Leonardo Duque-Muñoz

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The use of time series decomposition into sub–bands of frequency to accomplish the oscillation modes in nonstationary signals is proposed. Specifically, EEG signals are decomposed into frequency sub-bands, and the most relevant of them are employed for the detection of epilepsy seizures. Since the computation of oscillation modes is carried out based on(More)
The characterization of daily activities using accelerometers is a currently active research field, with special interest on fall detection of elderly people and sports performance. Most works that account walking and running are peak-acceleration based, however, false positives due to artefact acceleration peaks affect the estimation. Also, the proposed(More)
BACKGROUND The extraction of physiological rhythms from electroencephalography (EEG) data and their automated analyses are extensively studied in clinical monitoring, to find traces of interictal/ictal states of epilepsy. METHODS Because brain wave rhythms in normal and interictal/ictal events, differently influence neuronal activity, our proposed(More)
Time-frequency decompositions (TFDs) are well known techniques that permit to extract useful information or features from EEG signals, being necessary to distinguish between irrelevant information and the features effectively representing the subjacent physiological phenomena, according to some evaluation measure. This work introduces a new method to obtain(More)
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