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Register a new userPediatric Automatic Sleep Staging: A comparative study of state-of-the-art deep learning methods
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Despite the tremendous progress recently made towards automatic sleep staging in adults, it is currently known if the most advanced algorithms generalize to the pediatric population, which displays distinctive characteristics in overnight polysomnography (PSG). To answer the question, in this work, we conduct a large-scale comparative study on the state-of-the-art deep learning methods for pediatric automatic sleep staging. A selection of six different deep neural networks with diverging features are adopted to evaluate a sample of more than 1,200 children across a wide spectrum of obstructive sleep apnea (OSA) severity. Our experimental results show that the performance of automated pediatric sleep staging when evaluated on new subjects is equivalent to the expert-level one reported on adults, reaching an overall accuracy of 87.0%, a Cohens kappa of 0.829, and a macro F1-score of 83.5% in case of single-channel EEG. The performance is further improved when dual-channel EEG$cdot$EOG are used, reaching an accuracy of 88.2%, a Cohens kappa of 0.844, and a macro F1-score of 85.1%. The results also show that the studied algorithms are robust to concept drift when the training and test data were recorded 7-months apart. Detailed analyses further demonstrate almost perfect agreement between the automatic scorers to one another and their similar behavioral patterns on the staging errors.
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Background: Despite recent significant progress in the development of automatic sleep staging methods, building a good model still remains a big challenge for sleep studies with a small cohort due to the data-variability and data-inefficiency issues. This work presents a deep transfer learning approach to overcome these issues and enable transferring knowledge from a large dataset to a small cohort for automatic sleep staging. Methods: We start from a generic end-to-end deep learning framework for sequence-to-sequence sleep staging and derive two networks as the means for transfer learning. The networks are first trained in the source domain (i.e. the large database). The pretrained networks are then finetuned in the target domain (i.e. the small cohort) to complete knowledge transfer. We employ the Montreal Archive of Sleep Studies (MASS) database consisting of 200 subjects as the source domain and study deep transfer learning on three different target domains: the Sleep Cassette subset and the Sleep Telemetry subset of the Sleep-EDF Expanded database, and the Surrey-cEEGrid database. The target domains are purposely adopted to cover different degrees of data mismatch to the source domains. Results: Our experimental results show significant performance improvement on automatic sleep staging on the target domains achieved with the proposed deep transfer learning approach. Conclusions: These results suggest the efficacy of the proposed approach in addressing the above-mentioned data-variability and data-inefficiency issues. Significance: As a consequence, it would enable one to improve the quality of automatic sleep staging models when the amount of data is relatively small. The source code and the pretrained models are available at http://github.com/pquochuy/sleep_transfer_learning.
Many sleep studies suffer from the problem of insufficient data to fully utilize deep neural networks as different labs use different recordings set ups, leading to the need of training automated algorithms on rather small databases, whereas large annotated databases are around but cannot be directly included into these studies for data compensation due to channel mismatch. This work presents a deep transfer learning approach to overcome the channel mismatch problem and transfer knowledge from a large dataset to a small cohort to study automatic sleep staging with single-channel input. We employ the state-of-the-art SeqSleepNet and train the network in the source domain, i.e. the large dataset. Afterwards, the pretrained network is finetuned in the target domain, i.e. the small cohort, to complete knowledge transfer. We study two transfer learning scenarios with slight and heavy channel mismatch between the source and target domains. We also investigate whether, and if so, how finetuning entirely or partially the pretrained network would affect the performance of sleep staging on the target domain. Using the Montreal Archive of Sleep Studies (MASS) database consisting of 200 subjects as the source domain and the Sleep-EDF Expanded database consisting of 20 subjects as the target domain in this study, our experimental results show significant performance improvement on sleep staging achieved with the proposed deep transfer learning approach. Furthermore, these results also reveal the essential of finetuning the feature-learning parts of the pretrained network to be able to bypass the channel mismatch problem.
Recent approaches for predicting layouts from 360 panoramas produce excellent results. These approaches build on a common framework consisting of three steps: a pre-processing step based on edge-based alignment, prediction of layout elements, and a post-processing step by fitting a 3D layout to the layout elements. Until now, it has been difficult to compare the methods due to multiple different design decisions, such as the encoding network (e.g. SegNet or ResNet), type of elements predicted (e.g. corners, wall/floor boundaries, or semantic segmentation), or method of fitting the 3D layout. To address this challenge, we summarize and describe the common framework, the variants, and the impact of the design decisions. For a complete evaluation, we also propose extended annotations for the Matterport3D dataset [3], and introduce two depth-based evaluation metrics.
Recent progress on intelligent fault diagnosis has greatly depended on the deep learning and plenty of labeled data. However, the machine often operates with various working conditions or the target task has different distributions with the collected data used for training (we called the domain shift problem). This leads to the deep transfer learning based (DTL-based) intelligent fault diagnosis which attempts to remit this domain shift problem. Besides, the newly collected testing data are usually unlabeled, which results in the subclass DTL-based methods called unsupervised deep transfer learning based (UDTL-based) intelligent fault diagnosis. Although it has achieved huge development in the field of fault diagnosis, a standard and open source code framework and a comparative study for UDTL-based intelligent fault diagnosis are not yet established. In this paper, commonly used UDTL-based algorithms in intelligent fault diagnosis are integrated into a unified testing framework and the framework is tested on five datasets. Extensive experiments are performed to provide a systematically comparative analysis and the benchmark accuracy for more comparable and meaningful further studies. To emphasize the importance and reproducibility of UDTL-based intelligent fault diagnosis, the testing framework with source codes will be released to the research community to facilitate future research. Finally, comparative analysis of results also reveals some open and essential issues in DTL for intelligent fault diagnosis which are rarely studied including transferability of features, influence of backbones, negative transfer, and physical priors. In summary, the released framework and comparative study can serve as an extended interface and the benchmark results to carry out new studies on UDTL-based intelligent fault diagnosis. The code framework is available at https://github.com/ZhaoZhibin/UDTL.
Lattice models consisting of high-dimensional local degrees of freedom without global particle-number conservation constitute an important problem class in the field of strongly correlated quantum many-body systems. For instance, they are realized in electron-phonon models, cavities, atom-molecule resonance models, or superconductors. In general, these systems elude a complete analytical treatment and need to be studied using numerical methods where matrix-product states (MPS) provide a flexible and generic ansatz class. Typically, MPS algorithms scale at least quadratic in the dimension of the local Hilbert spaces. Hence, tailored methods, which truncate this dimension, are required to allow for efficient simulations. Here, we describe and compare three state-of-the-art MPS methods each of which exploits a different approach to tackle the computational complexity. We analyze the properties of these methods for the example of the Holstein model, performing high-precision calculations as well as a finite-size-scaling analysis of relevant ground-state obervables. The calculations are performed at different points in the phase diagram yielding a comprehensive picture of the different approaches.
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