Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Neural Computing and Applications
Erschienen in: Neural Computing and Applications 8/2020

28.02.2019 | Original Article

FPGA-based system for artificial neural network arrhythmia classification

verfasst von: Hadjer Zairi, Malika Kedir Talha, Karim Meddah, Saliha Ould Slimane

Erschienen in: Neural Computing and Applications | Ausgabe 8/2020

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

The automatic detection and cardiac classification are essential tasks for real-time cardiac diseases diagnosis. In this context, this paper describes a field programmable gates array (FPGA) implementation of arrhythmia recognition system, based on artificial neural network. Firstly, we have developed an optimized software-based medical diagnostic approach, capable of defining the best electrocardiogram (ECG) signal classes. The main advantage of this approach is the significant features minimization, compared to the existing researches, which leads to minimize the FPGA prototype size and saving energy consumption. Secondly, to provide a continuous and mobile arrhythmia monitoring system for patients, we have performed a hardware implementation. The FPGA has been referred due to their easy testing and quick implementation. The optimized approach implementation has been designed on the Nexys4 Artix7 evaluation kit using the Xilinx System Generator for DSP. In order to evaluate the performance of our proposal system, the classification performances of proposed FPGA fixed point have been compared to those obtained from the MATLAB floating point. The proposed architecture is validated on FPGA to be a customized mobile ECG classifier for long-term real-time monitoring of patients.
Vorheriger Artikel Numerical simulation of fractional-order reaction–diffusion equations with the Riesz and Caputo derivatives
Nächster Artikel Supplier selection based on normal process yield: the Bayesian inference

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren
Literatur
1.
Drew BJ, Califf RM, Funk M, Kaufman ES, Krucoff MW, Laks MM, Macfarlane PW, Sommargren C, Swiryn S, Van Hare GF (2004) Practice standards for electrocardiographic monitoring in hospital settings. Circulation 110(17):2721–2746CrossRef
2.
Moavenian M, Khorrami H (2010) A qualitative comparison of artificial neural networks and support vector machines in ECG arrhythmias classification. Expert Syst Appl 37(4):3088–3093CrossRef
3.
Afsar F.A, Arif M, (2008) Robust electrocardiogram beat classification using discrete wavelet transform. In: 2008 2nd international conference on bioinformatics and biomedical engineering, IEEE, pp 1867–1870
4.
Afkhami RG, Azarnia G, Tinati MA (2016) Cardiac arrhythmia classification using statistical and mixture modeling features of ECG signals. Pattern Recognit Lett 70:45–51CrossRef
5.
Elhaj FA, Salim N, Harris AR, Swee TT, Ahmed T (2016) Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals. Comput Methods Programs Biomed 127:52–63CrossRef
6.
Kadbi MH, Hashemi J, Mohseni HR, Maghsoudi A (2006) Classification of ECG arrhythmias based on statistical and time-frequency features. In: IET 3rd international conference on advances in medical, signal and information processing, MEDSIP 2006. IET, pp 1–4
7.
Güler I, Übeyl ED (2005) ECG beat classifier cesigned by combined neural network model. Pattern Recognit 38(2):199–208CrossRef
8.
Thomas M, Das MK, Ari S (2015) Automatic ECG arrhythmia classification using dual tree complex wavelet based features. AEU-Int J Electron Commun 69(4):715–721CrossRef
9.
Rai HM, Trivedi A, Shukla S (2013) ECG signal processing for abnormalities detection using multi-resolution wavelet transform and artificial neural network classifier. Measurement 46(9):3238–3246CrossRef
10.
Homaeinezhad MR, Tavakkoli E, Habibi M, Atyabi SA, Ghaffari A (2011) Combination of different classifiers for cardiac arrhythmia recognition. World Acad Sci Eng Technol 5:1189–1200
11.
Kumari V, Rajesh K (2015) Optimization of multi-layer perceptron neural network using genetic algorithm for arrhythmia classification. J Commun 3:150–157
12.
Diker A, Avci D, Avci E, Gedikpinar M (2019) A new technique for ECG signal classification genetic algorithm wavelet kernel extreme learning machine. Optik 180:46–55CrossRef
13.
Übeyli ED (2008) Support vector machines for detection of electrocardiographic changes in partial epileptic patients. Eng Appl Artif Intell 21(8):1196–1203CrossRef
14.
Jankowski S, Oreziak A, Skorupski A, Kowalski H, Szymanski Z, Piatkowska-Janko E (2003) Computer-aided morphological analysis of Holter ECG recordings based on support vector learning system. Comput Cardiol 30:597–600
15.
Osowski S, Hoai LT, Markiewicz T (2004) Support vector machine-based expert system for reliable heartbeat recognition. IEEE Trans Biomed Eng 51(4):582–589CrossRef
16.
Acr N (2005) Classification of ECG beats by using a fast least square support vector machines with a dynamic programming feature selection algorithm. Neural Comput Appl 14(4):299–309CrossRef
17.
Song MH, Lee J, Cho SP, Lee KJ, Yoo SK (2005) Support vector machine based arrhythmia classification using reduced features. Int J Control Autom Syst 3(4):571
18.
Acr N (2006) A support vector machine classifier algorithm based on a perturbation method and its application to ECG beat recognition systems. Expert Syst Appl 31(1):150–158CrossRef
19.
Mohamed B, Issam A, Mohamed A, Abdellatif B (2015) ECG image classification in real time based on the haar-like features and artificial neural networks. Procedia Comput Sci 73:32–39CrossRef
20.
Sharma J, Kumar V, Ayub S, Saini JP (2012) Uniform sampling of ECG waveform of MIT-BIH normal sinus rhythm database at desired intervals. Int J Comput Appl 50(15):6–9
21.
Wang J-S, Chiang W-C, Hsu Y-L, Yang Y-TC (2013) ECG arrhythmia classification using a probabilistic neural network with a feature reduction method. Neurocomputing 116:38–45CrossRef
22.
Sarkaleh MK, Shahbahrami A (2012) Classification of ECG arrhythmias using discrete wavelet transform and neural networks. Int J Comput Sci Eng Appl 2(1):1
23.
Prasad GK, Sahambi JS (2003) Classification of ECG arrhythmias using multi-resolution analysis and neural networks. In: TENCON 2003. Conference on convergent technologies for the Asia-Pacific region, vol. 1. IEEE, pp 227–231
24.
Güler I, Übeyli ED (2004) Application of adaptive neuro-fuzzy inference system for detection of electrocardiographic changes in patients with partial epilepsy using featur extraction. Expert Syst Appl 27(3):323–330CrossRef
25.
Dokur Z, Ölmez T (2001) ECG beat classification by a novel hybrid neural network. Comput Methods Programs Biomed 66(2–3):167–181CrossRef
26.
Ceylan R (2018) The effect of feature extraction based on dictionary learning on ECG signal classification. Int J Intell Syst Appl Eng 6(1):40–46MathSciNetCrossRef
27.
Minami K-i, Nakajima H, Toyoshima T (1999) Real-time discrimination of ventricular tachyarrhythmia with Fourier-transform neural network. IEEE Trans Biomed Eng 46(2):179–185CrossRef
28.
Gutiërrez-Gnecchi J A, Morfin-Magaña R, Lorias-Espinoza D, del Carmen Tellez-Anguiano A, Reyes-Archundia E (2017) DSP-based arrhythmia classification using wavelet transform and probabilistic neural network. Biomed Signal Process Control 32:44–56CrossRef
29.
Gu X, Zhu Y, Zhou S, Wang C, Qiu M, Wang G (2016) A real-time FPGA-based accelerator for ECG analysis and diagnosis using association-rule mining. ACM Trans Embed Comput Syst (TECS) 15(2):25
30.
Ma Y, Li T, Ma Y, Zhan K (2016) Novel real-time FPGA-based R-wave detection using lifting wavelet. Circuits Syst Signal Process 35(1):281–299MathSciNetMATHCrossRef
31.
Panigrahy D, Rakshit M, Sahu PK (2016) FPGA implementation of heart rate monitoring system. J Med Syst 40(3):1–12CrossRef
32.
Özdemir AT, Danisman K (2011) Fully parallel ANN-based arrhythmia classifier on a single-chip FPGA: FPAAC. Turk J Electr Eng Comput Sci 19(4):667–687
33.
Özdemir AT, Danisman K (2015) A comparative study of two different FPGA-based arrhythmia classifier architectures. Turk J Electr Eng Comput Sci 23(Sup. 1):2016–2089
34.
35.
36.
Subhashrao DP, Halahlli SR (2015) Low complexity algorithm for extraction of ECG fiducial points. Int J Eng Sci Res Technol 1(4):65–74
37.
Dhubkarya DC, Dubey S (2009) High quality audio coding at low bit rate using wavelet and wavelet packet transform. J Theor Appl Inf Technol 6(2):194–200
38.
Vanisree K, Singaraju J (2011) Automatic detection of ECG RR interval using discrete wavelet transformation. Int J Comput Sci Eng (IJCSE) 3(4):1599–1605
39.
40.
Mahmoodabadi S, Ahmadian A, Abolhasani M, Eslami M, Bidgoli J (2006) ECG feature extraction based on multiresolution wavelet transform. In: 27th annual international conference of the engineering in medicine and biology society, 2005. IEEE-EMBS 2005, IEEE, pp 3902–3905
41.
Saritha C, Sukanya V, Murthy YN (2008) Ecg signal analysis using wavelet transforms. Bulg J Phys 35(1):68–77MATH
42.
Silipo R, Marchesi C (1998) Artificial neural networks for automatic ECG analysis. IEEE Trans Signal Process 46(5):1417–1425CrossRef
43.
Bahoura M (2014) FPGA implementation of high-speed neural network for power amplifier behavioral modeling. Analog Integr Circuits Signal Process 79(3):507–527CrossRef
44.
Bishop CM (1995) Neural networks for pattern recognition. Oxford University Press, OxfordMATH
45.
Bahoura M, Ezzaidi H (2012) FPGA-implementation of discrete wavelet transform with application to signal denoising. Circuits Syst Signal Process 31(3):987–1015MathSciNetMATHCrossRef
46.
Matlab (2010) Signal processing blockset 7 users guide. The MathWorks Inc., Natick
47.
Bahoura M, Park C-W (2012) FPGA-implementation of dynamic time delay neural network for power amplifier behavioral modeling. Analog Integr Circuits Signal Process 73(3):819–828CrossRef
48.
Bastos JL, Figueroa HP, Monti A (2006) FPGA implementation of neural network-based controllers for power electronics applications. In: Twenty-first annual IEEE applied power electronics conference and exposition, 2006. APEC’06. IEEE, p 6
Metadaten
Titel
FPGA-based system for artificial neural network arrhythmia classification
verfasst von
Hadjer Zairi
Malika Kedir Talha
Karim Meddah
Saliha Ould Slimane
Publikationsdatum
28.02.2019
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 8/2020
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-019-04081-4

Weitere Artikel der Ausgabe 8/2020

Neural Computing and Applications 8/2020 Zur Ausgabe

Review Article

Word sense disambiguation for Punjabi language using deep learning techniques

Original Article

Imbalanced dataset-based echo state networks for anomaly detection

Original Article

Fault detection in distillation column using NARX neural network

Original Article

Large-margin Distribution Machine-based regression

Original Article

Exploring nested ensemble learners using overproduction and choose approach for churn prediction in telecom industry

Original Article

Multivariate Gaussian and Student-t process regression for multi-output prediction