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Electroencephalogram (EEG) is a gold standard in epilepsy diagnosis and has been widely studied for epilepsy-related signal classification, such as seizure detection or focus localization. In the past few years, discrete wavelet transform (DWT) has been widely used to analyze epileptic EEG. However, one practical question unanswered is the optimal levels of wavelet decomposition. Deeper DWT can yield a more detailed depiction of signals but it requires substantially more computational time.

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Telemonitoring of biosignals is a growing area of research due to the aging world population. Telemonitoring utilizes a wireless body-area network (WBAN) consisting of wearable biosignal sensors equipped with ultra low power radios. The measured data from each sensor on the patient is sent to a smartphone, which then sends the data to a healthcare provider via the internet. To enable real-time telemonitoring of the biosignals, it is desirable to have accurate timestamped data from the sensors in the WBAN.

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