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Books on the topic 'ECG signal'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 September 2023

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1

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

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2

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

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3

Martin, Peter. The application of digital signal processing techniques to ECG averaging systems. Salford: University of Salford, 1985.

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4

Escalona, Omar Jacinto. Characterisation of ventricular late potentials in the signal-averaged ECG for diagnostic purposes. [S.l: The Author], 1992.

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5

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

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6

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

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7

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

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8

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

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9

Jatoi, Munsif Ali, and Nidal Kamel. Brain Source Localization Using EEG Signal Analysis. Boca Raton : Taylor & Francis, 2018.: CRC Press, 2017. http://dx.doi.org/10.1201/9781315156415.

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10

Genquan, Feng. EKG and EEG multiphase information analysis. [New York]: American Medical Publishers, 1992.

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11

Paszkiel, Szczepan. Analysis and Classification of EEG Signals for Brain–Computer Interfaces. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-30581-9.

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12

Freeman, Walter J. Imaging Brain Function With EEG: Advanced Temporal and Spatial Analysis of Electroencephalographic Signals. New York, NY: Springer New York, 2013.

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13

O, Quadens, and European Space Agency, eds. Analysis of EEG signals recorded in microgravity during parabolic flight using the method of strange attractor dimensions. Noordwijk, The Netherlands: ESA, 1999.

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14

Developments and Applications for ECG Signal Processing. Elsevier, 2019. http://dx.doi.org/10.1016/c2017-0-01102-3.

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15

Pushpendra, Singh, Sahu Navneet Kumar, and Singh Seema. A Contemporary Approach for ECG Signal Compression. LAP Lambert Academic Publishing, 2015.

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16

Kher, Rahul K., ed. ECG Signal Compression using Compressive Sensing and Wavelet Transform. OMICS International, 2015. http://dx.doi.org/10.4172/978-1-63278-045-4-046.

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17

Joao Paulo do Vale Madeiro, Paulo Cesar Cortez, José Maria Da Silva Monteiro Filho, and Angelo Roncalli Alencar Brayner. Developments and Applications for ECG Signal Processing: Modeling, Segmentation, and Pattern Recognition. Elsevier Science & Technology, 2018.

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18

Joao Paulo do Vale Madeiro, Paulo Cesar Cortez, José Maria Da Silva Monteiro Filho, and Angelo Roncalli Alencar Brayner. Developments and Applications for ECG Signal Processing: Modeling, Segmentation, and Pattern Recognition. Elsevier Science & Technology Books, 2018.

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19

Gacek, Adam, and Witold Pedrycz. ECG Signal Processing, Classification and Interpretation: A Comprehensive Framework of Computational Intelligence. Springer London, Limited, 2011.

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20

Gacek, Adam, and Witold Pedrycz. ECG Signal Processing, Classification and Interpretation: A Comprehensive Framework of Computational Intelligence. Springer, 2013.

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21

Gacek, Adam, and Witold Pedrycz. ECG Signal Processing, Classification and Interpretation: A Comprehensive Framework of Computational Intelligence. Springer, 2014.

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22

Pedrycz, Witold. ECG Signal Processing Classification And Interpretation A Comprehensive Framework Of Computational Intelligence. Springer, 2011.

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23

Butkov, Nic. Polysomnography. Edited by Sudhansu Chokroverty, Luigi Ferini-Strambi, and Christopher Kennard. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199682003.003.0007.

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This chapter provides an overview of the sleep recording process, including the application of electrodes and sensors to the patient, instrumentation, signal processing, digital polysomnography (PSG), and artifact recognition. Topics discussed include indications for PSG, standard recording parameters, patient preparation, electrode placement for recording the electroencephalogram (EEG), electrooculogram (EOG), electromyogram (EMG), and electrocardiogram (ECG), the use of respiratory transducers, oximetry, signal processing, filters, digital data display, electrical safety, and patient monitoring. This chapter also includes record samples of the various types of recording artifacts commonly found in sleep studies, with a detailed description of their causes, preventative measures, and recommended corrective actions.
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24

Wendling, Fabrice, Marco Congendo, and Fernando H. Lopes da Silva. EEG Analysis. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0044.

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This chapter addresses the analysis and quantification of electroencephalographic (EEG) and magnetoencephalographic (MEG) signals. Topics include characteristics of these signals and practical issues such as sampling, filtering, and artifact rejection. Basic concepts of analysis in time and frequency domains are presented, with attention to non-stationary signals focusing on time-frequency signal decomposition, analytic signal and Hilbert transform, wavelet transform, matching pursuit, blind source separation and independent component analysis, canonical correlation analysis, and empirical model decomposition. The behavior of these methods in denoising EEG signals is illustrated. Concepts of functional and effective connectivity are developed with emphasis on methods to estimate causality and phase and time delays using linear and nonlinear methods. Attention is given to Granger causality and methods inspired by this concept. A concrete example is provided to show how information processing methods can be combined in the detection and classification of transient events in EEG/MEG signals.
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25

EEG Signal Processing. Wiley-Interscience, 2007.

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26

Sanei, Saeid, and Jonathon A. Chambers. EEG Signal Processing. Wiley & Sons, Incorporated, John, 2013.

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27

Sanei, Saeid, and Jonathon A. Chambers. EEG Signal Processing. Wiley & Sons, Incorporated, John, 2013.

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28

Chambers, Jonathon, and Saeid Sanei. Eeg Signal Processing. John Wiley & Sons Inc, 2007.

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29

Sanei, Saeid, and Jonathon A. Chambers. EEG Signal Processing. Wiley & Sons, Incorporated, John, 2008.

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30

Mohanty, Saraju P., and Narayan Panigrahi. Brain Computer Interface: EEG Signal Processing. Taylor & Francis Group, 2022.

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31

Sanei, Saeid, and Jonathon A. Chambers. EEG Signal Processing and Machine Learning. Wiley & Sons, Incorporated, John, 2021.

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32

Tran, Yvonne, ed. EEG Signal Processing for Biomedical Applications. MDPI, 2023. http://dx.doi.org/10.3390/books978-3-0365-6536-1.

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33

Sanei, Saeid, and Jonathon A. Chambers. EEG Signal Processing and Machine Learning. Wiley & Sons, Limited, John, 2021.

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34

Hu, Li, and Zhiguo Zhang. EEG Signal Processing and Feature Extraction. Springer, 2019.

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35

Mohanty, Saraju P., and Narayan Panigrahi. Brain Computer Interface: EEG Signal Processing. Taylor & Francis Group, 2022.

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36

Hu, Li, and Zhiguo Zhang. EEG Signal Processing and Feature Extraction. Springer Singapore Pte. Limited, 2020.

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37

Sanei, Saeid, and Jonathon A. Chambers. EEG Signal Processing and Machine Learning. Wiley & Sons, Incorporated, John, 2021.

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38

Sanei, Saeid, and Jonathon A. Chambers. EEG Signal Processing and Machine Learning. Wiley & Sons, Incorporated, John, 2021.

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39

Mohanty, Saraju P., and Narayan Panigrahi. Brain Computer Interface: EEG Signal Processing. Taylor & Francis Group, 2022.

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40

Vanhatalo, Sampsa, and J. Matias Palva. Infraslow EEG Activity. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0032.

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Infraslow electroencephalographic (EEG) activity refers to frequencies below the conventional clinical EEG range that starts at about 0.5 Hz. Evidence suggests that salient EEG signals in the infraslow range are essential parts of many physiological and pathological conditions. In addition, brain is known to exhibit multitude of infraslow processes, which may be observed directly as fluctuations in the EEG signal amplitude, as infraslow fluctuations or intermittency in other neurophysiological signals, or as fluctuations in behavioural performance. Both physiological and pathological EEG activity may range from 0.01 Hz to several hundred Hz. In the clinical context, infraslow activity is commonly observed in the neonatal EEG, during and prior to epileptic seizures, and during sleep and arousals. Laboratory studies have demonstrated the presence of spontaneous infraslow EEG fluctuations or very slow event-related potentials in awake and sleeping subjects. Infraslow activity may not only arise in cortical and subcortical networks but is also likely to involve non-neuronal generators such as glial networks. The full, physiologically relevant range of brain mechanisms can be readily recorded with wide dynamic range direct-current (DC)-coupled amplifiers or full-band EEG (FbEEG). Due to the different underlying mechanisms, a single FbEEG recording can even be perceived as a multimodal recording where distinct brain modalities can be studied simultaneously by performing data analysis for different frequency ranges. FbEEG is likely to become the standard approach for a wide range of applications in both basic science and in the clinic.
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41

Brain Source Localization Using EEG Signal Analysis. Taylor & Francis Group, 2017.

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42

Kamel, Nidal, and Munsif Ali Jatoi. Brain Source Localization Using EEG Signal Analysis. Taylor & Francis Group, 2017.

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43

Kamel, Nidal, and Munsif Ali Jatoi. Brain Source Localization Using EEG Signal Analysis. Taylor & Francis Group, 2017.

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44

Kamel, Nidal, and Munsif Ali Jatoi. Brain Source Localization Using EEG Signal Analysis. Taylor & Francis Group, 2017.

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45

Kamel, Nidal, and Munsif Ali Jatoi. Brain Source Localization Using EEG Signal Analysis. Taylor & Francis Group, 2017.

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46

Kamel, Nidal, and Munsif Ali Jatoi. Brain Source Localization Using EEG Signal Analysis. Taylor & Francis Group, 2017.

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47

Zhang, Yanchun, Yan Li, and Siuly Siuly. EEG Signal Analysis and Classification: Techniques and Applications. Springer International Publishing AG, 2017.

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48

Zhang, Yanchun, Yan Li, and Siuly Siuly. EEG Signal Analysis and Classification: Techniques and Applications. Springer, 2018.

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49

Zhang, Yanchun, Yan Li, and Siuly Siuly. EEG Signal Analysis and Classification: Techniques and Applications. Springer, 2017.

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50

Leong, Wai Yie. EEG Signal Processing: Feature extraction, selection and classification methods. The Institution of Engineering and Technology, 2019.

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