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Jianzhe Zhang Thesis (8.10).pdf (3.2 MB)

ETD Abstract Container

Abstract Header

Development of an Apache Spark-Based Framework for Processing and Analyzing Neuroscience Big Data: Application in Epilepsy Using EEG Signal Data

Zhang, Jianzhe
http://orcid.org/0000-0002-9193-3337
http://rave.ohiolink.edu/etdc/view?acc_num=case1597089028333942

Abstract Details

, Master of Sciences, Case Western Reserve University, EECS - Computer and Information Sciences.
Brain functional connectivity measures are used to study interactions between brain regions in various neurological disorders such as Alzheimer’s Disease and epilepsy. In particular, high-resolution electrophysiological signal data recorded from intracranial electrodes, such as stereotactic electroencephalography (SEEG) signal data, is often used to characterize the properties of brain connectivity in neurological disorders. For example, SEEG data is used to lateralize the epileptogenic zone and characterize seizure networks in epilepsy. However, there are several computational challenges associated with efficient and scalable analysis of signal data in neurological disorders due to the large volume and complexity of signal data. In order to address the challenges associated with processing and analyzing signal datasets, we have developed an integrated platform called Neuro-Integrative Connectivity (NIC) platform that integrates and streamlines multiple data processing and analysis steps into a single tool. In particular, in this thesis we have developed a suite of new approaches covering signal data format, indexing structure, and Apache Spark libraries to support efficient and scalable signal data management for applications in neurological disorders such as epilepsy. Our evaluations demonstrate the utility of Apache Spark in supporting neuroscience Big Data application; however, our results also demonstrate that Apache Spark is not well suited for all types of computational tasks associated with signal data management. Therefore, the overall objective of this thesis is to identify specific computational tasks that benefit from the use of main memory-based Apache Spark methods in neuroscience Big Data applications. The new NIC platform developed in this thesis is a significant resource for the brain connectivity research community as it has applications in real world settings for advancing research in neurological disorders using signal data.
Satya Sahoo (Advisor)
Jing Li (Committee Chair)
An Wang (Committee Member)
82 p.
Bioinformatics; Computer Science
EEG signal data processing, Brain connectivity analysis; Network analysis; Apache Spark; Distributed computing

Recommended Citations

Citations

  • Zhang, J. (2020). Development of an Apache Spark-Based Framework for Processing and Analyzing Neuroscience Big Data: Application in Epilepsy Using EEG Signal Data [Master's thesis, Case Western Reserve University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=case1597089028333942

    APA Style (7th edition)

  • Zhang, Jianzhe. Development of an Apache Spark-Based Framework for Processing and Analyzing Neuroscience Big Data: Application in Epilepsy Using EEG Signal Data. 2020. Case Western Reserve University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=case1597089028333942.

    MLA Style (8th edition)

  • Zhang, Jianzhe. "Development of an Apache Spark-Based Framework for Processing and Analyzing Neuroscience Big Data: Application in Epilepsy Using EEG Signal Data." Master's thesis, Case Western Reserve University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1597089028333942

    Chicago Manual of Style (17th edition)

case1597089028333942
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© 2020, all rights reserved.
This open access ETD is published by Case Western Reserve University School of Graduate Studies and OhioLINK.