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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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Training variational autoencoders with Discrete Latent Variables Using Importance Sampling 27

Training Variational Autoencoders with Discrete Latent Varia...

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27th European Signal Processing Conference (EUSIPCO)

作者： Bartler, Alexander Wiewel, Felix Mauch, Lukas Yang, Bin Univ Stuttgart Inst Signal Proc & Syst Theory Stuttgart Germany

ISBN: (纸本)9789082797039

The variational autoencoder (VAE) is a popular generative latent variable model that is often used for representation learning. Standard VAEs assume continuous-valued latent variables and are trained by maximization of the evidence lower bound (ELBO). Conventional methods obtain a differentiable estimate of the ELBO with reparametrized sampling and optimize it with Stochastic Gradient Descend (SGD). However, this is not possible if we want to train VAEs with discrete-valued latent variables, since reparametrized sampling is not possible. In this paper, we propose an easy method to train VAEs with binary or categorically valued latent representations. Therefore, we use a differentiable estimator for the ELBO which is based on importance sampling. In experiments, we verify the approach and train two different VAEs architectures with Bernoulli and categorically distributed latent representations on two different benchmark datasets.

关键词： variational autoencoder discrete latent variables importance sampling

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Deep variational autoencoders for breast cancer tissue modeling and synthesis in SFDI 7

Deep variational autoencoders for breast cancer tissue model...

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Conference on Diffuse Optical Spectroscopy and Imaging VII held at European Conferences on Biomedical Optics

作者： Pardo, Arturo Lopez-Higuera, Jose M. Pogue, Brian W. Conde, Olga M. Univ Cantabria Photon Engn Grp GIF TEISA Dept Edificio IDi TelecomuniacAvda Castros S-N E-39005 Santander Cantabria Spain Inst Invest Sanitaria Valdecilla IDIVAL Santander 39011 Cantabria Spain Biomed Res Networking Ctr Bioengn Nanomat & Nanos Ave Monforte de Lemos3-5 Pabellon 11Planta 0 Madrid 28029 Spain Dartmouth Coll Thayer Sch Engn Hanover NH 03755 USA

ISBN: (数字)9781510628427

ISBN: (纸本)9781510628410;9781510628427

Extracting pathology information embedded within surface optical properties in Spatial Frequency Domain Imaging (SFDI) datasets is still a rather cumbersome nonlinear translation problem, mainly constrained by intrasample and interpatient variability, as well as dataset size. The beta-variational autoencoder (beta-VAE) is a rather novel dimensionality reduction technique where a tractable set of latent low-dimensional embeddings can be obtained from a given dataset. These embeddings can then be sampled to synthesize new data, providing further insight into pathology variability as well as differentiability in terms of optical properties. Its applications for data classification and breast margin delineation are also discussed.

关键词： Deep learning modulated imaging optical properties spatial frequency domain imaging breast cancer variational autoencoder turbid media

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Simpler rather than Challenging: Design of Non-Dyadic Human-Robot Collaboration to Mediate Human-Human Concurrent Tasks 23

Simpler rather than Challenging: Design of Non-Dyadic Human-...

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Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems

作者： Francesco Semeraro Jon Carberry Angelo Cangelosi The University of Manchester Manchester United Kingdom BAE Systems (Operations) Ltd. Warton United Kingdom

ISBN: (纸本)9781450394321

Human-robot interaction (HRI) is progressively addressing multi-party scenarios, where a robot interacts with more than one human user at the same time. Conversely, research in this area is still at an early stage for human-robot collaboration (HRC). The intervention of a robot in human collaboration could be helpful to handle mutual disturbances of workers operating at the same time on the same target object. Therefore, this work outlines design methodologies of non-dyadic human-robot collaborations to address concurrent human-human tasks in manufacturing applications. After this, preliminary results regarding a robotic agent's high-level understanding of such scenarios realised through a variational autoencoder trained by means of transfer learning are shown.

关键词： multi-party human-robot collaboration transfer learning deep learning concurrent tasks non-dyadic human-robot collaboration multi-user activity recognition variational autoencoder

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A Semi-Supervised and Incremental Modeling Framework for Wafer Map Classification

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IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING 2020年第1期33卷 62-71页

作者： Kong, Yuting Ni, Dong Zhejiang Univ Coll Control Sci & Engn Hangzhou 310000 Peoples R China Zhejiang Univ State Key Lab Ind Control Technol Hangzhou 310000 Peoples R China

Wafer map analysis provides critical information for quality control and yield improvement tasks in semiconductor manufacturing. In particular, wafer patterns of gross failing areas (GFA) are important clues to identify the causes of relevant failures during the manufacturing process. In this work, a semi-supervised classification framework is proposed for wafer map analysis, and its application to wafer bin maps with GFA patterns classification is demonstrated. The Ladder network and the semi-supervised variational autoencoder are adopted to classify wafer bin maps in comparison with a standard convolutional neural network (CNN) model on two real-world datasets. The results have illustrated that two semi-supervised models are consistently and substantially better than the CNN model across various training data percentages by effective utilization of the unlabeled data. Active learning and pseudo labeling are also utilized to accelerate the learning curve.

关键词： Wafer map semi-supervised classification ladder network variational autoencoder semiconductor manufacturing

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Cardiac Segmentation With Strong Anatomical Guarantees

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2020年第11期39卷 3703-3713页

作者： Painchaud, Nathan Skandarani, Youssef Judge, Thierry Bernard, Olivier Lalande, Alain Jodoin, Pierre-Marc Univ Sherbrooke Dept Comp Sci Sherbrooke PQ J1K 2R1 Canada Univ Bourgogne Franche Comte ImVIA Lab F-21078 Dijon France CASIS F-21000 Dijon France Univ Claude Bernard Lyon 1 Univ Lyon INSA Lyon CNRSUJM St EtienneINSERMCREATISUMR 5220U1206 F-69621 Lyon France

Convolutional neural networks (CNN) have had unprecedented success in medical imaging and, in particular, in medical image segmentation. However, despite the fact that segmentation results are closer than ever to the inter-expert variability, CNNs are not immune to producing anatomically inaccurate segmentations, even when built upon a shape prior. In this paper, we present a framework for producing cardiac image segmentation maps that are guaranteed to respect pre-defined anatomical criteria, while remaining within the inter-expert variability. The idea behind our method is to use a well-trained CNN, have it process cardiac images, identify the anatomically implausible results and warp these results toward the closest anatomically valid cardiac shape. This warping procedure is carried out with a constrained variational autoencoder (cVAE) trained to learn a representation of valid cardiac shapes through a smooth, yet constrained, latent space. With this cVAE, we can project any implausible shape into the cardiac latent space and steer it toward the closest correct shape. We tested our framework on short-axis MRI as well as apical two and four-chamber view ultrasound images, two modalities for which cardiac shapes are drastically different. With our method, CNNs can now produce results that are both within the inter-expert variability and always anatomically plausible without having to rely on a shape prior.

关键词： CNN variational autoencoder cardiac segmentation MRI ultrasound

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A Preliminary Study of a Two-Stage Paradigm for Preserving Speaker Identity in Dysarthric Voice Conversion 22

A Preliminary Study of a Two-Stage Paradigm for Preserving S...

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Interspeech Conference

作者： Huang, Wen-Chin Kobayashi, Kazuhiro Peng, Yu-Huai Liu, Ching-Feng Tsao, Yu Wang, Hsin-Min Toda, Tomoki Nagoya Univ Nagoya Aichi Japan Acad Sinica Taipei Taiwan Chi Mei Hosp Tainan Taiwan

ISBN: (纸本)9781713836902

We propose a new paradigm for maintaining speaker identity in dysarthric voice conversion (DVC). The poor quality of dysarthric speech can be greatly improved by statistical VC, but as the normal speech utterances of a dysarthria patient are nearly impossible to collect, previous work failed to recover the individuality of the patient. In light of this, we suggest a novel, two-stage approach for DVC, which is highly flexible in that no normal speech of the patient is required. First, a powerful parallel sequence-to-sequence model converts the input dysarthric speech into a normal speech of a reference speaker as an intermediate product, and a nonparallel, frame-wise VC model realized with a variational autoencoder then converts the speaker identity of the reference speech back to that of the patient while assumed to be capable of preserving the enhanced quality. We investigate several design options. Experimental evaluation results demonstrate the potential of our approach to improving the quality of the dysarthric speech while maintaining the speaker identity.

关键词： dysarthric voice conversion sequence-to-sequence modeling nonparallel voice conversion variational autoencoder

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A VAE Conversion Method for Private Data Linkage 26

A VAE Conversion Method for Private Data Linkage

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26th IEEE Pacific Rim International Symposium on Dependable Computing (PRDC)

作者： Tai, Bo-Chen Li, Szu-Chuang Huang, Yennun Acad Sinica Res Ctr Informat Technol Innovat Taipei Taiwan Tamkang Univ Dept Informat Commun New Taipei Taiwan

ISBN: (纸本)9781665424769

Data linkage plays a crucial role in realizing big data's value but is often regarded as a threat to personal privacy. Regulations like GDPR requires users' consent on each specific use of data, which is not practical for data analyzers. In this study, we propose a way to address the problem by having a trustworthy third party collect data from two or more parties, then use the data to train one or more variational autoencoder (VAE) models to remove privacy and send them to the data providers. Using this model, the users express their consent to share data with a trustworthy party. The third party links data from various datasets together to build a variational autoencoder model that allows all parties to generate datasets with full attributes without revealing sensitive personal data. System architectures and machine learning accuracy of generated data sets are measured in this study.

关键词： data linkage synthetic data variational autoencoder

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Deep probabilistic time series forecasting using augmented recurrent input for dynamic systems

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2022年 177卷 1页

作者： Liu, Haitao Liu, Changjun Jiang, Xiaomo Chen, Xudong Yang, Shuhua Wang, Xiaofang Dalian Univ Technol Sch Energy & Power Engn Dalian 116024 Peoples R China Dalian Univ Technol Digital Twin Lab Ind Equipment Dalian 116024 Peoples R China Shenyang Blower Works Grp Corp Shenyang 110869 Peoples R China

The demand of probabilistic time series forecasting has been recently raised in various dynamic system scenarios, for example, system identification and prognostic and health management of machines. To this end, we combine the advances in both deep generative models and state space model (SSM) to come up with a novel, data-driven deep probabilistic sequence model. Specifically, we follow the popular encoder-decoder generative structure to build the recurrent neural networks (RNN) assisted variational sequence model on an augmented recurrent input space, which could induce rich stochastic sequence dependency. Besides, in order to alleviate the inconsistency issue of the posterior between training and predicting as well as improving the mining of dynamic patterns, we (i) propose using a lagged hybrid output as input for the posterior at next time step, which brings training and predicting into alignment;and (ii) further devise a generalized auto-regressive strategy that encodes all the historical dependencies for the posterior. Thereafter, we first investigate the methodological characteristics of the proposed deep probabilistic sequence model on toy cases, and then comprehensively demonstrate the superiority of our model against existing deep probabilistic SSM models through extensive numerical experiments on eight system identification benchmarks from various dynamic systems. Finally, we apply our sequence model to a real-world centrifugal compressor forecasting problem, and again verify its outstanding performance by quantifying the time series predictive distribution.

关键词： State space model Dynamic system Recurrent neural networks variational inference variational autoencoder Compressor

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