundamental research. The general approach described in the work can also be used to optimize the processing parameters of other physiological signals including fMRI.</p>
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Gordillo-Roblero, Luis Alberto, Jorge Alberto Soto-Cajiga, Carlos Romo-Fuentes, Luis Felipe Martínez-Soto, and Noé Amir Rodríguez-Olivares. "A Methodology for the Design of a Compliant Electrocardiograph: A Case Study." Electronics 13, no. 21 (October 29, 2024): 4238. http://dx.doi.org/10.3390/electronics13214238.
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This document presents the methodology for designing an electrocardiograph capable of acquiring IEC 60601-2-25-compliant signals. The objective of developing this methodology is to address a signal incompatibility problem that has existed in academia for years, specifically in physiological processing research. This problem is related to the signal’s sampling rate and/or noise levels, and it becomes evident when one signal processing method is intended to work with another, either as a subsequent or simultaneous process. Even though matching algorithms can be implemented to remedy this incompatibility problem, the ultimate solution is the standardization of signals, which depends exclusively on the standardization of hardware. The signal incompatibility problem is urgent to solve because it makes the integration and scalability of different academic works difficult, preventing academia from reaching the stage of development that commercial equipment displays in automatic interpretation procedures. The design methodology presented in this document addresses the stated problem by creating an open-source hardware device capable of acquiring compliant signals, with careful consideration given to Signal Integrity and EMC concepts—a methodology that can be extended to other physiological acquisition systems. The expedited availability of the device’s design documentation and fabrication files is also an advantage of this work.
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Jegan R. and Nimi W.S. "Sensor Based Smart Real Time Monitoring of Patients Conditions Using Wireless Protocol." International Journal of E-Health and Medical Communications 9, no. 3 (July 2018): 79–99. http://dx.doi.org/10.4018/ijehmc.2018070105.
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This article describes how physiological signal monitoring plays an important role in identifying the health condition of heart. In recent years, online monitoring and processing of biomedical signals play a major role in accurate clinical diagnosis. Therefore, there is a requirement for the developing of online monitoring systems that will be helpful for physicians to avoid mistakes. This article focuses on the method for real time acquisition of an ECG and PPG signal and it's processing and monitoring for tele-health applications. This article also presents the real time peak detection of ECG and PPG for vital parameters measurement. The implementation and design of the proposed wireless monitoring system can be done using a graphical programming environment that utilizes less power and a minimized area with reasonable speed. The advantages of the proposed work are very simple, low cost, easy integration with programming environment and continuous monitoring of physiological signals.
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Xie, Liping, Xingyu Zi, Qingshi Meng, Zhiwen Liu, and Lisheng Xu. "Detection of Physiological Signals Based on Graphene Using a Simple and Low-Cost Method." Sensors 19, no. 7 (April 6, 2019): 1656. http://dx.doi.org/10.3390/s19071656.
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Despite that graphene has been extensively used in flexible wearable sensors, it remains an unmet need to fabricate a graphene-based sensor by a simple and low-cost method. Here, graphene nanoplatelets (GNPs) are prepared by thermal expansion method, and a sensor is fabricated by sealing of a graphene sheet with polyurethane (PU) medical film. Compared with other graphene-based sensors, it greatly simplifies the fabrication process and enables the effective measurement of signals. The resistance of graphene sheet changes linearly with the deformation of the graphene sensor, which lays a solid foundation for the detection of physiological signals. A signal processing circuit is developed to output the physiological signals in the form of electrical signals. The sensor was used to measure finger bending motion signals, respiration signals and pulse wave signals. All the results demonstrate that the graphene sensor fabricated by the simple and low-cost method is a promising platform for physiological signal measurement.
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Ingle, Rahul, and R. N. Awale. "Impact Analysis of Meditation on Physiological Signals." JOIV : International Journal on Informatics Visualization 2, no. 1 (January 5, 2018): 31. http://dx.doi.org/10.30630/joiv.2.1.98.
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Vipassana meditation is a type of mindfulness meditation technique mostly practices in southwest part of the globe, where relaxing but highly awake and alert mind state is achieved. Vipassana Meditation involvement was carried out for a group of mid-aged people. These people constantly dealing with high level of stress. This research evaluates advance signal processing methodologies of respiration and electroencephalographic (EEG) signals during Vipassana meditation and control condition to assist in quantification of the meditative state. EEG of respiration and Vipassana Meditation data were collected and analyzed on 40 novice meditators after a 3-weeks meditation intervention. Collected data were analyzed with an advanced mathematical tool such as Wavelet Transform for spectral analysis. The Support Vector Machine is used as a classifier for classification of EEG signals to evaluate an objective marker for meditation. We analyzed and observed Vipassana meditation and control condition differences in the different frequency bands such as (alpha, beta, theta, delta, and gamma) for EEG signals of subjects. Moreover, we confirmed a classifier with a higher accuracy (92%) during respiration and EEG signals for discrimination between meditation and control conditions, rather than EEG signal alone (85%). A classifier based on respiration and EEG signal is the feasible objective marker for verifying the ability of meditation. Different level of meditation depth and experience can be studied using this classifier for future studies. The main objective of this work is to develop a physiological meditation marker as a medication (mind-body medicine field) to advance by nourishing severity of methods.
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Azudin, Khalida, Kok Beng Gan, Rosmina Jaafar, and Mohd Hasni Ja’afar. "The Principles of Hearable Photoplethysmography Analysis and Applications in Physiological Monitoring–A Review." Sensors 23, no. 14 (July 18, 2023): 6484. http://dx.doi.org/10.3390/s23146484.
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Not long ago, hearables paved the way for biosensing, fitness, and healthcare monitoring. Smart earbuds today are not only producing sound but also monitoring vital signs. Reliable determination of cardiovascular and pulmonary system information can explore the use of hearables for physiological monitoring. Recent research shows that photoplethysmography (PPG) signals not only contain details on oxygen saturation level (SPO2) but also carry more physiological information including pulse rate, respiration rate, blood pressure, and arterial-related information. The analysis of the PPG signal from the ear has proven to be reliable and accurate in the research setting. (1) Background: The present integrative review explores the existing literature on an in-ear PPG signal and its application. This review aims to identify the current technology and usage of in-ear PPG and existing evidence on in-ear PPG in physiological monitoring. This review also analyzes in-ear (PPG) measurement configuration and principle, waveform characteristics, processing technology, and feature extraction characteristics. (2) Methods: We performed a comprehensive search to discover relevant in-ear PPG articles published until December 2022. The following electronic databases: Institute of Electrical and Electronics Engineers (IEEE), ScienceDirect, Scopus, Web of Science, and PubMed were utilized to conduct the studies addressing the evidence of in-ear PPG in physiological monitoring. (3) Results: Fourteen studies were identified but nine studies were finalized. Eight studies were on different principles and configurations of hearable PPG, and eight studies were on processing technology and feature extraction and its evidence in in-ear physiological monitoring. We also highlighted the limitations and challenges of using in-ear PPG in physiological monitoring. (4) Conclusions: The available evidence has revealed the future of in-ear PPG in physiological monitoring. We have also analyzed the potential limitation and challenges that in-ear PPG will face in processing the signal.
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Balaji, M. Sundar Prakash, R. Jayabharathy, Betty Martin, A. Parvathy, R. K. Arvind Shriram, and V. Elamaran. "Exploring Modern Digital Signal Processing Techniques on Physiological Signals in Day-to-Day Life Applications." Journal of Medical Imaging and Health Informatics 10, no. 1 (January 1, 2020): 93–98. http://dx.doi.org/10.1166/jmihi.2020.2841.
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Coatrieux, Jean-Louis. "Signal Processing and Physiological Modeling-Part II: Depth Model-Driven Analysis." Critical Reviews in Biomedical Engineering 30, no. 1-3 (2002): 37–54. http://dx.doi.org/10.1615/critrevbiomedeng.v30.i123.30.
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Cowan, D. M., E. R. I. Deane, T. M. Robinson, J. W. Lee, and V. C. Roberts. "A transputer-based physiological signal processing system. Part 1—System design." Medical Engineering & Physics 17, no. 6 (September 1995): 403–9. http://dx.doi.org/10.1016/1350-4533(94)00004-s.
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Yauri, Ricardo, Antero Castro, Rafael Espino, and Segundo Gamarra. "Implementation of a sensor node for monitoring and classification of physiological signals in an edge computing system." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 1 (October 1, 2022): 98. http://dx.doi.org/10.11591/ijeecs.v28.i1.pp98-105.
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We describe the design and development of sensor nodes, based on Edge computing technologies, for the processing and classification of events detected in physiological signals such as the electrocardiographic signal (ECG is the electrical signal of the heart), temperature, heart rate, and human movement. The edge device uses a 32-bit Tensilica microcontroller-based module with the ability to transmit data wirelessly using Wi-Fi. In addition, algorithms for classification and detection of movement patterns were implemented to be implemented in devices with limited resources and not only in high-performance computers. The Internet of Things and its application in smart environments can help non-intrusive monitoring of daily activities by implementing support vector machine (SVM is a machine learning algorithm) for implementation in embedded systems with low hardware resources. This paper shows experimental results obtained during the acquisition, transmission, and processing of physiological signals in a edge computing system and their visualization in a web application.
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Yu, Xilin, Zhenning Mei, Chen Chen, and Wei Chen. "Ranking Power Spectra: A Proof of Concept." Entropy 21, no. 11 (October 29, 2019): 1057. http://dx.doi.org/10.3390/e21111057.
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To characterize the irregularity of the spectrum of a signal, spectral entropy and its variants are widely adopted measures. However, spectral entropy is invariant under the permutation of the power spectrum estimations on a predefined grid. This erases the inherent order structure in the spectrum. To disentangle the order structure and extract meaningful information from raw digital signal, a novel analysis method is necessary. In this paper, we tried to unfold this order structure by defining descriptors mapping real- and vector-valued power spectrum estimation of a signal into a scalar value. The proposed descriptors showed its potential in diverse problems. Significant differences were observed from brain signals and surface electromyography of different pathological/physiological states. Drastic change accompanied by the alteration of the underlying process of signals enables it as a candidate feature for seizure detection and endpoint detection in speech signal. Since the order structure in the spectrum of physiological signal carries previously ignored information, which cannot be properly extracted by existing techniques, this paper takes one step forward along this direction by proposing computationally efficient descriptors with guaranteed information gain. To the best of our knowledge, this is the first work revealing the effectiveness of the order structure in the spectrum in physiological signal processing.
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R, Praveena, Ravish D K, T. R. Ganesh Babu, and Preetha J. "DESIGN AND DEVELOPMENT OF VIBROARTHOGRAM SCREENING DEVICE AND ASSESSMENT OF JOINT MOTION IN THE PURSUIT OF SIGNAL PROCESSING." ICTACT Journal on Image and Video Processing 11, no. 4 (May 1, 2021): 2453–59. http://dx.doi.org/10.21917/ijivp.2021.0349.
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Abnormal conditions in the knee joint are factors to lead changes in the vibroarthro graphic signal which represents the sound or vibration emitted from the joint during flexion or extension with suitable instrumentation these signals are to be acquired and also converted into digital signal. Signals are amplitude limited, distorted limited length and non-stationary in nature. The vibroarthro graphic system is unknown, modeling of vibroarthro graphic signal are essential to explore physiological behavior. Biosignal Processing and Pattern classification techniques have been applied to vibroarthro graphic signals to derive features that characterize the state of articular cartilage surface and assist in non-invasive detection of knee joint pathology. Screening of knee joint abnormal condition using vibroarthro graphic signals could reduce the need for diagnostic surgery. Diagnostic surgeries are invasive techniques and could deteriorate joints as well. In the first part of the work suitable instrumentation setup is designed and developed. Subsequent second part of work is extended to model vibroarthro graphic signal and algorithms are used to assess joint motion in the pursuit of signal processing.
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Akishin, A. D., A. P. Nikolaev, and A. V. Pisareva. "PPG System Development for the Organism Physiological Parameters Monitoring with Artificial Intelligence Technologies." Journal of Physics: Conference Series 2096, no. 1 (November 1, 2021): 012187. http://dx.doi.org/10.1088/1742-6596/2096/1/012187.
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Abstract Monitoring such health parameters as cardiac rate (CR), respiration rate (RR), blood pressure (BP), degree of oxygen in blood (SpO2), body temperature and other requires careful approach to design and development of medical devices. New non-invasive methods introduced in measuring human physiological parameters based on photoplethysmography (PPG) demonstrated their significant potential in monitoring the state of an organism, but their use in wearable devices is largely hampered by exposure to motion artifacts. This article presents a device for photoplethysmographic studies using various adaptive algorithms for processing the registered signals. The work uses artificial intelligence technologies to monitor the heart rate exposed to external mechanical and electrical interference worsening accuracy characteristics of the system. Besides, system architecture was developed, and a device model was manufactured, which made it possible to measure the optimal algorithm for digital signal processing. When using the PPG system, methods of adaptive signal processing based on Wiener filters, filters on the method of least squares (MLS) and Kalman filtering were used. To ensure heart rate monitoring with the given accuracy, studies were performed with participation of volunteers, and analysis was carried out of the results of various signal processing algorithms operation. In the course of experimental studies, a method was proposed to estimate the heart rate calculation accuracy and to analyze the external noise filtering efficiency by adaptive algorithms. PPG designed and developed system made it possible to monitor the heart rate with the given accuracy, control the organism current state and could be used as a means of cardiovascular disease diagnostics.
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Meng, Jingfei, Weiming Cai, Siyi Ou, Jian Zhao, Shengli Fan, and Bicong Zheng. "Research on the Signal Noise Reduction Method of Fish Electrophysiological Behavior Based on CEEMDAN with Improved Wavelet Thresholding." Electronics 12, no. 23 (December 1, 2023): 4861. http://dx.doi.org/10.3390/electronics12234861.
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Electrophysiological signals are one of the key ways that fish convey information and govern movement. Changes in physiological electrical signals may indirectly reflect changes in fish sensory thresholds and locomotor behavior. The acquisition of physiological electrical signals in fish is more susceptible than in mammals to the effects of surface mucus and water noise, thereby reducing signal quality. In this study, a noise reduction method for electrophysiological behavioral signals in fish was proposed, namely the decomposition of the original EMG signal into multiple intrinsic mode components using CEEMDAN. To choose the signal-dominated IMF, noise-dominated IMF, and pure IMF, mutual correlation function characteristic analysis is done on each IMF and the original signal. The signal-dominated IMF is then filtered using the improved wavelet thresholding approach. Finally, the wavelet threshold filtered signal-dominated IMF with pure IMF was reconstructed into the processed fish EMG signal. It is demonstrated that the algorithm proposed in this paper improves the SNR by 3.1977 dB and reduces the RMSE by 0.0235 when compared to the traditional wavelet threshold denoising. The denoising method proposed in this paper can effectively improve the signal quality and provides an effective tool for the in-depth analysis of fish behavior from the perspective of physiological electrical signals.
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Lu, Zhihai, Zhaoxiang Li, and Lei Zhang. "Physiological Index Monitoring of Wearable Sports Training Based on a Wireless Sensor Network." Journal of Sensors 2021 (December 6, 2021): 1–10. http://dx.doi.org/10.1155/2021/7552510.
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According to the development needs of wireless sensor networks, this paper uses the combination of embedded system and wireless sensor network technology to design a network node platform. This platform is equipped with a sports training sensor module to measure the physiological indicators of the ward in real time. The network node sends the collected physiological parameters to a remote monitoring center in real time. First, according to the generation mechanism of the physiological index signal and the characteristics of the physiological index signal, the wireless sensor network analysis and processing method are used to denoise the physiological index signal, and the wireless sensor network package is used to extract the characteristics of the physiological index, indicating different types of respiration. The energy characteristics of the sound physiological index signals are different, which verifies the feasibility of the independent component analysis method for separating the physiological index and the physiological index signal of the heart sound. Secondly, the hardware system of physiological index signal acquisition is designed, and the selection principle of the hardware unit is introduced. At the same time, the system structure of the monitor is designed, and then, the wireless sensor network sensor node is researched, the hardware of the wearable monitor system is designed, and the hardware architecture and working mode based on the single-chip MSP430F149 are given. Finally, the wireless hardware platform includes the following main modules: sensor part, preprocessing circuit module, microprocessing module based on MSP430 low power consumption, wireless transceiver module based on RF chip CC2420, and power supply unit used to provide energy.
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Zhou, Weiheng. "Comparison and analysis of different ECG denoising methods." Journal of Physics: Conference Series 2634, no. 1 (November 1, 2023): 012045. http://dx.doi.org/10.1088/1742-6596/2634/1/012045.
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Abstract With the improvement of medical level, electrocardiogram (ECG) is widely used for disease diagnosis. A lot of pathological and physiological information is contained in the ECG, which can be used to record the point activity of normal human heart and diagnose various heart disease. However, the acquired ECG signals are always contaminated with noise which caused by acquisition equipment or other circumstance. Therefore, Efficient denoising method is very important. In this paper, three typical ECG signal denoising methods are listed, including FIR filtering, wavelet filtering and EMD filtering. In this paper, the principles of the three filtering methods are introduced in detail, and their effects are compared. By comparison, it intuitively shows the processing effects of each method on ECG signals. Meanwhile, a simple Butterworth filter is designed to denoise a standard wave, which represents the logic knowledge related to denoising. It is very significant for the medical signal processing field and help to research more effective signal processing methods.
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Guo, Benyuan. "Identification and processing of in-ear acoustic signals." Theoretical and Natural Science 18, no. 1 (December 8, 2023): 275–80. http://dx.doi.org/10.54254/2753-8818/18/20230438.
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Hearing is the sound depiction of the world for human beings. The ear, or in other world, the peripheral organ of hearing, is an important physiological structure and basis to produce hearing. In order to learn more about hearing organs, the first thing required to know is how hearing is formed, as well as how sound is perceived by us. For this purpose, this paper will examine the matter of identification and processing of in-ear acoustic signals. The main content of this paper are principles of sound, hearing, and signal encoding, as well as their usage in a model which describes the neuron system in cochlear nucleus. In addition, this paper puts forward an application of in-ear signal identifying in hearing aids and cochlear implants by using the convolution signal encoding mode to improve the sound recognition function of these devices and further improve the lives of the hearing impaired. In the end, basic conceptions and related knowledge are organized in this paper. The meanings and problems of this paper are also mentioned in the last section.
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Li, Xiao, Yujing Shang, Jiaqi Wei, and Yiheng Zhou. "Research on electronic stethoscope system and signal processing algorithm." Journal of Physics: Conference Series 2634, no. 1 (November 1, 2023): 012037. http://dx.doi.org/10.1088/1742-6596/2634/1/012037.
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Abstract Stethoscopes have an important role in non-invasive diagnosis of cardiovascular and respiratory diseases, digestive diseases, and other kinds of diseases. The emergence of high-end diagnostic devices and new diagnostic methods have caused the status of the stethoscope to decline. However, stethoscope has the advantages of simple operation, mature auscultation theory and low cost, and thus is still widely used in medical diagnosis. This paper first introduces the design and application of electronic stethoscope solutions based on contact sensors and air coupling sensors, and then introduces advanced algorithms for digital signal processing for the diagnosis and treatment of different diseases, including heart sound noise reduction algorithm, heart sound segmentation algorithm and heart sound feature extraction and recognition algorithm. Finally, this paper summarizes the application of the electronic stethoscope system in medical testing, and its future development direction. In summary, the electronic stethoscope system is a reliable medical testing tool, which can convert sound signals into digital signals through complex signal processing algorithms for more accurate detection of human physiological parameters. The research of this paper will be of great value to the research and application of electronic stethoscopes.
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Alge, Olivia P., Jonathan Gryak, J. Scott VanEpps, and Kayvan Najarian. "Sepsis Trajectory Prediction Using Privileged Information and Continuous Physiological Signals." Diagnostics 14, no. 3 (January 23, 2024): 234. http://dx.doi.org/10.3390/diagnostics14030234.
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The aim of this research is to apply the learning using privileged information paradigm to sepsis prognosis. We used signal processing of electrocardiogram and electronic health record data to construct support vector machines with and without privileged information to predict an increase in a given patient’s quick-Sequential Organ Failure Assessment score, using a retrospective dataset. We applied this to both a small, critically ill cohort and a broader cohort of patients in the intensive care unit. Within the smaller cohort, privileged information proved helpful in a signal-informed model, and across both cohorts, electrocardiogram data proved to be informative to creating the prediction. Although learning using privileged information did not significantly improve results in this study, it is a paradigm worth studying further in the context of using signal processing for sepsis prognosis.
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LEE, BARRY B. "Neural models and physiological reality." Visual Neuroscience 25, no. 3 (March 6, 2008): 231–41. http://dx.doi.org/10.1017/s0952523808080140.
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Neural models of retinal processing provide an important tool for analyzing retinal signals and their functional significance. However, it is here argued that in biological reality, retinal connectivity is unlikely to be as specific as ideal neural models might suggest. The retina is thought to provide functionally specific signals, but this specificity is unlikely to be anatomically complete. This is illustrated by examples of cone connectivity to macaque ganglion cells. For example, cells of the magnocellular pathway appear to avoid short-wavelength cone input, so that such input is negligible under normal conditions. However, there is anatomical, physiological, and psychophysical evidence that under special conditions, weak input may be revealed. Second, ideal models of how retinal information is centrally utilized have to take into account the biological reality of retinal signals. The stochastic nature of impulse trains modifies signal-to-noise ratio in unexpected ways. Also, non-linearities in cell responses make, for example, multiplexing of luminance and chromatic signals in the parvocellular pathway impracticable. The purpose of this analysis is to show than ideal neural models must confront an often more complex and nuanced physiological reality.
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Zhang, Chaohong, Xingguang Geng, Fei Yao, Liyuan Liu, Ziyang Guo, Yitao Zhang, and Yunfeng Wang. "The Ultrasound Signal Processing Based on High-Performance CORDIC Algorithm and Radial Artery Imaging Implementation." Applied Sciences 13, no. 9 (May 4, 2023): 5664. http://dx.doi.org/10.3390/app13095664.
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The radial artery reflects the largest amount of physiological and pathological information about the human body. However, ultrasound signal processing involves a large number of complex functions, and traditional digital signal processing can hardly meet the requirements of real-time processing of ultrasound data. The research aims to improve computational accuracy and reduce the hardware complexity of ultrasound signal processing systems. Firstly, this paper proposes to apply the coordinate rotation digital computer (CORDIC) algorithm to the whole radial artery ultrasound signal processing, combines the signal processing characteristics of each sub-module, and designs the dynamic filtering module based on the radix-4 CORDIC algorithm, the quadrature demodulation module based on the partitioned-hybrid CORDIC algorithm, and the dynamic range transformation module based on the improved scale-free CORDIC algorithm. A digital radial artery ultrasound imaging system was then built to verify the accuracy of the three sub-modules. The simulation results show that the use of the high-performance CORDIC algorithm can improve the accuracy of data processing. This provides a new idea for the real-time processing of ultrasound signals. Finally, radial artery ultrasound data were collected from 20 volunteers using different probe scanning modes at three reference positions. The vessel diameter measurements were averaged to verify the reliability of the CORDIC algorithm for radial artery ultrasound imaging, which has practical application value for computer-aided clinical diagnosis.
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Lin, Jzau Sgeng, and Sun Ming Huang. "An FPGA-Based Brain-Computer Interface for Wireless Electric Wheelchairs." Applied Mechanics and Materials 284-287 (January 2013): 1616–21. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.1616.
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A wireless EEG-based brain-computer interface (BCI) and an FPGA-based system to control electric wheelchairs through a Bluetooth interface was proposed in this paper for paralyzed patients. Paralytic patients can not move freely and only use wheelchairs in their daily life. Especially, people getting motor neuron disease (MND) can only use their eyes and brain to exercise their willpower. Therefore, real-time EEG and winking signals can help these patients effectively. However, current BCI systems are usually complex and have to send the brain waves to a personal computer or a single-chip microcontroller to process the EEG signals. In this paper, a simple BCI system with two channels and an FPGA-based circuit for controlling DC motor can help paralytic patients easily to drive the electric wheelchair. The proposed BCI system consists of a wireless physiological with two-channel acquisition module and an FPGA-based signal processing unit. Here, the physiological signal acquisition module and signal processing unit were designed for extracting EEG and winking signals from brain waves which can directly transformed into control signals to drive the electric wheelchairs. The advantages of the proposed BCI system are low power consumption and compact size so that the system can be suitable for the paralytic patients. The experimental results showed feasible action for the proposed BCI system and drive circuit with a practical operating in electric wheelchair applications.
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Sevil, Mert, Mudassir Rashid, Mohammad Reza Askari, Zacharie Maloney, Iman Hajizadeh, and Ali Cinar. "Detection and Characterization of Physical Activity and Psychological Stress from Wristband Data." Signals 1, no. 2 (December 4, 2020): 188–208. http://dx.doi.org/10.3390/signals1020011.
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Wearable devices continuously measure multiple physiological variables to inform users of health and behavior indicators. The computed health indicators must rely on informative signals obtained by processing the raw physiological variables with powerful noise- and artifacts-filtering algorithms. In this study, we aimed to elucidate the effects of signal processing techniques on the accuracy of detecting and discriminating physical activity (PA) and acute psychological stress (APS) using physiological measurements (blood volume pulse, heart rate, skin temperature, galvanic skin response, and accelerometer) collected from a wristband. Data from 207 experiments involving 24 subjects were used to develop signal processing, feature extraction, and machine learning (ML) algorithms that can detect and discriminate PA and APS when they occur individually or concurrently, classify different types of PA and APS, and estimate energy expenditure (EE). Training data were used to generate feature variables from the physiological variables and develop ML models (naïve Bayes, decision tree, k-nearest neighbor, linear discriminant, ensemble learning, and support vector machine). Results from an independent labeled testing data set demonstrate that PA was detected and classified with an accuracy of 99.3%, and APS was detected and classified with an accuracy of 92.7%, whereas the simultaneous occurrences of both PA and APS were detected and classified with an accuracy of 89.9% (relative to actual class labels), and EE was estimated with a low mean absolute error of 0.02 metabolic equivalent of task (MET).The data filtering and adaptive noise cancellation techniques used to mitigate the effects of noise and artifacts on the classification results increased the detection and discrimination accuracy by 0.7% and 3.0% for PA and APS, respectively, and by 18% for EE estimation. The results demonstrate the physiological measurements from wristband devices are susceptible to noise and artifacts, and elucidate the effects of signal processing and feature extraction on the accuracy of detection, classification, and estimation of PA and APS.
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Pudipeddi, Srinidhi, Shriya Aishani Rachakonda, and T. S. Shiny Angel. "PulseVision: A Real-Time Heart-Rate Mornitoring System Using Computer Vision and Signal Processing Techniques." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 008 (August 23, 2024): 1–16. http://dx.doi.org/10.55041/ijsrem37151.
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In the dynamic landscape of technology and healthcare, the quest for real-time physiological monitoring solutions has sparked innovation. This study proposes a method that leverages computer vision and signal processing to create a cutting-edge system for monitoring heart rates in real-time. The script employs OpenCV for face detection and incorporates custom modules for real-time plotting, offering a comprehensive and instantaneous assessment of cardiovascular activity. The Python script unveils a real-time heart rate monitoring system that harnesses the synergy of computer vision and signal processing. Utilizing OpenCV for precise face detection and custom modules for dynamic plotting, the script processes video frames from a webcam to analyze the facial region for heart rate monitoring. Employing color magnification, Gaussian pyramid construction, and bandpass filtering, the script extracts the pulse signal's frequency content. Heart rate, calculated in beats per minute (BPM), provides a valuable metric for physiological assessment. This system, with its innovative approach, has the potential to redefine real- time physiological monitoring applications, offering insights for healthcare and personal well-being. Key Words: Heart Rate Monitoring, Computer Vision, Signal Processing, OpenCV, Real-time Plotting, Face Detection, Frequency Analysis, Pulse Signal, Gaussian Pyramid, Bandpass Filtering.
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Disney, Anita A., and Simon R. Schultz. "Hallucinations and acetylcholine: Signal or noise?" Behavioral and Brain Sciences 27, no. 6 (December 2004): 790–91. http://dx.doi.org/10.1017/s0140525x0425018x.
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The cholinergic system is a good candidate for the role of determining the relative weight given in cortical information processing to new sensory information versus prior knowledge. We discuss the physiological data supporting this, and suggest that this Bayesian perspective can easily be reconciled with the dynamical framework proposed by Behrendt & Young (B&Y).
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Postolache, Octavian A., Pedro M. B. Silva Girao, Joaquim Mendes, Eduardo C. Pinheiro, and Gabriela Postolache. "Physiological Parameters Measurement Based on Wheelchair Embedded Sensors and Advanced Signal Processing." IEEE Transactions on Instrumentation and Measurement 59, no. 10 (October 2010): 2564–74. http://dx.doi.org/10.1109/tim.2010.2057590.
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