A prediction model for the number of intrinsic mode functions in biomedical signals: The case of electrocardiogram

Karagiannis, A.; Constantinou, P.

doi:10.1016/j.bspc.2011.02.005

Cited by 11 publications

(2 citation statements)

References 24 publications

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“…Although the physical meaning of IMF is still an open issue (Karagiannis and Constantinou, ), the characteristics of the IMF help to define the instantaneous amplitude and instantaneous frequency of the signal.…”

Section: Methodsmentioning

confidence: 97%

Electrocardiogram‐based emotion recognition system using empirical mode decomposition and discrete Fourier transform

et al. 2013

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Emotion recognition using physiological signals has gained momentum in the field of human computer–interaction. This work focuses on developing a user‐independent emotion recognition system that would classify five emotions (happiness, sadness, fear, surprise and disgust) and neutral state. The various stages such as design of emotion elicitation protocol, data acquisition, pre‐processing, feature extraction and classification are discussed. Emotional data were obtained from 30 undergraduate students by using emotional video clips. Power and entropy features were obtained in three ways – by decomposing and reconstructing the signal using empirical mode decomposition, by using a Hilbert–Huang transform and by applying a discrete Fourier transform to the intrinsic mode functions (IMFs). Statistical analysis using analysis of variance indicates significant differences among the six emotional states (p < 0.001). Classification results indicate that applying the discrete Fourier transform instead of the Hilbert transform to the IMFs provides comparatively better accuracy for all the six classes with an overall accuracy of 52%. Although the accuracy is less, it reveals the possibility of developing a system that could identify the six emotional states in a user‐independent manner using electrocardiogram signals. The accuracy of the system can be improved by investigating the power and entropy of the individual IMFs.

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Section: Methodsmentioning

confidence: 97%

Electrocardiogram‐based emotion recognition system using empirical mode decomposition and discrete Fourier transform

et al. 2013

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“…Οι δημοσιεύσεις του συγγραφέα που αναφέρονται στο κεφάλαιο αυτό είναι η εργασία για το μοντέλο πρόβλεψης που δημοσιεύτηκε στο περιοδικό Biomedical Signal Processing and Control του εκδοτικού οίκου Elsevier [162] και μια σειρά δημοσιεύσεων για τη μελέτη της επίδοσης της εμπειρικής μεθόδου αποσύνθεσης [151], [152], [153], [154]. Η ερευνητική εργασία πάνω στο μοντέλο εκτίμησης έχει δημοσιευτεί στις εργασίες [163], [164] αλλά μόνο για το posteriori τμήμα του μοντέλου εκτίμησης ενώ θα υποβληθεί προς κρίση σε περιοδικό ολόκληρη η εργασία του δεύτερου τμήματος του έκτου κεφαλαίου.…”

Section: κατανάλωσηςunclassified

Τεχνικές Και Μέθοδοι Παρακολούθησης Ενεργειακής Κατανάλωσης Και Μοντελοποίηση Αλγορίθμων Επεξεργασίας Βιοϊατρικών Σημάτων Σε Ασύρματα Δίκτυα Αισθητήρων

Καραγιάννης¹

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Detectability of rotor failure for induction motors through stator current based on advanced signal processing approaches

Dehina

Boumehraz

Kratz

2021

Int. J. Dynam. Control

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A prediction model for the number of intrinsic mode functions in biomedical signals: The case of electrocardiogram

Cited by 11 publications

References 24 publications

Electrocardiogram‐based emotion recognition system using empirical mode decomposition and discrete Fourier transform

Electrocardiogram‐based emotion recognition system using empirical mode decomposition and discrete Fourier transform

Τεχνικές Και Μέθοδοι Παρακολούθησης Ενεργειακής Κατανάλωσης Και Μοντελοποίηση Αλγορίθμων Επεξεργασίας Βιοϊατρικών Σημάτων Σε Ασύρματα Δίκτυα Αισθητήρων

Detectability of rotor failure for induction motors through stator current based on advanced signal processing approaches

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