Photo by 🇸🇮 Janko Ferlič on UnsplashSeizure Detection and Prediction by Parallel Memristive Convolutional Neural Networks(arXiv)Author : Chenqi Li, Corey Lammie, Xuening Dong, Amirali Amirsoleimani, Mostafa Rahimi Azghadi, Roman GenovAbstract : During the previous 20 years, epileptic seizure detection and prediction algorithms have developed quickly. However, regardless of vital efficiency enhancements, their {hardware} implementation utilizing typical applied sciences, comparable to Complementary Metal-Oxide-Semiconductor (CMOS), in energy and area-constrained settings stays a difficult activity; particularly when many recording channels are used. In this paper, we suggest a novel low-latency parallel Convolutional Neural Network (CNN) structure that has between 2–2,800x fewer community parameters in comparison with SOTA CNN architectures and achieves 5-fold cross validation accuracy of 99.84% for epileptic seizure detection, and 99.01% and 97.54% for epileptic seizure prediction, when evaluated utilizing the University of Bonn Electroencephalogram (EEG), CHB-MIT and SWEC-ETHZ seizure datasets, respectively. We subsequently implement our community onto analog crossbar arrays comprising Resistive Random-Access Memory (RRAM) gadgets, and supply a complete benchmark by simulating, laying out, and figuring out {hardware} necessities of the CNN part of our system. To the very best of our data, we’re the primary to parallelize the execution of convolution layer kernels on separate analog crossbars to allow 2 orders of magnitude discount in latency in comparison with SOTA hybrid Memristive-CMOS DL accelerators. Furthermore, we examine the results of non-idealities on our system and examine Quantization Aware Training (QAT) to mitigate the efficiency degradation because of low ADC/DAC decision. Finally, we suggest a caught weight offsetting methodology to mitigate efficiency degradation because of caught RON/ROFF memristor weights, recovering as much as 32% accuracy, with out requiring retraining. The CNN part of our platform is estimated to eat roughly 2.791W of energy whereas occupying an space of 31.255mm2 in a 22nm FDSOI CMOS process2.Ensemble studying utilizing particular person neonatal knowledge for seizure detection (arXiv)Author : Ana Borovac, Steinn Gudmundsson, Gardar Thorvardsson, Saeed M. Moghadam, Päivi Nevalainen, Nathan Stevenson, Sampsa Vanhatalo, Thomas P. RunarssonAbstract : Sharing medical knowledge between establishments is troublesome in observe because of knowledge safety legal guidelines and official procedures inside establishments. Therefore, most current algorithms are skilled on comparatively small electroencephalogram (EEG) knowledge units which is more likely to be detrimental to prediction accuracy. In this work, we simulate a case when the information can’t be shared by splitting the publicly obtainable knowledge set into disjoint units representing knowledge in particular person establishments. We suggest to coach a (native) detector in every establishment and mixture their particular person predictions into one closing prediction. Four aggregation schemes are in contrast, particularly, the bulk vote, the imply, the weighted imply and the Dawid-Skene methodology. The methodology was validated on an unbiased knowledge set utilizing solely a subset of EEG channels. The ensemble reaches accuracy corresponding to a single detector skilled on all the information when ample quantity of information is out there in every establishment. The weighted imply aggregation scheme confirmed finest efficiency, it was solely marginally outperformed by the Dawid — Skene methodology when native detectors strategy efficiency of a single detector skilled on all obtainable data3.Low Latency Real-Time Seizure Detection Using Transfer Deep Learning (arXiv)Author : Vahid Khalkhali, Nabila Shawki, Vinit Shah, Meysam Golmohammadi, Iyad Obeid, Joseph PiconeAbstract : Scalp electroencephalogram (EEG) alerts inherently have a low signal-to-noise ratio as a result of approach the sign is electrically transduced. Temporal and spatial data should be exploited to realize correct detection of seizure occasions. Most common approaches to seizure detection utilizing deep studying don’t collectively mannequin this data or require a number of passes over the sign, which makes the methods inherently non-causal. In this paper, we exploit each concurrently by changing the multichannel sign to a grayscale picture and utilizing switch studying to realize excessive efficiency. The proposed system is skilled end-to-end with solely quite simple pre- and postprocessing operations that are computationally light-weight and have low latency, making them conducive to scientific purposes that require real-time processing. We have achieved a efficiency of 42.05% sensitivity with 5.78 false alarms per 24 hours on the event dataset of v1.5.2 of the Temple University Hospital Seizure Detection Corpus. On a single-core CPU working at 1.7 GHz, the system runs quicker than real-time (0.58 xRT), makes use of 16 Gbytes of reminiscence, and has a latency of 300 msec.
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Advancements in Machine Learning techniques for Seizure Detection part2(Healthcare X ML) | by Monodeep Mukherjee | Nov, 2022
