Methods for the Classification of Biosignals Applied to the Detection of Epileptiform Waveforms and to the Recognition of Physical Activity: Dissertation

Miikka Ermes

    Research output: ThesisDissertation

    1 Citation (Scopus)

    Abstract

    Biosignals are such signals that quantify the physiological processes of a living organism. Classification of biosignals aims at inferring the physiological condition of the organism based on the biosignals obtained from it. In this thesis, the classifications of two biosignals originating from the human body are studied in detail: the electroencephalogram (EEG) and acceleration signals recorded from body-worn sensors (body accelerometry). EEG quantifies the electrical activity of the brain. In this thesis, EEG recorded in hospital operating room and intensive care unit environments is classified to detect epileptiform brain activity which is a potentially brain-damaging phenomenon. Wavelet subband entropy of EEG is shown to be statistically associated with epileptiform activity both in operating room patients under sevoflurane-induced anesthesia and in intensive care unit patients resuscitated after cardiac arrest. The results support the hypothesis that epileptiform activity can be continuously monitored in both clinical settings. Body accelerometry quantifies the movements of the human body with body-worn sensors. In this thesis, body accelerometry is classified for activity recognition purposes, i.e. the purpose is to detect the type of physical activity of the subject from the body acceleration signals. State-of-the-art offline classification results are obtained in two studies. In addition, conversion of the presented offline activity classification algorithms to an online version is demonstrated. The results confirm that multiple classes of daily physical activities and sports can be reliably recognized with body accelerometry.
    Original languageEnglish
    QualificationDoctor Degree
    Awarding Institution
    • Tampere University of Technology (TUT)
    Supervisors/Advisors
    • Värri, Alpo, Supervisor, External person
    Award date15 May 2009
    Place of PublicationEspoo
    Publisher
    Print ISBNs978-951-38-7338-7
    Electronic ISBNs978-951-38-7339-4
    Publication statusPublished - 2009
    MoE publication typeG5 Doctoral dissertation (article)

    Fingerprint

    Accelerometry
    Electroencephalography
    Exercise
    Operating Rooms
    Human Body
    Intensive Care Units
    Brain
    Physiological Phenomena
    Entropy
    Heart Arrest
    Sports
    Anesthesia

    Keywords

    • biosignals
    • classification
    • EEG
    • accelerometers
    • activity recognition

    Cite this

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    title = "Methods for the Classification of Biosignals Applied to the Detection of Epileptiform Waveforms and to the Recognition of Physical Activity: Dissertation",
    abstract = "Biosignals are such signals that quantify the physiological processes of a living organism. Classification of biosignals aims at inferring the physiological condition of the organism based on the biosignals obtained from it. In this thesis, the classifications of two biosignals originating from the human body are studied in detail: the electroencephalogram (EEG) and acceleration signals recorded from body-worn sensors (body accelerometry). EEG quantifies the electrical activity of the brain. In this thesis, EEG recorded in hospital operating room and intensive care unit environments is classified to detect epileptiform brain activity which is a potentially brain-damaging phenomenon. Wavelet subband entropy of EEG is shown to be statistically associated with epileptiform activity both in operating room patients under sevoflurane-induced anesthesia and in intensive care unit patients resuscitated after cardiac arrest. The results support the hypothesis that epileptiform activity can be continuously monitored in both clinical settings. Body accelerometry quantifies the movements of the human body with body-worn sensors. In this thesis, body accelerometry is classified for activity recognition purposes, i.e. the purpose is to detect the type of physical activity of the subject from the body acceleration signals. State-of-the-art offline classification results are obtained in two studies. In addition, conversion of the presented offline activity classification algorithms to an online version is demonstrated. The results confirm that multiple classes of daily physical activities and sports can be reliably recognized with body accelerometry.",
    keywords = "biosignals, classification, EEG, accelerometers, activity recognition",
    author = "Miikka Ermes",
    note = "Project code: 34075",
    year = "2009",
    language = "English",
    isbn = "978-951-38-7338-7",
    series = "VTT Publications",
    publisher = "VTT Technical Research Centre of Finland",
    number = "707",
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    school = "Tampere University of Technology (TUT)",

    }

    Methods for the Classification of Biosignals Applied to the Detection of Epileptiform Waveforms and to the Recognition of Physical Activity : Dissertation. / Ermes, Miikka.

    Espoo : VTT Technical Research Centre of Finland, 2009. 84 p.

    Research output: ThesisDissertation

    TY - THES

    T1 - Methods for the Classification of Biosignals Applied to the Detection of Epileptiform Waveforms and to the Recognition of Physical Activity

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    AU - Ermes, Miikka

    N1 - Project code: 34075

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    Y1 - 2009

    N2 - Biosignals are such signals that quantify the physiological processes of a living organism. Classification of biosignals aims at inferring the physiological condition of the organism based on the biosignals obtained from it. In this thesis, the classifications of two biosignals originating from the human body are studied in detail: the electroencephalogram (EEG) and acceleration signals recorded from body-worn sensors (body accelerometry). EEG quantifies the electrical activity of the brain. In this thesis, EEG recorded in hospital operating room and intensive care unit environments is classified to detect epileptiform brain activity which is a potentially brain-damaging phenomenon. Wavelet subband entropy of EEG is shown to be statistically associated with epileptiform activity both in operating room patients under sevoflurane-induced anesthesia and in intensive care unit patients resuscitated after cardiac arrest. The results support the hypothesis that epileptiform activity can be continuously monitored in both clinical settings. Body accelerometry quantifies the movements of the human body with body-worn sensors. In this thesis, body accelerometry is classified for activity recognition purposes, i.e. the purpose is to detect the type of physical activity of the subject from the body acceleration signals. State-of-the-art offline classification results are obtained in two studies. In addition, conversion of the presented offline activity classification algorithms to an online version is demonstrated. The results confirm that multiple classes of daily physical activities and sports can be reliably recognized with body accelerometry.

    AB - Biosignals are such signals that quantify the physiological processes of a living organism. Classification of biosignals aims at inferring the physiological condition of the organism based on the biosignals obtained from it. In this thesis, the classifications of two biosignals originating from the human body are studied in detail: the electroencephalogram (EEG) and acceleration signals recorded from body-worn sensors (body accelerometry). EEG quantifies the electrical activity of the brain. In this thesis, EEG recorded in hospital operating room and intensive care unit environments is classified to detect epileptiform brain activity which is a potentially brain-damaging phenomenon. Wavelet subband entropy of EEG is shown to be statistically associated with epileptiform activity both in operating room patients under sevoflurane-induced anesthesia and in intensive care unit patients resuscitated after cardiac arrest. The results support the hypothesis that epileptiform activity can be continuously monitored in both clinical settings. Body accelerometry quantifies the movements of the human body with body-worn sensors. In this thesis, body accelerometry is classified for activity recognition purposes, i.e. the purpose is to detect the type of physical activity of the subject from the body acceleration signals. State-of-the-art offline classification results are obtained in two studies. In addition, conversion of the presented offline activity classification algorithms to an online version is demonstrated. The results confirm that multiple classes of daily physical activities and sports can be reliably recognized with body accelerometry.

    KW - biosignals

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    KW - EEG

    KW - accelerometers

    KW - activity recognition

    M3 - Dissertation

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    T3 - VTT Publications

    PB - VTT Technical Research Centre of Finland

    CY - Espoo

    ER -