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Relevant bibliographies by topics / EMG signal

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Academic literature on the topic 'EMG signal'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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Journal articles on the topic "EMG signal"

1

Oo, Thandar, and Pornchai Phukpattaranont. "Signal-to-Noise Ratio Estimation in Electromyography Signals Contaminated with Electrocardiography Signals." Fluctuation and Noise Letters 19, no. 03 (2020): 2050027. http://dx.doi.org/10.1142/s0219477520500273.

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When electromyography (EMG) signals are collected from muscles in the torso, they can be perturbed by the electrocardiography (ECG) signals from heart activity. In this paper, we present a novel signal-to-noise ratio (SNR) estimate for an EMG signal contaminated by an ECG signal. We use six features that are popular in assessing EMG signals, namely skewness, kurtosis, mean average value, waveform length, zero crossing and mean frequency. The features were calculated from the raw EMG signals and the detail coefficients of the discrete stationary wavelet transform. Then, these features are used

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Huang, Jian-Jia, Chung-Yu Chang, Jen-Kuang Lee, and Hen-Wai Tsao. "RESOLVING SINGLE-LEAD ECG FROM EMG INTERFERENCE IN HOLTER RECORDING BASED ON EEMD." Biomedical Engineering: Applications, Basis and Communications 26, no. 01 (2014): 1450008. http://dx.doi.org/10.4015/s1016237214500082.

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The aim of this study was to propose an electrocardiogram (ECG) de-noising framework based on ensemble empirical mode decomposition (EEMD) to eliminate electromyography (EMG) interference without signal distortion. ECG signals are easily corrupted by EMG, especially in Holter monitor recordings. The frequency component overlapping between EMG and ECG is a challenge in signal processing that remains to be solved. The aim of the present study, therefore, was to resolve ECG signals from recorded segments with EMG noise. Two units were put into our proposed framework; first, modified moving averag

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J, Jasper Gnana Chandran, Shanmathi M, Jerald Jeba Kumar S, and Ambigaipriya S. "NOVEL MACHINE LEARNING FILTER PROTOTYPING FOR ECG/EEG/EMG SIGNALS." ICTACT Journal on Microelectronics 9, no. 1 (2023): 1481–86. https://doi.org/10.21917/ijme.2023.0256.

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The ECG/EEG/EMG monitoring system is a new type of medical technology that has emerged because of the convergence of mobile technology and the increased demand for healthcare management caused by an ageing population. The ECG/EEG/EMG signal detecting system makes it possible to carry out a dynamic medical diagnosis in a manner that is both quicker and accurate by giving accurate ECG/EEG/EMG signals throughout a varied range of physical activities. This study covers the installation of a prototype biomedical measurement system, which can be used to pedagogically evaluate the usefulness of speci

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Chang, Kang-Ming, Peng-Ta Liu, and Ta-Sen Wei. "Electromyography Parameter Variations with Electrocardiography Noise." Sensors 22, no. 16 (2022): 5948. http://dx.doi.org/10.3390/s22165948.

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Electromyograms (EMG signals) may be contaminated by electrocardiographic (ECG) signals that cannot be easily separated with traditional filters, because both signals have some overlapping spectral components. Therefore, the first challenge encountered in signal processing is to extract the ECG noise from the EMG signal. In this study, the EMG, mixed with different degrees of noise (ECG), is simulated to investigate the variations of the EMG features. Simulated data were derived from the MIT-BIH Noise Stress Test (NSTD) Database. Two EMG and four ECG data were composed with four EMG/ECG SNR to

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L N, Dayananda. "Portable Non – Invasive Device for ECG and EMG Monitoring." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48910.

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Abstract— This project proposes the design and implementation of a portable, non-invasive ECG and EMG monitoring system using the ESP32-S3 microcontroller. The system is capable of capturing and displaying real-time bioelectrical signals from the human body using two dedicated sensors—an AD8232 module for electrocardiogram (ECG) signal acquisition and a modular EMG sensor for muscle activity monitoring. The ESP32-S3’s built-in ADC is used to digitize the signals, which are then processed and displayed as scrolling waveforms on a 320×240 SPI TFT display. A push-button interface is incorporated

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Neto, Osmar Pinto, and Evangelos A. Christou. "Rectification of the EMG Signal Impairs the Identification of Oscillatory Input to the Muscle." Journal of Neurophysiology 103, no. 2 (2010): 1093–103. http://dx.doi.org/10.1152/jn.00792.2009.

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Rectification of EMG signals is a common processing step used when performing electroencephalographic–electromyographic (EEG–EMG) coherence and EMG–EMG coherence. It is well known, however, that EMG rectification alters the power spectrum of the recorded EMG signal (interference EMG). The purpose of this study was to determine whether rectification of the EMG signal influences the capability of capturing the oscillatory input to a single EMG signal and the common oscillations between two EMG signals. Several EMG signals were reconstructed from experimentally recorded EMG signals from the surfa

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Strzecha, Krzysztof, Marek Krakós, Bogusław Więcek, et al. "Processing of EMG Signals with High Impact of Power Line and Cardiac Interferences." Applied Sciences 11, no. 10 (2021): 4625. http://dx.doi.org/10.3390/app11104625.

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This work deals with electromyography (EMG) signal processing for the diagnosis and therapy of different muscles. Because the correct muscle activity measurement of strongly noised EMG signals is the major hurdle in medical applications, a raw measured EMG signal should be cleaned of different factors like power network interference and ECG heartbeat. Unfortunately, there are no completed studies showing full multistage signal processing of EMG recordings. In this article, the authors propose an original algorithm to perform muscle activity measurements based on raw measurements. The effective

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Debbal, S. M. "Pathological Electromyogram (EMG) Signal Analysis Parameters." Clinical Cardiology and Cardiovascular Interventions 4, no. 13 (2021): 01–14. http://dx.doi.org/10.31579/2641-0419/185.

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Clinical analysis of the electromyogram is a powerful tool for diagnosis of neuromuscular diseases. There fore, the detection and the analysis of electromyogram signals has he attracted much attention over the years. Several methods based on modern signal Processing techniques such as temporal analysis, spectro-temporel analysis ..., have been investigated for electromyogram signal treatment. However, many of these analysis methods are not highly successful due to their complexity and non-stationarity. The aim of this study is to analyse the EMGs signals using nonlinear analysis. This analysis

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Granados-Ruiz, Jackeline, David Asael Gutiérrez-Hernández, Carlos Lino-Ramírez, et al. "METHODOLOGICAL APPROACH FOR EXTRACTION OF CHARACTERISTICS OF BIOLOGICAL SIGNALS." COMPUSOFT: An International Journal of Advanced Computer Technology 08, no. 02 (2019): 3011–20. https://doi.org/10.5281/zenodo.14811307.

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Generally, signal processing is applied to a set of data that is derived from the sampling of an acquired signal. This treatment is carried out with the help of a computer that in turn executes a series of logical and mathematical operations. The treatment of signals is linked to other techniques and scientific disciplines. Some of the applications of the signal treatments may be in the form of processing of audio signals, treatment of digital images, digital communications and biological signals. In this case, the treatment was applied to biological signals such as ECG (Electrocardiogram sign

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Shahbakhti, Mohammad, Elnaz Heydari, and Gia Thien Luu. "Segmentation of ECG from Surface EMG Using DWT and EMD: A Comparison Study." Fluctuation and Noise Letters 13, no. 04 (2014): 1450030. http://dx.doi.org/10.1142/s0219477514500308.

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The electrocardiographic (ECG) signal is a major artifact during recording the surface electromyography (SEMG). Removal of this artifact is one of the important tasks before SEMG analysis for biomedical goals. In this paper, the application of discrete wavelet transform (DWT) and empirical mode decomposition (EMD) for elimination of ECG artifact from SEMG is investigated. The focus of this research is to reach the optimized number of decomposed levels using mean power frequency (MPF) by both techniques. In order to implement the proposed methods, ten simulated and three real ECG contaminated S

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Dissertations / Theses on the topic "EMG signal"

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North, Graham M. "Signal characteristics of surface EMG." Thesis, McGill University, 1989. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=55624.

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E, N. Shaban, and V. Abousetta S. "Online EMG signal analysis for Parkinson’s tremor determination." Thesis, Boston, USA, 2020. http://openarchive.nure.ua/handle/document/11838.

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In this paper, we introduced a general development of software for quantifying the severity of the Parkinson's tremor. The extracted features from the surface EMG of the wrist and hand muscles were the input of the designed model of the application. Using the application that can record reliably the EMG signals and quantify the levels of tremor may contribute to the early diagnosis of the disorder online.

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Shaban, N. E., and V. S. Abousetta. "Online EMG signal analysis for Parkinson’s tremor determination." Thesis, Boston, USA, 2020. http://openarchive.nure.ua/handle/document/13654.

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In this paper, we introduced a general development of software for quantifying the severity of the Parkinson's tremor. The extracted features from the surface EMG of the wrist and hand muscles were the input of the designed model of the application. Using the application that can record reliably the EMG signals and quantify the levels of tremor may contribute to the early diagnosis of the disorder online.

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Konté, Cheick-Suhaibou. "Modélisation de l'atténuation du signal EMG diaphragmatique de surface." Grenoble, 2010. http://www.theses.fr/2010GRENS009.

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Le recueil du signal EMG diaphragmatique par voie de surface est une mesure délicate. L'atténuation induite par les différents tissus se situant sur le trajet diaphragme-électrode et la faiblesse de l'amplitude des potentiels générés sont à l'origine d'un rapport signal sur bruit qui rend l'analyse de ce signal particulièrement difficile. Dans ce travail de thèse, nous proposons d'évaluer cette atténuation à deux niveaux d'échelles : Une première échelle appelée " grand volume " consiste à considérer la cage thoracique comme homogène et constituée de tissu pulmonaire et d'évaluer l'atténuation

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Cheng, Lui. "Improvement of signal-to-noise ratio in uterine EMG recordings." Thesis, Texas A&M University, 2003. http://hdl.handle.net/1969.1/1548.

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The objective of this study is to remove or, at least, reduce the noise in uterine EMG recordings, which at their present noise level render the data unusable. Predicting when true labor will start and recognizing when labor actually starts are important for both normal and complex pregnancies. For normal pregnancy, the prognosis of labor is important for reducing unnecessary hospital costs. About 10% of the four million babies born each year in the United States are born prematurely. At $1,500 a day for neonatal intensive care, this comprises national health care expenses of well over $5 bil

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Liu, Lukai. "A Study of Myoelectric Signal Processing." Digital WPI, 2016. https://digitalcommons.wpi.edu/etd-dissertations/34.

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This dissertation of various aspects of electromyogram (EMG: muscle electrical activity) signal processing is comprised of two projects in which I was the lead investigator and two team projects in which I participated. The first investigator-led project was a study of reconstructing continuous EMG discharge rates from neural impulses. Related methods for calculating neural firing rates in other contexts were adapted and applied to the intramuscular motor unit action potential train firing rate. Statistical results based on simulation and clinical data suggest that performances of spline-based

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Rasheed, Sarbast. "A Multiclassifier Approach to Motor Unit Potential Classification for EMG Signal Decomposition." Thesis, University of Waterloo, 2006. http://hdl.handle.net/10012/934.

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EMG signal decomposition is the process of resolving a composite EMG signal into its constituent motor unit potential trains (classes) and it can be configured as a classification problem. An EMG signal detected by the tip of an inserted needle electrode is the superposition of the individual electrical contributions of the different motor units that are active, during a muscle contraction, and background interference. <BR>This thesis addresses the process of EMG signal decomposition by developing an interactive classification system, which uses multiple classifier fusion techniqu

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Loudon, Gareth. "Advances in knowledge based signal processing : a case study in EMG decomposition." Thesis, University of Leicester, 1991. http://hdl.handle.net/2381/34799.

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This thesis relates to the use of knowledge based signal processing techniques in the decomposition of EMG signals. The aim of the research is to fully decompose EMG signals recorded at fairly high force levels (up to twenty percent maximum voluntary contraction) automatically into their constituent motor unit potentials to provide a fast and accurate analysis routine for the clinician. This requires the classification of non-overlapping motor unit action potentials (MUAPs) and superimposed waveforms formed from overlapping MUAPs in the signal. Firstly, digital filtering algorithms are used to

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Rajotte, Kiriaki J. "Electromyogram (EMG) Signal Analysis: Extraction of a Novel EMG Feature and Optimal Root Difference of Squares (RDS) Processing in Additive Noise." Digital WPI, 2019. https://digitalcommons.wpi.edu/etd-theses/1339.

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Electromyogram signals generated by human muscles can be measured on the surface of the skin and then processed for use in applications such as prostheses control, kinesiology and diagnostic medicine. Most EMG applications extract an estimate of the EMG amplitude, defined as the time-varying standard deviation of EMG, EMGσ. To improve the quality of EMGσ, additional signal processing techniques, such as whitening, noise reduction and additional signal features can be incorporated into the EMGσ processing. Implementation of these additional processing techniques improve the quality of the proce

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Ayachi, Fouaz Sofiane. "Étude du recrutement des unités motrices par analyse du signal EMG de surface." Compiègne, 2011. http://www.theses.fr/2011COMP1998.

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Le système nerveux central contrôle des mouvements par l’activation des unités de motrices (UM), les plus petites structures fonctionnelles du muscle. Les UM produisent une activité électrique qui peut être détectée par la technique de l’électromyographie de surface (EMGs). Le caractère stochastique du signal EMGs est dû principalement à la superposition des trains de potentiels d’action d’UM (TPAUM) (recrutement spatial), les TPAUM sont caractérisés par leurs instants de décharge (recrutement temporel), ainsi que par la forme des potentiels d’action (PA), qui dépend de certains facteurs métho

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Books on the topic "EMG signal"

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Genquan, Feng. EKG and EEG multiphase information analysis. American Medical Publishers, 1992.

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Sanei, Saeid, and J. A. Chambers. EEG Signal Processing. John Wiley & Sons Ltd,, 2007. http://dx.doi.org/10.1002/9780470511923.

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Sanei, Saeid. EEG signal processing. John Wiley & Sons, 2007.

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Siuly, Siuly, Yan Li, and Yanchun Zhang. EEG Signal Analysis and Classification. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47653-7.

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Gacek, Adam, and Witold Pedrycz, eds. ECG Signal Processing, Classification and Interpretation. Springer London, 2012. http://dx.doi.org/10.1007/978-0-85729-868-3.

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Hu, Li, and Zhiguo Zhang, eds. EEG Signal Processing and Feature Extraction. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9113-2.

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Chen, Chang-Wei. Multichannel ECG signal acquisition and processing. The Author], 1992.

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Jatoi, Munsif Ali, and Nidal Kamel. Brain Source Localization Using EEG Signal Analysis. CRC Press, 2017. http://dx.doi.org/10.1201/9781315156415.

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Chau, Tom. Pattern recognition of processed EMG signals for two-site myoelectric control. National Library of Canada, 1994.

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Engineers, Society of Automotive, ed. IDB-C data bus: Report on studies for a) Modeling, simulation, and signal analysis, b) EMC/EMI measurements and testing. Society of Automotive Engineers, 2002.

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Book chapters on the topic "EMG signal"

1

Rasheed, Sarbast, and Daniel Stashuk. "Pattern Classification Techniques for EMG Signal Decomposition." In Advanced Biosignal Processing. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-89506-0_13.

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Ghalyan, Ibrahim F. J., Ziyad M. Abouelenin, Gnanapoongkothai Annamalai, and Vikram Kapila. "Gaussian Smoothing Filter for Improved EMG Signal Modeling." In Signal Processing in Medicine and Biology. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-36844-9_6.

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Jauw, Veronica Lestari, and S. Parasuraman. "Investigation on Upper Limb’s Muscle Utilizing EMG Signal." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35197-6_24.

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Matsumura, Yuji, Yasue Mitsukura, Minoru Fukumi, Norio Akamatsu, and Fumiaki Takeda. "Recognition of EMG Signal Patterns by Neural Networks." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45224-9_85.

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Wadud, Abdul, and Md Imran Hossain Showrov. "EMG Signal Classification with Effective Features for Diagnosis." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51859-2_57.

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Moritani, T., D. Stegeman, and R. Merletti. "Basic Physiology and Biophysics of EMG Signal Generation." In Electromyography. John Wiley & Sons, Inc., 2005. http://dx.doi.org/10.1002/0471678384.ch1.

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Merletti, R., and H. J. Hermens. "Detection and Conditioning of the Surface EMG Signal." In Electromyography. John Wiley & Sons, Inc., 2005. http://dx.doi.org/10.1002/0471678384.ch5.

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Tengshe, Richa, Anubhav Sharma, Harshbardhan Pandey, G. S. Jayant, Laveesh Pant, and Binish Fatimah. "Automated Detection for Muscle Disease Using EMG Signal." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8563-8_16.

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Raj, Priyanshu, Binish Fatimah, and B. Sushma. "Surface EMG Signal Classification for Hand Gesture Recognition." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1520-8_10.

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Rahman, N. A. Ab, M. Mustafa, N. Sulaiman, R. Samad, and N. R. H. Abdullah. "EMG Signal Segmentation to Predict Driver’s Vigilance State." In Human-Centered Technology for a Better Tomorrow. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4115-2_3.

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Conference papers on the topic "EMG signal"

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Hailma, Josephine Hope, and S. Prakash. "Signal Decomposition of EMG Signals in Prosthetic Bionic Arm using Component Analysis." In 2025 International Conference on Electronics and Renewable Systems (ICEARS). IEEE, 2025. https://doi.org/10.1109/icears64219.2025.10940097.

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Zhao, Ruihui, Haoxiang Zhang, and Yehao Ma. "MMV based EMG Signal Reconstruction for Remote Rehabilitation." In 2024 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2024. https://doi.org/10.1109/robio64047.2024.10907304.

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Awachat, Harshita S., Rajendra Rewatkar, and K. T. V. Reddy. "EMG Signal Processing for Disease Classification using Machine Learning." In 2024 2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIEI). IEEE, 2024. https://doi.org/10.1109/idicaiei61867.2024.10842774.

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Zîrnă, Bianca-Alexandra, Denis Mihailovschi, and Mădălin Corneliu Frunzete. "EMG Signal Acquisition and Processing for Muscle Contraction Classification." In 2024 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA). IEEE, 2024. http://dx.doi.org/10.23919/spa61993.2024.10715620.

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He, Yuanyuan, Weihong Pan, Ruijia Zhang, and Miao Li. "Towards Understanding Of Human Cervical Spine With EMG Signal." In 2024 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2024. https://doi.org/10.1109/robio64047.2024.10907364.

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Pati, Sarthak, Deepak Joshi, and Ashutosh Mishra. "Locomotion classification using EMG signal." In 2010 International Conference on Information and Emerging Technologies (ICIET). IEEE, 2010. http://dx.doi.org/10.1109/iciet.2010.5625677.

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Kim, Hyungjik, and Sunwoong Choi. "Automatic Sleep Stage Classification Using EEG and EMG Signal." In 2018 Tenth International Conference on Ubiquitous and Future Networks (ICUFN). IEEE, 2018. http://dx.doi.org/10.1109/icufn.2018.8436712.

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Rafiee, J., M. P. Schoen, N. Prause, A. Urfer, and M. A. Rafiee. "A comparison of forearm EMG and psychophysical EEG signals using statistical signal processing." In 2009 2nd International Conference on Computer, Control and Communication (IC$). IEEE, 2009. http://dx.doi.org/10.1109/ic4.2009.4909196.

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Adimulam, Mahesh Kumar, and M. B. Srinivas. "Modeling of EXG (ECG, EMG and EEG) non-idealities using MATLAB." In 2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, 2016. http://dx.doi.org/10.1109/cisp-bmei.2016.7852968.

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Akbary, Parisa, and Hossein Rabbani. "Removing Power Line Interference and ECG signal from EMG signal using Matching Pursuit." In 2010 10th International Conference on Signal Processing (ICSP 2010). IEEE, 2010. http://dx.doi.org/10.1109/icosp.2010.5656716.

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Reports on the topic "EMG signal"

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Sakulneya, Apidej, and Jeffery Roesler. Smart Construction Work-Zone Safety with V2I Passive Material Sensing. Illinois Center for Transportation, 2024. https://doi.org/10.36501/0197-9191/24-027.

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This study explored new vehicle to infrastructure (V2I) technology in construction work zones (CWZ), where speeding, unsafe driving behaviors, and drivers' failure to obey traffic signs contribute significantly to elevated accident rates and fatalities. The objective of this research to advance CWZ safety by evaluating the potential of 3-axis magnetometers attached to a moving cart and traversing over a pavement-assisted passive sensing system can improve vehicle lateral positioning and warning in CWZ. Secondly, to develop a process to implement a programmable ferromagnetic oxide material for

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Martinez, Kimberly D., and Gaojian Huang. Exploring the Effects of Meaningful Tactile Display on Perception and Preference in Automated Vehicles. Mineta Transportation Institute, 2022. http://dx.doi.org/10.31979/mti.2022.2164.

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There is an existing issue in human-machine interaction, such that drivers of semi-autonomous vehicles are still required to take over control of the vehicle during system limitations. A possible solution may lie in tactile displays, which can present status, direction, and position information while avoiding sensory (e.g., visual and auditory) channels overload to reliably help drivers make timely decisions and execute actions to successfully take over. However, limited work has investigated the effects of meaningful tactile signals on takeover performance. This study synthesizes literature i

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Sakulneya, Apidej, and Jeffery Roesler. Enhancing Construction Work-Zone Safety by Passive Pavement-to-Vehicle Communication. Illinois Center for Transportation, 2023. http://dx.doi.org/10.36501/0197-9191/23-016.

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Construction work zones for roads pose significant safety challenges for drivers and workers, which can lead to accidents, injuries, fatalities, and property damage. Enhancing construction work-zone safety requires an understanding of the factors influencing accidents and fatalities and an evaluation of existing safety and traffic-management measures. The objective of this study was to improve work-zone safety for roadways, by connecting passive material sensing in the road with vehicle communication systems. A review of the main roadway work-zone safety literature found driver behavior, traff

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Schultz, Gregory, Jonathan Miller, and Leonard Pasion. Exploiting EMI Signals During Active Transmission. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada534214.

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Carin, Lawrence, Nilanjan Dasgupta, and Levi Kennedy. Optimal Sensor Management and Signal Processing for New EMI Systems. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada534216.

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Bhagavatula, Vijayakumar. Advanced Signal Processing and Machine Learning Approaches for EEG Analysis. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada535204.

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Pineda-Mendez, Raul, Qiming Guo, Noshin Ahmad, Mario A. Romero, and Andrew P. Tarko. Incorporating Time-Dependent Data for Proactive Safety Management. Purdue University, 2024. http://dx.doi.org/10.5703/1288284317700.

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This study proposed a risk-based safety management framework to supplement the current crash-based safety management system. The proposed tool considers time-dependent factors (e.g., hourly traffic, speed features, weather conditions, signal controls) to help justify operational measures for safety improvements (e.g., variable message signs, variable speed limits, warnings). These selected temporal factors subsequently were included in the developed sequential logit models; and those models, applied hour by hour, were then used to estimate the crash probability and severity level. Two typical

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Sanders, Frank, Robert Achatz, John Carroll, Geoffrey Sanders, Robert Sole, and Lawrence Cohen. EMC Measurements for Spectrum Sharing Between LTE Signals and Radar Receivers. Institute for Telecommunication Sciences, 2014. https://doi.org/10.70220/bzsr4cww.

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Kanjilal, Partha P., Candace B. Matthew, Reed W. Hoyt, and Richard R. Gonzalez. Relationships of the Cardiac Signal and Heat Acclimated State: Spectral Profiles from ECG Analysis. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada392102.

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Middleton, David, and Arthur Spaulding. A Tutorial Review of Elements of Weak Signal Detection in Non–Gaussian EMI Environments. Institute for Telecommunication Sciences, 1986. https://doi.org/10.70220/4wp84qrn.

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