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Rapid Automated Classification of Anesthetic Depth Levels using GPU Based Parallelization of Neural Networks

  • Transactional Processing Systems
  • Published:
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Abstract

The effect of anesthesia on the patient is referred to as depth of anesthesia. Rapid classification of appropriate depth level of anesthesia is a matter of great importance in surgical operations. Similarly, accelerating classification algorithms is important for the rapid solution of problems in the field of biomedical signal processing. However numerous, time-consuming mathematical operations are required when training and testing stages of the classification algorithms, especially in neural networks. In this study, to accelerate the process, parallel programming and computing platform (Nvidia CUDA) facilitates dramatic increases in computing performance by harnessing the power of the graphics processing unit (GPU) was utilized. The system was employed to detect anesthetic depth level on related electroencephalogram (EEG) data set. This dataset is rather complex and large. Moreover, the achieving more anesthetic levels with rapid response is critical in anesthesia. The proposed parallelization method yielded high accurate classification results in a faster time.

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Acknowledgments

Our thanks to Abdulkadir but from the Department of Anesthesiology and Reanimation in Yildirim Beyazit University, Turkey who provided the anesthesia dataset.

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Authors and Affiliations

  1. Department of Information Technologies, Samandira Vocational and Technical School, Istanbul, Turkey

    Musa Peker

  2. Department of Computer Engineering, Yildirim Beyazit University, Ulus, Ankara, Turkey

    Baha Şen

  3. Department of Information Systems Engineering, Faculty of Technology, Mugla Sitki Kocman University, Mugla, Turkey

    Hüseyin Gürüler

Authors
  1. Musa Peker
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  2. Baha Şen
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  3. Hüseyin Gürüler
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Correspondence to Hüseyin Gürüler.

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This article is part of the Topical Collection on Transactional Processing Systems

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Peker, M., Şen, B. & Gürüler, H. Rapid Automated Classification of Anesthetic Depth Levels using GPU Based Parallelization of Neural Networks. J Med Syst 39, 18 (2015). https://doi.org/10.1007/s10916-015-0197-3

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  • DOI: https://doi.org/10.1007/s10916-015-0197-3

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