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1. Applicability of Hyperdimensional Computing for Seizure Prediction Using LBP and PSD Features from iEEG

   https://doi.org/10.1109/MWSCAS57524.2023.10405889  

   Ge, Lulu; Parhi, Keshab K. (August 2023, Proc. of 2023 Midwest Symposium on Circuits and Systems)

   Hyperdimensional computing (HDC) has been assumed to be attractive for time-series classification. These classifiers are ideal for one or few-shot learning and require fewer resources. These classifiers have been demonstrated to be useful in seizure detection. This paper investigates subject-specific seizure prediction using HDC from intracranial elec-troencephalogram (iEEG) from the publicly available Kaggle dataset. In comparison to seizure detection (interictal vs. ictal), seizure prediction (interictal vs. preictal) is a more challenging problem. Two HDC-based encoding strategies are explored: local binary pattern (LBP) and power spectral density (PSD). The average performance of HDC classifiers using the two encoding approaches is computed using the leave-one-seizure-out cross-validation method. Experimental results show that the PSD method using a small number of features selected by the minimum redundancy maximum relevance (mRMR) achieves better seizure prediction performance than the LBP method on the trairring and validation data. 

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2. Premature Ventricular Contraction Beat Classification via Hyperdimensional Computing

   https://doi.org/10.1109/IEEECONF56349.2022.10052044  

   Ung, Brandon W.; Ge, Lulu; Parhi, Keshab K. (October 2022, Proc. 2022 Asilomar Conference on Signals, Systems and Computers)

   Hyperdimensional computing (HD) is an emerging brain-inspired paradigm used for machine learning classification tasks. It manipulates ultra-long vectors-hypervectors- using simple operations, which allows for fast learning, energy efficiency, noise tolerance, and a highly parallel distributed framework. HD computing has shown a significant promise in the area of biological signal classification. This paper addresses group-specific premature ventricular contraction (PVC) beat detection with HD computing using the data from the MIT-BIH arrhythmia database. Temporal, heart rate variability (HRV), and spectral features are extracted, and minimal redundancy maximum relevance (mRMR) is used to rank and select features for classification. Three encoding approaches are explored for mapping the features into the HD space. The HD computing classifiers can achieve a PVC beat detection accuracy of 97.7 % accuracy, compared to 99.4% achieved by more computationally complex methods such as convolutional neural networks (CNNs). 

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3. Modeling and Simulation of Circuit-Level Nonidealities for an Analog Computing Design Approach with Application to EEG Feature Extraction

   https://doi.org/10.1109/TCAD.2022.3170248  

   Mirchandani, Nikita; Zhang, Yuqing; Abdelfattah, Safaa; Onabajo, Marvin; Shrivastava, Aatmesh (April 2022, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems)

   This paper presents a design approach for the modeling and simulation of ultra-low power (ULP) analog computing machine learning (ML) circuits for seizure detection using EEG signals in wearable health monitoring applications. In this paper, we describe a new analog system modeling and simulation technique to associate power consumption, noise, linearity, and other critical performance parameters of analog circuits with the classification accuracy of a given ML network, which allows to realize a power and performance optimized analog ML hardware implementation based on diverse application-specific needs. We carried out circuit simulations to obtain non-idealities, which are then mathematically modeled for an accurate mapping. We have modeled noise, non-linearity, resolution, and process variations such that the model can accurately obtain the classification accuracy of the analog computing based seizure detection system. Noise has been modeled as an input-referred white noise that can be directly added at the input. Device process and temperature variations were modeled as random fluctuations in circuit parameters such as gain and cut-off frequency. Nonlinearity was mathematically modeled as a power series. The combined system level model was then simulated for classification accuracy assessments. The design approach helps to optimize power and area during the development of tailored analog circuits for ML networks with the ability to potentially trade power and performance goals while still ensuring the required classification accuracy. The simulation technique also enables to determine target specifications for each circuit block in the analog computing hardware. This is achieved by developing the ML hardware model, and investigating the effect of circuit nonidealities on classification accuracy. Simulation of an analog computing EEG seizure detection block shows a classification accuracy of 91%. The proposed modeling approach will significantly reduce design time and complexity of large analog computing systems. Two feature extraction approaches are also compared for an analog computing architecture. 

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4. Krig-Detect: Exploring Alternative Kriging Methods for Real-Time Seizure Detection from EEG Signal

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

   Epileptic seizures are dangerous. They render patients unconscious and can lead to death within seconds of onset. There is, therefore, the need for a very fast and accurate seizure detection mechanism. Kriging methods have been used extensively in geostatistics for spatial prediction and are known for very high accuracy. By modeling the brain as a spatial map, we demonstrate the effectiveness of Kriging Methods for efficient seizure detection in an edge computing paradigm. We explore three different types of Kriging - Simple Kriging, Ordinary Kriging and Universal Kriging. Results from various experiments with electroencephalogram (EEG) signals of both healthy and diseased patients show that all three Kriging methods have good performance in terms of accuracy, sensitivity and detection latency. However, Simple Kriging emerged as the slight favorite for seizure detection with a mean detection latency of 0.81 sec, an accuracy of 97.50%, a sensitivity of 94.74% and a perfect specificity. Simple Kriging is at least 5% better than Ordinary Kriging and Universal Kriging when evaluated at 68.2% confidence interval. The results obtained in this paper compare favorably with other seizure detection models in the literature. 

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5. 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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