presurgical investigations for categorizing focal patterns are crucial, leading to localization and surgical removal of the epileptic focus. this paper presents a machine learning approach using information theoretic features extracted from high-frequency subbands to detect the epileptic focus from interictal intracranial electroencephalogram (ieeG). it is known that high-frequency subbands (>80 Hz) include important biomarkers such as high-frequency oscillations (Hfos) for identifying epileptic focus commonly referred to as the seizure onset zone (SoZ). in this analysis, the multi-channel interictal ieeG signals were splitted into segments and each segment was decomposed into multiple high-frequency subbands. The different types of entropy were calculated for each of the subbands and the sparse linear discriminant analysis (sLDA) was applied to select the prominent entropy features. Due to the imbalance of SoZ and non-SoZ channels in ieeG data, the use of machine learning techniques is always tricky. to deal with the imbalanced learning problem, an adaptive synthetic oversampling approach (ADASYn) with radial basis function kernel-based SVM was used to detect the focal segments. finally, the epileptic focus was identified based on detection of focal segments on SOZ and non-SOZ channels. Eight patients were examined to observe the efficiency of the automatic detector. The experimental results and statistical tests indicate that the proposed automatic detector can identify the epileptic focus accurately and efficiently.
Invasive species pose a grave threat to many national parks. Construction of roads and trails for tourism may facilitate invasion of alien species. To understand the effect of road and trail construction on invasive species, we established six transects in three land-use types (forest interior and road or trail edges) in Endau Rompin National Park, Johor, Malaysia, where we measured the number of an invasive shrub, Clidemia hirta (Melastomataceae), canopy openness, and soil properties; compared the density of C. hirta between the three land-use types; and finally, identified soil and canopy variables affecting its abundance using generalized linear mixed models. C. hirta was found along the road and trail with density ranging from 0.0 m À2 to 33 m À2 (average: 3.8 m À2 ), but was not found in the forest interior. Generalized linear mixed models suggested that canopy openness and soil pH negatively affected the density of C. hirta along the road, as did total soil nitrogen along the trail. This suggests that C. hirta was more abundant along dark and nutrient-poor road and trail edges. The construction of narrow roads (2.0-3.8 m) and trails (0.5-2.0 m wide) at our site would be considered a relatively minor disturbance without intensive clear cuts, and C. hirta seemed to prefer habitats with such minor disturbances. In the tropical rainforests, the managers or conservationists of the national park should include consideration of the effects such as minor disturbances have on invasive species.
To cope with the lack of highly skilled professionals, machine learning with proper signal processing is key for establishing automated diagnostic-aid technologies with which to conduct epileptic electroencephalogram (EEG) testing. In particular, frequency filtering with the appropriate passbands is essential for enhancing the biomarkers-such as epileptic spike waves-that are noted in the EEG. This paper introduces a novel class of neural networks (NNs) that have a bank of linear-phase finite impulse response filters at the first layer as a preprocessor that can behave as bandpass filters that extract biomarkers without destroying waveforms because of a linearphase condition. Besides, the parameters of the filters are also data-driven. The proposed NNs were trained with a large amount of clinical EEG data, including 15,833 epileptic spike waveforms recorded from 50 patients, and their labels were annotated by specialists. In the experiments, we compared three scenarios for the first layer: no preprocessing, discrete wavelet transform, and the proposed data-driven filters. The experimental results show that the trained data-driven filter bank with supervised learning behaves like multiple bandpass filters. In particular, the trained filter passed a frequency band of approximately 10-30 Hz. Moreover, the proposed method detected epileptic spikes, with the area under the receiver operating characteristic curve of 0.967 in the mean of 50 intersubject validations.
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