Accelerating Biomedical Signal Processing Using GPU: A Case Study of Snore Sound Feature Extraction

The advent of ‘Big Data’ and ‘Deep Learning’ offers both, a great challenge and a huge opportunity for personalised health-care. In machine learning-based biomedical data analysis, feature extraction is a key step for ‘feeding’ the subsequent classifiers. With increasing numbers of biomedical data,...

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Bibliographic Details
Published inInterdisciplinary sciences : computational life sciences Vol. 9; no. 4; pp. 550 - 555
Main Authors Guo, Jian, Qian, Kun, Zhang, Gongxuan, Xu, Huijie, Schuller, Björn
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2017
Springer Nature B.V
Subjects
Algorithms
Biomedical and Life Sciences
Biomedical data
Case studies
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Appl. in Life Sciences
Data analysis
Data management
Data processing
Feature extraction
Graphics processing units
Health Sciences
Humans
Learning algorithms
Life Sciences
Machine Learning
Mathematical and Computational Physics
Medicine
Original Research Article
Signal processing
Snoring
Sound
Statistics for Life Sciences
Theoretical
Theoretical and Computational Chemistry
Signal processing
Feature extraction
GPU
Biomedical
Python
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Summary:The advent of ‘Big Data’ and ‘Deep Learning’ offers both, a great challenge and a huge opportunity for personalised health-care. In machine learning-based biomedical data analysis, feature extraction is a key step for ‘feeding’ the subsequent classifiers. With increasing numbers of biomedical data, extracting features from these ‘big’ data is an intensive and time-consuming task. In this case study, we employ a Graphics Processing Unit (GPU) via Python to extract features from a large corpus of snore sound data. Those features can subsequently be imported into many well-known deep learning training frameworks without any format processing. The snore sound data were collected from several hospitals (20 subjects, with 770–990 MB per subject – in total 17.20 GB). Experimental results show that our GPU-based processing significantly speeds up the feature extraction phase, by up to seven times, as compared to the previous CPU system.
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ISSN:1913-2751
1867-1462
1867-1462
DOI:10.1007/s12539-017-0232-9