A Pipeline for Extraction of Sharp-Wave Ripples from Multi-Channel in vivo Recording EEG

Abstract

Extraction of Sharp-Wave Ripples (SWRs) from brain wave signals plays an important part in various medical studies of mammalian nervous systems. SWRs, playing a crucial role in memory consolidation, are oscillatory patterns in the mammalian brain hippocampus seen on an EEG during immobility and sleep. An SWR is composed of large-amplitude sharp waves accompanied by fast field potential oscillations known as ripple rhythms. However, most of the current commercial software for brain wave processing does not provide with an accurate SWR extraction function. Also, so far there are few literatures that fully explore the ripple detection method. Taking a fuller look at the characteristics of ripple events, an improved pipeline is presented to extract SWRs. The utility of detection based on the large-amplitude feature of SWRs will be weakened by another feature, fast oscillation. Therefore, to shield the extraction from that undesired influence, Hilbert transformation is suggested to restore the analytical signal in complex number field and then to obtain the envelope of the original EEG. Next, Gaussian window is adopted to get rid of some artifacts. Then, the central and the start and end segment of an SWR are successively determined with a sliding window. In addition, considering that the determination of the duration of a ripple also changes the frequency content of a detected, truncated ripple by the spectral leakage effect, which makes it hard to find the actual frequency of the rhythm, we add Hanning window to prevent that effect. From three sets of multi-channel in vivo recording EEG data obtained from different genotypes of mice, we detected SWR events with the proposed method, whose effectiveness and accuracy were validated.