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1. Distributed Kriging-Bootstrapped DNN Model for Fast, Accurate Seizure Detection from EEG Signals

   Olokodana, Ibrahim; Mohanty, Saraju; Kougianos, Elias (January 2020, Proceedings of the 19th IEEE Computer Society Annual Symposium on VLSI (ISVLSI))

   The modeling of the brain as a three-dimensional spatial object, similar to a geographical landscape, has the paved way for the successful application of Kriging methods in solving the seizure detection problem with good performance but in cubic computational time complexity. The Deep Neural Network (DNN) has been widely used for seizure detection due to its effectiveness in classification tasks, although at the cost of a protracted training time. While Kriging exploits the spatial correlation between data locations, DNN relies on its capacity to learn intrinsic representations within the dataset from the basest unit parts. This paper presents a Distributed Kriging-Bootstrapped Deep Neural Network (DNN) model as a twofold solution for fast and accurate seizure detection using brain signals collected with the electroencephalogram (EEG) from healthy subjects and patients of epilepsy. The proposed model parallelizes the Kriging computation into different cores in a machine and then produces a strongly correlated, unified quasi-output data which serves as an input to the Deep Neural Network. Experimental results validate the proposed model as superior to conventional Kriging methods and DNN by training in 91% less time than the basic DNN and about three times as fast as the ordinary Kriging-Bootstrapped Deep Neural Network (DNN) model while maintaining good performance in terms of sensitivity, specificity and testing accuracy compared to other models and existing works. 

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2. Kriging-Bootstrapped DNN Hierarchical Model for Real-Time Seizure Detection from EEG Signals

   Olokodana, Ibrahim; Mohanty, Saraju; Kougianos, Elias (January 2020, Proceedings of the 6th IEEE World Forum on Internet of Things (WF-IoT))

   The Deep Neural Network (DNN) model is known for its high accuracy in classification tasks due to its intrinsic ability to learn the underlying patterns existing in a set of data. Hence it has gained momentum in seizure detection research, as in many other fields. However, its high performance is at the expense of an extensive training time. This is not appropriate for a real-time application such as seizure detection in which a swift reaction is required to save the life of the patient. This paper presents a novel Kriging-Bootstrapped Deep Neural Network hierarchical model for early seizure detection in which Kriging is first used to generate a well-correlated intermediate data set from the original input. The correlated data is then fed into the DNN for the final training. Experiments were carried out using electroencephalogram (EEG) data from both normal and epileptic patients. Results show that, with the same architecture and data size, the cumulative training time of the Krigging-Bootstrapped DNN is about 75% lower than that of the ordinary DNN without a compromise in performance as the proposed hybrid model shows a slightly better accuracy than the baseline DNN model. 

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3. Low Latency Real-Time Seizure Detection Using Transfer Deep Learning

   Khalkhali, Vahid Shawki (December 2021, Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium (SPMB))

   Obeid, Iyad Selesnick (Ed.)

   Scalp electroencephalogram (EEG) signals inherently have a low signal-to-noise ratio due to the way the signal is electrically transduced. Temporal and spatial information must be exploited to achieve accurate detection of seizure events. Most popular approaches to seizure detection using deep learning do not jointly model this information or require multiple passes over the signal, which makes the systems inherently non-causal. In this paper, we exploit both simultaneously by converting the multichannel signal to a grayscale image and using transfer learning to achieve high performance. The proposed system is trained end-to-end with only very simple pre- and post-processing operations which are computationally lightweight and have low latency, making them conducive to clinical applications that require real-time processing. We have achieved a performance of 42.05% sensitivity with 5.78 false alarm per 24 hours on the development dataset of v1.5.2 of the Temple University Hospital Seizure Detection Corpus. On a single core CPU operating at 1.7 GHz, the system runs faster than real-time (0.58 xRT), uses 16 Gbytes of memory, and has a latency of 300 msec. 

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4. Automated seizure activity tracking and onset zone localization from scalp EEG using deep neural networks

   https://doi.org/10.1371/journal.pone.0264537  

   Craley, Jeff; Jouny, Christophe; Johnson, Emily; Hsu, David; Ahmed, Raheel; Venkataraman, Archana (February 2022, PLOS ONE)

   Bernhardt, Boris C (Ed.)

   We propose a novel neural network architecture, SZTrack, to detect and track the spatio-temporal propagation of seizure activity in multichannel EEG. SZTrack combines a convolutional neural network encoder operating on individual EEG channels with recurrent neural networks to capture the evolution of seizure activity. Our unique training strategy aggregates individual electrode level predictions for patient-level seizure detection and localization. We evaluate SZTrack on a clinical EEG dataset of 201 seizure recordings from 34 epilepsy patients acquired at the Johns Hopkins Hospital. Our network achieves similar seizure detection performance to state-of-the-art methods and provides valuable localization information that has not previously been demonstrated in the literature. We also show the cross-site generalization capabilities of SZTrack on a dataset of 53 seizure recordings from 14 epilepsy patients acquired at the University of Wisconsin Madison. SZTrack is able to determine the lobe and hemisphere of origin in nearly all of these new patients without retraining the network . To our knowledge, SZTrack is the first end-to-end seizure tracking network using scalp EEG. 

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5. Interpolating Hydrologic Data Using Laplace Formulation

   https://doi.org/10.3390/rs15153844  

   Xu, Tianle; Merwade, Venkatesh; Wang, Zhiquan (August 2023, Remote Sensing)

   Spatial interpolation techniques play an important role in hydrology, as many point observations need to be interpolated to create continuous surfaces. Despite the availability of several tools and methods for interpolating data, not all of them work consistently for hydrologic applications. One of the techniques, the Laplace Equation, which is used in hydrology for creating flownets, has rarely been used for data interpolation. The objective of this study is to examine the efficiency of Laplace formulation (LF) in interpolating data used in hydrologic applications (hydrologic data) and compare it with other widely used methods such as inverse distance weighting (IDW), natural neighbor, and ordinary kriging. The performance of LF interpolation with other methods is evaluated using quantitative measures, including root mean squared error (RMSE) and coefficient of determination (R2) for accuracy, visual assessment for surface quality, and computational cost for operational efficiency and speed. Data related to surface elevation, river bathymetry, precipitation, temperature, and soil moisture are used for different areas in the United States. RMSE and R2 results show that LF is comparable to other methods for accuracy. LF is easy to use as it requires fewer input parameters compared to inverse distance weighting (IDW) and Kriging. Computationally, LF is faster than other methods in terms of speed when the datasets are not large. Overall, LF offers a robust alternative to existing methods for interpolating various hydrologic data. Further work is required to improve its computational efficiency. 

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