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Front Neuroinform. 2016 Jun 6;10:18. doi: 10.3389/fninf.2016.00018. eCollection 2016.

NeuroPigPen: A Scalable Toolkit for Processing Electrophysiological Signal Data in Neuroscience Applications Using Apache Pig.

Author information

1
Division of Medical Informatics, School of Medicine, Case Western Reserve UniversityCleveland, OH, USA; Electrical Engineering and Computer Science Department, School of Engineering, Case Western Reserve UniversityCleveland, OH, USA.
2
Electrical Engineering and Computer Science Department, School of Engineering, Case Western Reserve University Cleveland, OH, USA.
3
Division of Medical Informatics, School of Medicine, Case Western Reserve University Cleveland, OH, USA.
4
Department of Neurology, School of Medicine, Case Western Reserve University Cleveland, OH, USA.

Abstract

The recent advances in neurological imaging and sensing technologies have led to rapid increase in the volume, rate of data generation, and variety of neuroscience data. This "neuroscience Big data" represents a significant opportunity for the biomedical research community to design experiments using data with greater timescale, large number of attributes, and statistically significant data size. The results from these new data-driven research techniques can advance our understanding of complex neurological disorders, help model long-term effects of brain injuries, and provide new insights into dynamics of brain networks. However, many existing neuroinformatics data processing and analysis tools were not built to manage large volume of data, which makes it difficult for researchers to effectively leverage this available data to advance their research. We introduce a new toolkit called NeuroPigPen that was developed using Apache Hadoop and Pig data flow language to address the challenges posed by large-scale electrophysiological signal data. NeuroPigPen is a modular toolkit that can process large volumes of electrophysiological signal data, such as Electroencephalogram (EEG), Electrocardiogram (ECG), and blood oxygen levels (SpO2), using a new distributed storage model called Cloudwave Signal Format (CSF) that supports easy partitioning and storage of signal data on commodity hardware. NeuroPigPen was developed with three design principles: (a) Scalability-the ability to efficiently process increasing volumes of data; (b) Adaptability-the toolkit can be deployed across different computing configurations; and (c) Ease of programming-the toolkit can be easily used to compose multi-step data processing pipelines using high-level programming constructs. The NeuroPigPen toolkit was evaluated using 750 GB of electrophysiological signal data over a variety of Hadoop cluster configurations ranging from 3 to 30 Data nodes. The evaluation results demonstrate that the toolkit is highly scalable and adaptable, which makes it suitable for use in neuroscience applications as a scalable data processing toolkit. As part of the ongoing extension of NeuroPigPen, we are developing new modules to support statistical functions to analyze signal data for brain connectivity research. In addition, the toolkit is being extended to allow integration with scientific workflow systems. NeuroPigPen is released under BSD license at: https://sites.google.com/a/case.edu/neuropigpen/.

KEYWORDS:

Apache Pig; MapReduce; data flow language; electrophysiological signal data; neuroscience

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