Towards an Online Seizure Advisory System - An Adaptive Seizure Prediction Framework Using Active Learning Heuristics

Vignesh Raja Karuppiah Ramachandran (Corresponding Author), Huibert J. Alblas, Duc V. Le, Nirvana Meratnia

    Research output: Contribution to journalArticleAcademicpeer-review

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    Abstract

    In the last decade, seizure prediction systems have gained a lot of attention because of their enormous potential to largely improve the quality-of-life of the epileptic patients. The accuracy of the prediction algorithms to detect seizure in real-world applications is largely limited because the brain signals are inherently uncertain and affected by various factors, such as environment, age, drug intake, etc., in addition to the internal artefacts that occur during the process of recording the brain signals. To deal with such ambiguity, researchers transitionally use active learning, which selects the ambiguous data to be annotated by an expert and updates the classification model dynamically. However, selecting the particular data from a pool of large ambiguous datasets to be labelled by an expert is still a challenging problem. In this paper, we propose an active learning-based prediction framework that aims to improve the accuracy of the prediction with a minimum number of labelled data. The core technique of our framework is employing the Bernoulli-Gaussian Mixture model (BGMM) to determine the feature samples that have the most ambiguity to be annotated by an expert. By doing so, our approach facilitates expert intervention as well as increasing medical reliability. We evaluate seven different classifiers in terms of the classification time and memory required. An active learning framework built on top of the best performing classifier is evaluated in terms of required annotation effort to achieve a high level of prediction accuracy. The results show that our approach can achieve the same accuracy as a Support Vector Machine (SVM) classifier using only 20% of the labelled data and also improve the prediction accuracy even under the noisy condition.
    Original languageEnglish
    Article number1698
    Number of pages30
    JournalSensors (Switserland)
    Volume18
    Issue number6
    DOIs
    Publication statusPublished - 24 May 2018

    Fingerprint

    seizures
    Problem-Based Learning
    Adaptive systems
    learning
    Seizures
    classifiers
    predictions
    Classifiers
    Brain
    ambiguity
    Artifacts
    brain
    Quality of Life
    Research Personnel
    annotations
    Pharmaceutical Preparations
    Support vector machines
    Heuristics
    artifacts
    drugs

    Keywords

    • Health-care technology
    • EEG
    • Epilepsy
    • Signal processing
    • Machine Learning
    • Seizure Prediction
    • Implantable body sensor networks

    Cite this

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    title = "Towards an Online Seizure Advisory System - An Adaptive Seizure Prediction Framework Using Active Learning Heuristics",
    abstract = "In the last decade, seizure prediction systems have gained a lot of attention because of their enormous potential to largely improve the quality-of-life of the epileptic patients. The accuracy of the prediction algorithms to detect seizure in real-world applications is largely limited because the brain signals are inherently uncertain and affected by various factors, such as environment, age, drug intake, etc., in addition to the internal artefacts that occur during the process of recording the brain signals. To deal with such ambiguity, researchers transitionally use active learning, which selects the ambiguous data to be annotated by an expert and updates the classification model dynamically. However, selecting the particular data from a pool of large ambiguous datasets to be labelled by an expert is still a challenging problem. In this paper, we propose an active learning-based prediction framework that aims to improve the accuracy of the prediction with a minimum number of labelled data. The core technique of our framework is employing the Bernoulli-Gaussian Mixture model (BGMM) to determine the feature samples that have the most ambiguity to be annotated by an expert. By doing so, our approach facilitates expert intervention as well as increasing medical reliability. We evaluate seven different classifiers in terms of the classification time and memory required. An active learning framework built on top of the best performing classifier is evaluated in terms of required annotation effort to achieve a high level of prediction accuracy. The results show that our approach can achieve the same accuracy as a Support Vector Machine (SVM) classifier using only 20{\%} of the labelled data and also improve the prediction accuracy even under the noisy condition.",
    keywords = "Health-care technology, EEG, Epilepsy, Signal processing, Machine Learning, Seizure Prediction, Implantable body sensor networks",
    author = "{Karuppiah Ramachandran}, {Vignesh Raja} and Alblas, {Huibert J.} and Le, {Duc V.} and Nirvana Meratnia",
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    doi = "10.3390/s18061698",
    language = "English",
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    Towards an Online Seizure Advisory System - An Adaptive Seizure Prediction Framework Using Active Learning Heuristics. / Karuppiah Ramachandran, Vignesh Raja (Corresponding Author); Alblas, Huibert J.; Le, Duc V.; Meratnia, Nirvana .

    In: Sensors (Switserland), Vol. 18, No. 6, 1698, 24.05.2018.

    Research output: Contribution to journalArticleAcademicpeer-review

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    AU - Karuppiah Ramachandran, Vignesh Raja

    AU - Alblas, Huibert J.

    AU - Le, Duc V.

    AU - Meratnia, Nirvana

    PY - 2018/5/24

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    N2 - In the last decade, seizure prediction systems have gained a lot of attention because of their enormous potential to largely improve the quality-of-life of the epileptic patients. The accuracy of the prediction algorithms to detect seizure in real-world applications is largely limited because the brain signals are inherently uncertain and affected by various factors, such as environment, age, drug intake, etc., in addition to the internal artefacts that occur during the process of recording the brain signals. To deal with such ambiguity, researchers transitionally use active learning, which selects the ambiguous data to be annotated by an expert and updates the classification model dynamically. However, selecting the particular data from a pool of large ambiguous datasets to be labelled by an expert is still a challenging problem. In this paper, we propose an active learning-based prediction framework that aims to improve the accuracy of the prediction with a minimum number of labelled data. The core technique of our framework is employing the Bernoulli-Gaussian Mixture model (BGMM) to determine the feature samples that have the most ambiguity to be annotated by an expert. By doing so, our approach facilitates expert intervention as well as increasing medical reliability. We evaluate seven different classifiers in terms of the classification time and memory required. An active learning framework built on top of the best performing classifier is evaluated in terms of required annotation effort to achieve a high level of prediction accuracy. The results show that our approach can achieve the same accuracy as a Support Vector Machine (SVM) classifier using only 20% of the labelled data and also improve the prediction accuracy even under the noisy condition.

    AB - In the last decade, seizure prediction systems have gained a lot of attention because of their enormous potential to largely improve the quality-of-life of the epileptic patients. The accuracy of the prediction algorithms to detect seizure in real-world applications is largely limited because the brain signals are inherently uncertain and affected by various factors, such as environment, age, drug intake, etc., in addition to the internal artefacts that occur during the process of recording the brain signals. To deal with such ambiguity, researchers transitionally use active learning, which selects the ambiguous data to be annotated by an expert and updates the classification model dynamically. However, selecting the particular data from a pool of large ambiguous datasets to be labelled by an expert is still a challenging problem. In this paper, we propose an active learning-based prediction framework that aims to improve the accuracy of the prediction with a minimum number of labelled data. The core technique of our framework is employing the Bernoulli-Gaussian Mixture model (BGMM) to determine the feature samples that have the most ambiguity to be annotated by an expert. By doing so, our approach facilitates expert intervention as well as increasing medical reliability. We evaluate seven different classifiers in terms of the classification time and memory required. An active learning framework built on top of the best performing classifier is evaluated in terms of required annotation effort to achieve a high level of prediction accuracy. The results show that our approach can achieve the same accuracy as a Support Vector Machine (SVM) classifier using only 20% of the labelled data and also improve the prediction accuracy even under the noisy condition.

    KW - Health-care technology

    KW - EEG

    KW - Epilepsy

    KW - Signal processing

    KW - Machine Learning

    KW - Seizure Prediction

    KW - Implantable body sensor networks

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    JO - Sensors (Switserland)

    JF - Sensors (Switserland)

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