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Unsupervised seizure identification on EEG

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022年 215卷 106604-106604页

作者： Yildiz, Ilkay Garner, Rachael Lai, Matthew Duncan, Dominique Univ Southern Calif Keck Sch Med Stevens Neuroimaging & Informat Inst Lab Neuro Imaging 2025 Zonal Ave Los Angeles CA 90033 USA

Background and Objective: Epilepsy is one of the most common neurological disorders, whose development is typically detected via early seizures. Electroencephalogram (EEG) is prevalently employed for seizure identification due to its routine and low expense collection. The stochastic nature of EEG makes manual seizure inspections laborsome, motivating automated seizure identification. The relevant literature focuses mostly on supervised machine learning. Despite their success, supervised methods require expert labels indicating seizure segments, which are difficult to obtain on clinically-acquired EEG. Thus, we aim to devise an unsupervised method for seizure identification on EEG. Methods: We propose the first fully-unsupervised deep learning method for seizure identification on raw EEG, using a variational autoencoder (VAE). In doing so, we train the VAE on recordings without seizures. As training captures non-seizure activity, we identify seizures with respect to the reconstruction errors at inference time. Moreover, we extend the traditional VAE training loss to suppress EEG artifacts. Our method does not require ground-truth expert labels indicating seizure segments or manual feature extraction. Results: We implement our method using the PyTorch library and execute experiments on an NVIDIA V100 GPU. We evaluate our method on three benchmark EEG datasets: (i) intracranial recordings from the University of Pennsylvania and the Mayo Clinic, (ii) scalp recordings from the Temple University Hospital of Philadelphia, and (iii) scalp recordings from the Massachusetts Institute of Technology and the Boston Children's Hospital. To assess performance, we report accuracy, precision, recall, Area under the Receiver Operating Characteristics Curve (AUC), and p-value under the Welch t-test for distinguishing seizure vs. non-seizure EEG windows. Our approach can successfully distinguish seizures from nonseizure activity, with up to 0.83 AUC on intracranial recordings. Moreove

关键词： Epilepsy Seizure EEG variational autoencoder Unsupervised learning Sparsity

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A Novel Deep Neural Network Method for HAR-Based Team Training Using Body-Worn Inertial Sensors

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SENSORS 2022年第21期22卷 8507页

作者： Fan, Yun-Chieh Tseng, Yu-Hsuan Wen, Chih-Yu Natl Chung Shan Inst Sci & Technol Aeronaut Syst Res Div Simulator Syst Sect Taichung 407 Taiwan Natl Chung Hsing Univ Dept Elect Engn Taichung 402 Taiwan Natl Chung Hsing Univ Dept Comp Sci & Engn Taichung 402 Taiwan Natl Chung Hsing Univ Innovat & Dev Ctr Sustainable Agr IDCSA Taichung 402 Taiwan

Human activity recognition (HAR) became a challenging issue in recent years. In this paper, we propose a novel approach to tackle indistinguishable activity recognition based on human wearable sensors. Generally speaking, vision-based solutions struggle with low illumination environments and partial occlusion problems. In contrast, wearable inertial sensors can tackle this problem and avoid revealing personal privacy. We address the issue by building a multistage deep neural network framework that interprets accelerometer, gyroscope, and magnetometer data that provide useful information of human activities. Initially, the stage of variational autoencoders (VAE) can extract the crucial information from raw data of inertial measurement units (IMUs). Furthermore, the stage of generative adversarial networks (GANs) can generate more realistic human activities. Finally, the transfer learning method is applied to enhance the performance of the target domain, which builds a robust and effective model to recognize human activities.

关键词： human activity recognition variational autoencoder generative adversarial networks

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Multimodal Remote Sensing Data Classification Based on Gaussian Mixture variational Dynamic Fusion Network

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 1页

作者： Wang, Haoyu Liu, Xiaomin Qiao, Zhenzhuang Wang, Guoqing Chen, Haotian China Univ Min & Technol Engn Res Ctr Intelligent Control Underground Space Minist Educ Xuzhou 221116 Peoples R China China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Peoples R China

With the development of sensor technology, the rational use of multimodal data has become a research hotspot in the field of remote sensing. The multimodal fusion method can effectively improve the accuracy of remote sensing data classification by using the complementary information of different modalities. However, the existing multimodal fusion methods face many challenges, including difficulties in suppressing spectral noise, fully mining contextual information, and learning the strong adaptive fusion pattern. To address the above challenges, a Gaussian mixture variational dynamic fusion network (GM-VDFN) is proposed. First, a multimodal multiscale spatial graph is constructed, and the graph convolution is used to learn the multiscale features. In this process, a spatial topology constraint based on GM (STC-GM) is proposed, which suppresses spectral noise by constraining the topological consistency of the two modalities. Second, a multiscale dynamic graph aggregation module (MDGAM) is constructed, which can capture the shareable class identification information from multiscale features and mine personalized fusion patterns suitable for each sample. Finally, the evidence lower bound for the multimodal joint distribution is derived, and a multimodal variational autoencoder (M-VAE) is designed. Optimizing the evidence lower bound to model multimodal joint distributions, thereby learning the strong adaptive fusion pattern between modalities. Experimental results on four fusion datasets (Houston 2013, Trento, MUUFL, and Houston 2018) show that GM-VDFN achieved state-of-the-art performance in multimodal remote sensing data classification tasks.

关键词： Laser radar Noise Remote sensing Topology Data mining Data integration Interference Classification feature fusion hyperspectral image (HSI) light detection and ranging (LiDAR) variational autoencoder

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Learning and Reusing Quadruped Robot Movement Skills from Biological Dogs for Higher-Level Tasks

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SENSORS 2024年第1期24卷 28页

作者： Wan, Qifeng Luo, Aocheng Meng, Yan Zhang, Chong Chi, Wanchao Zhang, Shenghao Liu, Yuzhen Zhu, Qiuguo Kong, Shihan Yu, Junzhi Peking Univ Coll Engn Dept Adv Mfg & Robot State Key Lab Turbulence & Complex Syst Beijing 100871 Peoples R China Tencent Robot X Shenzhen 518057 Peoples R China Zhejiang Univ Inst Cyber Syst & Control Coll Control Sci & Engn Hangzhou 310027 Peoples R China Chinese Acad Sci Inst Software Sci & Technol Integrated Informat Syst Lab Beijing 100190 Peoples R China

In the field of quadruped robots, the most classic motion control algorithm is based on model prediction control (MPC). However, this method poses challenges as it necessitates the precise construction of the robot's dynamics model, making it difficult to achieve agile movements similar to those of a biological dog. Due to these limitations, researchers are increasingly turning to model-free learning methods, which significantly reduce the difficulty of modeling and engineering debugging and simultaneously reduce real-time optimization computational burden. Inspired by the growth process of humans and animals, from learning to walk to fluent movements, this article proposes a hierarchical reinforcement learning framework for the motion controller to learn some higher-level tasks. First, some basic motion skills can be learned from motion data captured from a biological dog. Then, with these learned basic motion skills as a foundation, the quadruped robot can focus on learning higher-level tasks without starting from low-level kinematics, which saves redundant training time. By utilizing domain randomization techniques during the training process, the trained policy function can be directly transferred to a physical robot without modification, and the resulting controller can perform more biomimetic movements. By implementing the method proposed in this article, the agility and adaptability of the quadruped robot can be maximally utilized to achieve efficient operations in complex terrains.

关键词： quadrupedal robots reinforcement learning motion imitation variational autoencoder

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Slice-to-voxel stochastic reconstructions on porous media with hybrid deep generative model

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COMPUTATIONAL MATERIALS SCIENCE 2021年 186卷 110018-110018页

作者： Zhang, Fan Teng, Qizhi Chen, Honggang He, Xiaohai Dong, Xiucheng Sichuan Univ Coll Elect & Informat Engn Chengdu Peoples R China Xihua Univ Sch Elect Engn & Elect Informat Chengdu Peoples R China Minist Educ Key Lab Wireless Power Transmiss Chengdu Peoples R China

Three-dimensional (3D) microstructures are useful for studying the spatial structures and physical properties of porous media. A number of stochastic reconstructions are essential to discover the geometry and topology of the porous media and the its flow behavior. While several deep-learning based generative models have been proposed to deal with the issue, the obstacles about stable training and difficulty in convergence limit the application of these models. To address these problems, a hybrid deep generative model for 3D porous media reconstruction is proposed. The hybrid model is composed of a variant autoencoder (VAE) and a generative adversarial network (GAN). It receives a two-dimensional image as input and generates 3D porous media. The encoder from VAE characterizes the statistical and morphological information of input image and generates a low-dimensional feature vector for generator. Benefiting from the hybrid model, the training becomes more stable and the generative capability is enhanced as well. Furthermore, a simple but useful loss function is used to help improve accuracy. The proposed model is tested on both isotropic and anisotropic porous media. The results show the synthetic realizations have good agreement to the targets on visual inspection, statistical functions and two-phase flow simulation.

关键词： Porous media 3D reconstruction Generative adversarial networks variational autoencoder Hybrid deep generative model

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Deep generative learning for exploration in large electrochemical impedance dataset

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第PartC期126卷

作者： Doonyapisut, Dulyawat Kim, Byeongkyu Kim, Jung Kyu Lee, Eunseok Chung, Chan-Hwa Sungkyunkwan Univ Sch Chem Engn Suwon 16419 South Korea Sungkyunkwan Univ Coll Comp & Informat Suwon 16419 South Korea

The fields of energy storage, photocatalysis, and sensors have undergone substantial technological advancements, which have led to the generation of vast amounts of data on electrochemical impedance (EIS). The interpretation of large amounts of EIS data is a challenging task since the analysis of EIS data requires multiple steps to get a suitable equivalent circuit. Recently, some progress has been made in the machine learning (ML) model for EIS classification. However, most of the ML models are performed as a "black box" model, which provides only the classification result and lacks physical descriptor representation. Here, we apply variational autoencoders (VAE) to EIS data analysis, which includes classification, parameter prediction, and the visualization of physical descriptors. The VAE model performed well in the classification task, with an accuracy of 82.0%-92.4%. In the prediction task, VAE shows a high R-squared value on the Randles circuit. Additionally, the VAE model can map physical descriptors to the latent space, allowing the latent space to transform into a property space, which plays an important role in the optimization and exploration of novel materials research.

关键词： Electrochemical impedance spectroscopy Equivalent circuit model variational autoencoder Impedance optimization Impedance visualization

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Deep generative fuel design in low data regimes via multi-objective imitation

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CHEMICAL ENGINEERING SCIENCE 2023年第1期274卷

作者： Liu, Yifan Liu, Runze Duan, Jinyu Wang, Li Zhang, Xiangwen Li, Guozhu Tianjin Univ Sch Chem Engn & Technol Key Lab Green Chem Technol Minist Educ Tianjin 300072 Peoples R China Collaborat Innovat Ctr Chem Sci & Engn Tianjin Tianjin 300072 Peoples R China Haihe Lab Sustainable Chem Transformat Tianjin 300192 Peoples R China

Commercial fuel discovery faces a constantly decreasing return of investment due to due to increasingly tight environmental criteria and reducing potential uses for each new fuel. In this paper, a deep generative model, termed Latent Interspace Generative Adversarial Network with a Domain of Stacking (LIGANDS), has been established to screen desired fuel molecules in the large chemical space without setting design rules manually. A variational autoencoder, a generative adversarial network and a stacking model are well integrated in LIGANDS through model convergence. Given only the structures of 255 typical highenergy???density fuels in low data regimes, LIGANDS generated 3461 new fuel molecules with similar property distribution and improved energy performance as the qualified candidates of next-generation fuels. To expand and enrich the fuel-relevant chemical space with innovative molecular entities on demand, in-depth multi-objective imitation on the key properties of target fuel is realized by LIGANDS through optimizing generative molecular structures and their distribution. ?? 2023 Elsevier Ltd. All rights reserved.

关键词： Deep learning Generative model Fuel variational autoencoder Generative adversarial network Ensemble learning

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Data-Driven Virtual Material Analysis and Synthesis for Solid Electrolyte Interphases

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ADVANCED ENERGY MATERIALS 2023年第40期13卷 2301985-2301985页

作者： Rajagopal, Deepalaxmi Koeppe, Arnd Esmaeilpour, Meysam Selzer, Michael Wenzel, Wolfgang Stein, Helge Nestler, Britta Karlsruhe Inst Technol KIT Inst Appl Mat IAM MMS Str Forum 7 D-76131 Karlsruhe Germany Karlsruhe Inst Technol KIT Inst Nanotechnol INT Hermann Von Helmholtz Pl 1 D-76344 Eggenstein Leopoldshafen Germany Helmholtz Inst Ulm Lise Meitner Str 16 D-89081 Ulm Germany

Solid electrolyte interphases (SEIs) form as reduction products at the electrodes and strongly affect battery performance and safety. Because SEI formation poses a highly nonlinear, complex multi-physics problem over various lengths and time scales, traditional modeling approaches struggle to characterize SEI evolution solely with existing physical properties. To improve the characterization of SEIs, it proposes a data-driven strategy for a virtual material design that learns to represent and characterize SEI formation with physical and data-driven properties from kinetic Monte Carlo simulations. A variational autoencoder with a property regressor learns data-driven properties, which represent SEI configurations and correlate with physical target properties. This new neural network design encodes the high-dimensional structural and reaction spaces into a lower-dimensional latent space, while the property regressor orders the latent space by physical target properties. The model achieves high correlation scores between target and predicted properties from latent representations, thereby proving that the data-driven properties enrich the expressiveness of SEI characterizations. A recent study has developed a parallel regressor variational autoencoder tool that can automate the characterization of solid electrolyte interphase (SEI) in batteries. This tool can synthesize and explain SEI configurations by analyzing data-driven properties encoded with physical properties. The study also investigates the impact of reaction barriers on SEI ***

关键词： kinetic Monte Carlo simulations solid electrolyte interphases variational autoencoder virtual material design

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Knowledge Graph and Personalized Answer Sequences for Programming Knowledge Tracing

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APPLIED SCIENCES-BASEL 2024年第17期14卷 7952页

作者： Pan, Jianguo Dong, Zhengyang Yan, Lijun Cai, Xia Shanghai Normal Univ Coll Informat Mech & Elect Engn Shanghai 200234 Peoples R China

Knowledge tracing is a significant research area in educational data mining, aiming to predict future performance based on students' historical learning data. In the field of programming, several challenges are faced in knowledge tracing, including inaccurate exercise representation and limited student information. These issues can lead to biased models and inaccurate predictions of students' knowledge states. To effectively address these issues, we propose a novel programming knowledge tracing model named GPPKT (Knowledge Graph and Personalized Answer Sequences for Programming Knowledge Tracing), which enhances performance by using knowledge graphs and personalized answer sequences. Specifically, we establish the associations between well-defined knowledge concepts and exercises, incorporating student learning abilities and latent representations generated from personalized answer sequences using variational autoencoders (VAE) in the model. This deep knowledge tracing model employs Long Short-Term Memory (LSTM) networks and attention mechanisms to integrate the embedding vectors, such as exercises and student information. Extensive experiments are conducted on two real-world programming datasets. The results indicate that GPPKT outperforms state-of-the-art methods, achieving an AUC of 0.8840 and an accuracy of 0.8472 on the Luogu dataset, and an AUC of 0.7770 and an accuracy of 0.8799 on the Codeforces dataset. This demonstrates the superiority of the proposed model, with an average improvement of 9.03% in AUC and 2.02% in accuracy across both datasets.

关键词： knowledge graph programming learning variational autoencoder knowledge tracing LSTM

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Few-Shot User-Adaptable Radar-Based Breath Signal Sensing

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SENSORS 2023年第2期23卷 804-804页

作者： Mauro, Gianfranco Diez, Maria De Carlos Ott, Julius Servadei, Lorenzo Cuellar, Manuel P. Morales-Santos, Diego P. Infineon Technol AG Am Campeon 1 15 D-85579 Neubiberg Germany Univ Granada Dept Elect & Comp Technol Ave de Fuente Nueva s n Granada 18071 Spain Tech Univ Munich Dept Elect & Comp Engn Arcis str 21 D-80333 Munich Germany Univ Granada Dept Comp Sci & Artificial Intelligence C Pdta Daniel Saucedo Aranda s n Granada 18015 Spain

Vital signs estimation provides valuable information about an individual's overall health status. Gathering such information usually requires wearable devices or privacy-invasive settings. In this work, we propose a radar-based user-adaptable solution for respiratory signal prediction while sitting at an office desk. Such an approach leads to a contact-free, privacy-friendly, and easily adaptable system with little reference training data. Data from 24 subjects are preprocessed to extract respiration information using a 60 GHz frequency-modulated continuous wave radar. With few training examples, episodic optimization-based learning allows for generalization to new individuals. Episodically, a convolutional variational autoencoder learns how to map the processed radar data to a reference signal, generating a constrained latent space to the central respiration frequency. Moreover, autocorrelation over recorded radar data time assesses the information corruption due to subject motions. The model learning procedure and breathing prediction are adjusted by exploiting the motion corruption level. Thanks to the episodic acquired knowledge, the model requires an adaptation time of less than one and two seconds for one to five training examples, respectively. The suggested approach represents a novel, quickly adaptable, non-contact alternative for office settings with little user motion.

关键词： vital sign sensing respiration signal artificial neural networks meta-learning radar FMCW few-shot learning autocorrelation variational autoencoder signal processing

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