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


Peker M., ŞEN B. , GÜRÜLER H.

JOURNAL OF MEDICAL SYSTEMS, cilt.39, sa.2, 2015 (SCI İndekslerine Giren Dergi) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 39 Konu: 2
  • Basım Tarihi: 2015
  • Doi Numarası: 10.1007/s10916-015-0197-3
  • Dergi Adı: JOURNAL OF MEDICAL SYSTEMS

Özet

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.