A parallel genetic algorithm for adaptive hardware and its application to ECG signal classification

This paper presents a parallel genetic algorithm (GA) called the cellular compact genetic algorithm (c-cGA) and its implementation for adaptive hardware. An adaptive hardware based on the c-cGA is proposed to automate real-time classification of ECG signals. The c-cGA not only provides a strong sear...

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Published in:	Neural computing & applications Vol. 22; no. 7-8; pp. 1609 - 1626
Main Authors:	Jewajinda, Yutana, Chongstitvatana, Prabhas
Format:	Journal Article
Language:	English
Published:	London Springer-Verlag 01.06.2013 Springer
Subjects:	Applied sciences Artificial Intelligence Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Computer science; control theory; systems Data Mining and Knowledge Discovery Exact sciences and technology Image Processing and Computer Vision Inference from stochastic processes; time series analysis Learning and adaptive systems Mathematics Multivariate analysis Original Article Parametric inference Probability and statistics Probability and Statistics in Computer Science Sciences and techniques of general use Statistics ECG signal classification Parallel genetic algorithm Adaptive hardware Parallel algorithm Neural computation Adaptive algorithm Adaptive system Field programmable gate array Long term variation Adaptive filter Neural network Real time Long term Implementation Learning Digital filter Genetic algorithm Classification Genetics Monitoring
ISSN:	0941-0643, 1433-3058
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Abstract	This paper presents a parallel genetic algorithm (GA) called the cellular compact genetic algorithm (c-cGA) and its implementation for adaptive hardware. An adaptive hardware based on the c-cGA is proposed to automate real-time classification of ECG signals. The c-cGA not only provides a strong search capability while maintaining genetic diversity using multiple GAs but also has a cellular-like structure and is a straight-forward algorithm suitable for hardware implementation. The c-cGA hardware and an adaptive digital filter structure also perform an adaptive feature selection in real time. The c-cGA is applied to a block-based neural network (BbNN) for online learning in the hardware. Using an adaptive hardware approach based on the c-cGA, an adaptive hardware system for classifying ECG signals is feasible. The proposed adaptive hardware can be implemented in a field programmable gate array (FPGA) for an adaptive embedded system applied to personalised ECG signal classifications for long-term patient monitoring.
AbstractList	This paper presents a parallel genetic algorithm (GA) called the cellular compact genetic algorithm (c-cGA) and its implementation for adaptive hardware. An adaptive hardware based on the c-cGA is proposed to automate real-time classification of ECG signals. The c-cGA not only provides a strong search capability while maintaining genetic diversity using multiple GAs but also has a cellular-like structure and is a straight-forward algorithm suitable for hardware implementation. The c-cGA hardware and an adaptive digital filter structure also perform an adaptive feature selection in real time. The c-cGA is applied to a block-based neural network (BbNN) for online learning in the hardware. Using an adaptive hardware approach based on the c-cGA, an adaptive hardware system for classifying ECG signals is feasible. The proposed adaptive hardware can be implemented in a field programmable gate array (FPGA) for an adaptive embedded system applied to personalised ECG signal classifications for long-term patient monitoring.
Author	Jewajinda, Yutana Chongstitvatana, Prabhas
Author_xml	– sequence: 1 givenname: Yutana surname: Jewajinda fullname: Jewajinda, Yutana email: yutana.jewajinda@nectec.or.th organization: National Electronics and Computer Technology Center, Department of Computer Engineering, Chulalongkorn University – sequence: 2 givenname: Prabhas surname: Chongstitvatana fullname: Chongstitvatana, Prabhas organization: Department of Computer Engineering, Chulalongkorn University
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Cites_doi	10.1109/TEVC.2003.820662 10.1109/TNN.2007.900239 10.1109/4235.797971 10.1109/TBME.2006.883802 10.1109/AHS.2009.46 10.1109/TSP.2006.888883 10.1109/10.623058 10.1109/TIM.2003.816841 10.1109/AHS.2008.13 10.1109/WEAH.2009.4925661 10.1109/CEC.2008.4631161 10.1109/5.784219 10.1109/TITB.2008.923147 10.1007/0-387-31238-2 10.1109/72.914525 10.1161/01.CIR.101.23.e215 10.1109/51.932724 10.1109/TEVC.2002.800880 10.1007/978-3-540-85857-7_32 10.1007/978-3-540-85857-7_3 10.1109/CEC.2007.4424587 10.1007/978-3-540-30217-9_25 10.1109/TBME.2004.824138 10.1109/TEVC.2009.2025032 10.1109/CEC.2006.1688705 10.1080/09540090701725508 10.1007/BF02637023 10.1109/TNN.2007.891626 10.1109/TEVC.2003.814633
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Keywords	ECG signal classification Parallel genetic algorithm Adaptive hardware Parallel algorithm Neural computation Adaptive algorithm Adaptive system Field programmable gate array Long term variation Adaptive filter Neural network Real time Long term Implementation Learning Digital filter Genetic algorithm Classification Genetics Monitoring
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In: Proceeding of IEEE congress on evolutionary computation, pp 3129–3136 MelganiFBaziYClassification of electrocardiogram signals with support vector machines and particle swarm optimizationIEEE Trans Inf Technol Biomed200812566767710.1109/TITB.2008.923147 OsowskiSHoaiLTMarkiewiczTSupport vector machine based expert system for reliable heartbeat recognitionIEEE Trans Biomed Eng200451458258910.1109/TBME.2004.824138 GarrisonWGTyrellAMIntroduction to evolvable hardware: a practical guide for designing self-adaptive systems2007New JerseyWiley-IEEE Press Glette K, Torresen J, Hovin M (2009) Intermediate level FPGA reconfiguration for an online EHW pattern recognition system. 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References_xml	– reference: Teerakittikul P, Tempesti G, Tyrrel AM (2009) The application of evolvable hardware to fault tolerant robot control. In: Proceeding of IEEE workshop on evolvable and adaptive hardware, Nashville, pp 1–8 – reference: Cantu-PazEEfficient and accurate parallel genetic algorithms2000BostonKluwer0963.68164 – reference: Merchant S et al (2006) FPGA implementation of evolvable block-based neural network. In: Proceeding of IEEE congress on evolutionary computation, pp 3129–3136 – reference: WookCRamakrishnaRSElitism-based compact genetic algorithmIEEE Trans Evol Comput20037436738510.1109/TEVC.2003.814633 – reference: ChazalPReillyRBA patient adapting heart beat classifier using ECG morphology and heartbeat interval featuresIEEE Trans Biomed Eng200653122535254310.1109/TBME.2006.883802 – reference: LagunaPJanRCaminalPRixHThakorNVAdaptive estimation of the QRS complex wave in the electrocardiographic signal (ECG) by the Hermite model: classification and ectopic beat detectionJ Med Biol Eng Comput199534586810.1007/BF02637023 – reference: LinhTHOsowskiSStodolskiMOn-line heart beat recognition using Hermite polynomials and neuro-fuzzy networkIEEE Trans Instrum Meas20035241224123110.1109/TIM.2003.816841 – reference: ChazalPO’DwyerMReillyRBAutomatic classification of heartbeats using ECG morphology and heartbeat interval featuresIEEE Trans Biomed Eng200451411961206 – reference: MoodyGBMarkRGThe impact of the MIT-BIH arrhythmia databaseIEEE Eng Med Biol2001203455010.1109/51.932724 – reference: GallagherJCVigrahamSKramerGA family of compact genetic algorithms for intrinsic evolvable hardwareIEEE Trans Evol Comput20048211112610.1109/TEVC.2003.820662 – reference: HiguchiTIwataMLiuYYaoXIntroduction to evolvable hardware2006New YorkSpringer10.1007/0-387-31238-2 – reference: MoonSWKongSGBlock-based neural networksIEEE Trans Neural Netw20011230731710.1109/72.914525 – reference: Ahn CW, Goldberg DE, Ramakrishna RS (2004) Multiple-deme parallel estimation of distribution algorithm. In: Proceeding of parallel processing and applied mathematics, Lect Notes Comput Sci 3019, pp 544–551 – reference: MelganiFBaziYClassification of electrocardiogram signals with support vector machines and particle swarm optimizationIEEE Trans Inf Technol Biomed200812566767710.1109/TITB.2008.923147 – reference: HeJYaoXChenYA novel and practicable on-chip adaptive lossless image compression scheme using intrinsic evolvable hardwareConnect Sci200719428129510.1080/09540090701725508 – reference: GoldbergerALAmaralLANGlassLHausdorffJMIvanovPChMarkRGMietusJEMoodyGBPengCKStanleyHEPhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signalsCirculation200110123e215e22010.1161/01.CIR.101.23.e215 – reference: Mokhtar M, Halliday DM, Tyrell AM (2008) Hippocampus-inspired spiking neural network on FPGA, In: Proceeding of international conference on evolvable systems, Lect Notes Comput Sci, Springer 5216, pp 362–371 – reference: FernandoPRKatkooriSKeymeulenDZebulumRStoicaACustomizable FPGA IP core implementation of a general-purpose genetic algorithm engineIEEE Trans Evol Comput201014113314910.1109/TEVC.2009.2025032 – reference: GarrisonWGTyrellAMIntroduction to evolvable hardware: a practical guide for designing self-adaptive systems2007New JerseyWiley-IEEE Press – reference: AlbaETomassiniMParallelism and evolutionary algorithmsIEEE Trans Evol Comput20026544346310.1109/TEVC.2002.800880 – reference: Salcedo-SanzSCruz-RoldFHeneghanCYaoXEvolutionary design of digital filters with application to sub-band coding and data transmissionIEEE Trans Signal Process200755411931203246498310.1109/TSP.2006.888883 – reference: Mark R, Wallen R (1987) Recommended practice for testing and reporting performance results of ventricular arrhythmia detection algorithm. Association for the Advancement of Medical Instrumentation, Arlington – reference: YaoXEvolving artificial neural networksProc IEEE19998791423144710.1109/5.784219 – reference: TorensenTIncremental evolution of a signal classification hardware architecture for prosthetic hand controlInt J Knowl Intell Eng Syst200812187199 – reference: DelaOssa, L, Gmez JA, Puerta JM (2004) Migration of probability models instead of individuals: an alternative when applying the island model to edas. In: proceeding of international conference on parallel problem solving in nature (PPSN 2004), pp 242–252 – reference: Glette K, Torresen J, Hovin M (2009) Intermediate level FPGA reconfiguration for an online EHW pattern recognition system. In: Proceeding of international conference on adaptive hardware and system, San Francisco, pp 19–26 – reference: HimavathiSFeedforward neural network implementation in FPGA using layer multiplexing for effective resource utilizationIEEE Trans Neural Netw200718388088810.1109/TNN.2007.891626 – reference: HuYPalreddySTompkinsWJA patient-adaptable ECG beat classifier using a mixture of experts approachIEEE Trans Biomed Eng199744989190010.1109/10.623058 – reference: OsowskiSHoaiLTMarkiewiczTSupport vector machine based expert system for reliable heartbeat recognitionIEEE Trans Biomed Eng200451458258910.1109/TBME.2004.824138 – reference: JiangWKongSGBlock-Based neural networks for personalized ECG signal classificationIEEE Trans Neural Netw20071861750176110.1109/TNN.2007.900239 – reference: Shayani H, Bentley PJ, Tyrell AM (2008) Hardware Implementation of a bio-plausible neuron model for evolution and growth of spiking neural networks on FPGA. 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Snippet	This paper presents a parallel genetic algorithm (GA) called the cellular compact genetic algorithm (c-cGA) and its implementation for adaptive hardware. An...
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SubjectTerms	Applied sciences Artificial Intelligence Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Computer science; control theory; systems Data Mining and Knowledge Discovery Exact sciences and technology Image Processing and Computer Vision Inference from stochastic processes; time series analysis Learning and adaptive systems Mathematics Multivariate analysis Original Article Parametric inference Probability and statistics Probability and Statistics in Computer Science Sciences and techniques of general use Statistics
Title	A parallel genetic algorithm for adaptive hardware and its application to ECG signal classification
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