4. Principal methodologies to study and analyze ripples as a
biomarker of memory consolidation
Studies of oscillations with frequencies above traditional
electroencephalogram limits (greater than 40 Hz), have increased over
the last decade due to increasing evidence that suggests that HFOs
reflect a mechanism of epileptic phenomena and might serve as a
biomarker of epileptogenesis and epileptogenicity. This has resulted in
a growing interest in the detection and analysis of these events (Birot
et al., 2013; Navarrete et al., 2016). The bandwidth of SWRs (120-250
Hz) (Buzsáki, 2015) falls between the one of HFOs (80-500 Hz) (Burnos et
al., 2014), therefore, the methodologies for automatic identification of
HFOs might be adapted to identify SWRs. In recent decades, there has
been a substantial surge in the field of artificial intelligence, which
has been extensively explored for enhancing the classification of HFOs.
Although our subsequent discussion primarily focuses on conventional
methodologies that do not utilize machine or deep learning, it is worth
noting that readers may find the works of Wong et al., 2021 and
Navas-Olive et al., 2020 intriguing in the context of HFO
classification.
In general, HFO detection methods are classified in three main groups:
manual review, supervised detection, and unsupervised detection. Manual
review is a visual inspection performed by an expert. It is a
time-consuming process and highly subjective to the perception of the
reviewer; however, it is currently considered the gold standard when
assessing the performance of automated algorithms for HFO detection.
Supervised detection consists of methods with high sensitivity and low
specificity detection, which is later reviewed manually to eliminate
false positive events. On the other hand, unsupervised detection methods
must have high sensitivity and high specificity, which is difficult to
achieve (Birot et al., 2013; Navarrete et al., 2016).
In the last two decades, several methods for the automatic detection of
HFOs have been developed. Broadly speaking, the supervised and
unsupervised detection algorithms perform a series of common steps to
identify the putative events. The first step is to emphasize the
frequency of interest by filtering the raw signal. Then, a threshold
handling detection method is implemented. This can be based on the
energy, statistical or spectral characteristics of the filtered data.
Finally, depending on the method, a supervised or unsupervised mechanism
is implemented to distinguish the HFO from noise, artifacts, and spikes
(Navarrete et al., 2016).
Staba et al. (2002) reported the first supervised method for the
automatic detection of HFOs, which uses the root mean square (RMS) to
calculate the threshold after applying a band-pass filter. The authors
declared a sensibility of 84%. Later, other authors used the algorithm
proposed by Staba et al. as the initial steps for theirs. For example,
Burnos et al. (2014) decreased the number of standard deviations (SDs)
established to classify the oscillation as an event of interest (EoI).
Then, they utilized the instantaneous power spectra of the Fourier
Transform representation to eliminate some false positive events. This
methodology had a higher sensibility in 4 out of 5 accepted patients
when compared to the methodology proposed by Staba et al (Burnos et al.,
2014). Other authors that used the algorithm of Staba et al. as the
initial steps of theirs include Crèpon et al. (2010), Ellenrieder et al.
(2012), and Blanco et al. (2010).
Charupanit and Lopour (2017) reported a simple statistical detection
method. The iterative algorithm uses an estimate of the amplitude
probability distribution of the background activity to calculate the
optimum threshold for identification of HFO. It described a sensitivity
of 99.6%. Additionally, Shimamoto et al. (2018) computed an algorithm
that uses independent component analysis to detect ripples. The events
detected were further classified as true or false ripples by
implementing a topographical analysis to the time-frequency plots. They
declared a precision of over 91% and a sensibility of over 79%.
Another supervised algorithm to detect HFOs was implemented by Birot et
al. (2013) . This algorithm uses the RMS amplitudes and a Fourier or
Wavelet energy ratio to detect putative events. They implemented this
algorithm in both human and animal datasets and had an area under the
curve greater than 0.95. Another author that used wavelet entropy was
Zelmann et al. (2010) with a reported sensitivity and specificity of
96%. Finally, Liu et al. (2021) computed in 2021 an algorithm that
combines several reported features like Short-time Energy and Hilbert
Transform with visual extracted features from the frequency spectrum.
They also evaluated this algorithm with datasets from both patients and
animal models and reported a sensibility greater than 91%.
However, despite the increasing methodologies for effective automatic
identification of HFOs, there is currently no gold standard other than
visual examination. Therefore, we suggest that a global consensus of the
identification of true ripple activity and its analysis must be reached
to evaluate more accurately and compare properly under different
experimental conditions. It has been proposed as a solution to implement
different algorithms across similar datasets to reach a consensus about
the one method that performs best in all contexts (Navarrete et al.,
2016).