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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| Published in | Interdisciplinary sciences : computational life sciences Vol. 9; no. 4; pp. 550 - 555 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.12.2017
Springer Nature B.V |
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| Abstract | 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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| AbstractList | 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.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. 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. 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. |
| Author | Guo, Jian Zhang, Gongxuan Qian, Kun Xu, Huijie Schuller, Björn |
| Author_xml | – sequence: 1 givenname: Jian surname: Guo fullname: Guo, Jian organization: School of Computer Science and Engineering, Nanjing University of Science Technology – sequence: 2 givenname: Kun surname: Qian fullname: Qian, Kun organization: Department of Electrical and Computer Engineering, MISP group, MMK Technische University Munchen – sequence: 3 givenname: Gongxuan surname: Zhang fullname: Zhang, Gongxuan email: gongxuan@njust.edu.cn organization: School of Computer Science and Engineering, Nanjing University of Science Technology – sequence: 4 givenname: Huijie surname: Xu fullname: Xu, Huijie organization: Department of Otolaryngology, Beijing Hospital – sequence: 5 givenname: Björn surname: Schuller fullname: Schuller, Björn organization: Bjorn Schuller Department of Computing, Machine Learning Group Imperial College London |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/28948531$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1007/s12539-016-0158-7 10.1007/s12539-015-0022-1 10.1109/TBME.2010.2061846 10.1109/MCSE.2011.37 10.1088/0967-3334/34/2/99 10.1109/CSCS.2015.37 10.1016/j.procs.2013.05.164 10.1109/TBME.2016.2619675 10.1111/j.1365-2796.2011.02491.x 10.1145/2647868.2654889 10.1007/s12539-013-0203-8 10.1016/j.smrv.2009.06.002 10.1145/3022670.2976746 10.1056/NEJM199601113340207 10.1109/ICASSP.2016.7471669 10.1007/s11517-012-0885-9 |
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IEEE, pp. 599–604 BastrakovSMeyerovIGergelVGonoskovAGorshkovAEfimenkoEIvanchenkoMKirillinMMalovaAOsipovGHigh performance computing in biomedical applicationsProcedia Comput Sci201318101910.1016/j.procs.2013.05.164 QianKGuoJXuHZhuZZhangGSnore related signals processing in a private cloud computing systemInterdiscip Sci20146321622110.1007/s12539-013-0203-825205499 232_CR19 232_CR18 A Azarbarzin (232_CR15) 2011; 58 232_CR6 J Mesquita (232_CR9) 2012; 50 S Bastrakov (232_CR4) 2013; 18 232_CR22 232_CR21 U Abeyratne (232_CR10) 2013; 34 R Grossman (232_CR3) 2012; 271 232_CR2 PJ Strollo Jr (232_CR7) 1996; 334 232_CR20 232_CR1 K Qian (232_CR5) 2014; 6 232_CR26 232_CR25 J Bergstra (232_CR23) 2011 D Pevernagie (232_CR8) 2010; 14 K Qian (232_CR12) 2017; 64 Z Al-Ameen (232_CR16) 2015; 7 232_CR11 232_CR13 S Walt Van Der (232_CR17) 2011; 13 X Huijie (232_CR14) 2011; 1 F Pedregosa (232_CR24) 2011; 12 |
| References_xml | – reference: AzarbarzinAMoussaviZMAutomatic and unsupervised snore sound extraction from respiratory sound signalsIEEE Trans Biomed Eng20115851156116210.1109/TBME.2010.206184620679022 – reference: StrolloPJJrRogersRMObstructive sleep apneaN Engl J Med199633429910410.1056/NEJM1996011133402078531966 – reference: Al-AmeenZSulongGDeblurring computed tomography medical images using a novel amended landweber algorithmInterdiscip Sci20157331932510.1007/s12539-015-0022-126199211 – reference: GrossmanRWhiteKA vision for a biomedical cloudJ Intern Med201227121221301:CAS:528:DC%2BC38Xjt1Wkurc%3D10.1111/j.1365-2796.2011.02491.x22142244 – reference: G. Van Rossum et al. (2007) Python programming language. In: USENIX Annual Technical Conference. vol. 41 – reference: MesquitaJSolà-SolerJFizJAMoreraJJanéRAll night analysis of time interval between snores in subjects with sleep apnea hypopnea syndromeMed Biol Eng Computing20125043733811:STN:280:DC%2BC38vmt1yjsA%3D%3D10.1007/s11517-012-0885-9 – reference: HuijieXWeiningHYulishengCLSpectral analysis of snoring sound and site of obstruction in obstructive sleep apnea/hypopnea syndromeAm J Audiol Speech Pathol20111009 – reference: BastrakovSMeyerovIGergelVGonoskovAGorshkovAEfimenkoEIvanchenkoMKirillinMMalovaAOsipovGHigh performance computing in biomedical applicationsProcedia Comput Sci201318101910.1016/j.procs.2013.05.164 – reference: AbeyratneUDe SilvaSHukinsCDuceBObstructive sleep apnea screening by integrating snore feature classesPhysiol Meas2013342991:STN:280:DC%2BC3szhs1ersQ%3D%3D10.1088/0967-3334/34/2/9923343563 – reference: PedregosaFVaroquauxGGramfortAMichelVThirionBGriselOBlondelMPrettenhoferPWeissRDubourgVVanderplasJPassosACournapeauDBrucherMPerrotMDuchesnayEScikit-learn: machine learning in pythonJ Mach Learn Res20111228252830 – reference: Y. 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| SubjectTerms | 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 |
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| Title | Accelerating Biomedical Signal Processing Using GPU: A Case Study of Snore Sound Feature Extraction |
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