MOGPTK: The multi-output Gaussian process toolkit

Wolff, Taco de; Cuevas, Alejandro; Tobar, Felipe

doi:10.1016/j.neucom.2020.09.085

Cited by 37 publications

(18 citation statements)

References 3 publications

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“…Training in both tasks was achieved using MOGPTK [44], a PyTorch toolkit for training MOGPs via maximum likelihood. All experiments were run on an 8GB NVIDIA GeForce GTX 1080.…”

Section: Experimental Settingmentioning

confidence: 99%

Modeling Neonatal EEG Using Multi-Output Gaussian Processes

et al. 2022

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Neonatal seizures are sudden events in brain activity with detrimental effects in neurological functions usually related to epileptic fits. Though neonatal seizures can be identified from electroencephalography (EEG), this is a challenging endeavour since expert visual inspection of EEG recordings is time consuming and prone to errors due the data's nonstationarity and low signal-to-noise ratio. Towards the greater aim of automatic clinical decision making and monitoring, we propose a multi-output Gaussian process (MOGP) framework for neonatal EEG modelling. In particular, our work builds on the multi-output spectral mixture (MOSM) covariance kernel and shows that MOSM outperforms other commonly-used covariance functions in the literature when it comes to data imputation and hyperparameter-based seizure detection. To the best of our knowledge, our work is the first attempt at modelling and classifying neonatal EEG using MOGPs. Our main contributions are: i) the development of an MOGP-based framework for neonatal EEG analysis; ii) the experimental validation of the MOSM covariance kernel on real-world neonatal EEG for data imputation; and iii) the design of features for EEG based on MOSM hyperparameters and their validation for seizure detection (classification) in a patient specific approach.

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“…Training in both tasks was achieved using MOGPTK [44], a PyTorch toolkit for training MOGPs via maximum likelihood. All experiments were run on an 8GB NVIDIA GeForce GTX 1080.…”

Section: Experimental Settingmentioning

confidence: 99%

Modeling Neonatal EEG Using Multi-Output Gaussian Processes

et al. 2022

Self Cite

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“…Multi-Output Gaussian Process Toolkit (MOGPTK), our second Gaussian Process approach builds from a Python package for multi-channel data modeling using Gaussian processes (GP) [10,11]. This toolkit aims to address the need for a Multi-output Gaussian Process kernel and provides a natural way to train our model and it is based on the trained model to predict the following pattern.…”

Section: Gaussian Processesmentioning

confidence: 99%

Machine Learning to Summarize and Provide Context for Sleep and Eating Schedules.

Chen

Gao

et al. 2021

Preprint

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The relative timing of sleep and of eating within the circadian day is important for human health. Despite much data on sleep and a growing data set for eating, there remains a need for an interpretative framework for the understanding of this data for health decisions. This study provides a new statistical and machine learning analysis of more than 500 participants in the Daily24 project. From their data, and the analysis, we propose a framework for determining the classification of participants into different chronotypes and with that the ability to realize the potential impact of daily circadian habits on health. We propose that our resulting distribution curves could be used, similar to NHANES (National Health and Nutrition Examination Survey) data for pediatric growth, as a measure for circadian misalignment and used to help guide re-entrainment schedules.

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“…While this normalization is not strictly necessary, it is helpful for the calibration procedure because it reduces the search space of the parameters α; something quite common in machine learning algorithms. 28 Once the raw data have been normalized, we proceed to impute missing values using the Multi-Output Gaussian Process Toolkit (MOGPTK) (de Wolff et al, 2020). This method employs Gaussian processes and neural networks to predict missing values while exploiting the observations of other 'similar' units of analysis of the same indicator.…”

Section: A3 Normalization and Imputationmentioning

confidence: 99%

Subnational Sustainable Development: The Role of Vertical Intergovernmental Transfers in Reaching Multidimensional Goals

Guerrero

Ramos

Trujillo³

et al. 2021

SSRN Journal

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This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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MOGPTK: The multi-output Gaussian process toolkit

Cited by 37 publications

References 3 publications

Modeling Neonatal EEG Using Multi-Output Gaussian Processes

Modeling Neonatal EEG Using Multi-Output Gaussian Processes

Machine Learning to Summarize and Provide Context for Sleep and Eating Schedules.

Subnational Sustainable Development: The Role of Vertical Intergovernmental Transfers in Reaching Multidimensional Goals

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