Bioelectrical Signal Processing in Cardiac and Neurological Applications

By L. Sörnmo, Pablo Laguna. 2005

1,127 citations recorded by the source corpus.

Chapter 1. Introduction Chapter 2. The Electroencephalogram Chapter 3. EEG Signal Processing Chapter 4. Evoked Potentials Chapter 5. The Electromyogram Chapter 6. The Electrocardiogram Chapter 7. ECG Signal Processing Chapter 8. Heart Rate Variability Appendices Problems

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/2a4b845d86669b6fae45fd4eed48e1ac909e6249

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In Cognitive Neuroscience

Citations in the corpus

Analysis of Human Electrocardiogram for Biometric Recognition (2008, background): “The position of the T wave is strongly dependent on heart rate, becoming narrower and closer to the QRS complex at rapid rates [13].”
Epidermal Electronics (2011, background): “to a red, vertical stripe that spans from 10 to 300 Hz (32).”
Deep learning for healthcare applications based on physiological signals: A review (2018, background): “Relevant information must be extracted from the physiological signal in order to support a specific healthcare application [5, 6].”
ECG Pattern Analysis for Emotion Detection (2012, methodology): “In a subject-independent mode, the combination of HRV and GSR achieved a performance of 61.8 percent for classification among four emotions.”
A comparative review on sleep stage classification methods in patients and healthy individuals (2017, methodology): “To solve this problem, some criteria such as Whilst Akaike’s information criterion (AIC) [50] are introduced in the literature [51].”
EEG artifact removal—state-of-the-art and guidelines (2015, methodology): “A number of properties of cranial EMG are responsible for its adverse effects on the EEG background activity [32, 33] and make it more difficult to correct for than other kinds of artifacts [34]: EMG presents a wide spectral distribution, thus perturbing all classic EEG bands; in particular it consi”
Principal Component Analysis in ECG Signal Processing (2007, background): “The orthonormal linear transform of x is obtained by w = ΦTx, (15) where the set of basis functions Φ = [ϕ1 ϕ2 · · · ϕN ] is to be determined so that x can be accurately represented in the minimumMSE sense using a subset of functions and the KLT coefficients w1, . . . ,wK .”