nach oben

Erschienen in:

29.03.2021 | Original Article

A real-time FPGA-based implementation for detection and sorting of bio-signals

verfasst von: Francisco Javier Iniguez-Lomeli, Yannick Bornat, Sylvie Renaud, Jose Hugo Barron-Zambrano, Horacio Rostro-Gonzalez

Erschienen in: Neural Computing and Applications | Ausgabe 18/2021

Einloggen

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

search-config

KI-gestützte Suche

Aus

Abstract

Extracting and analyzing relevant information from bio-signal recordings are complex tasks in which action potential detection and sorting processes take place, moreover if these are performed in real time. In this regard, the present paper introduces real-time FPGA-based architectures for detection and sorting of bio-signals, in particular macaque and human pancreatic signals. Action potential detection is performed by using an adaptive threshold. Also, during this process we have identified six different action potential shapes from the signals, which have been used to classify the action potentials. Our implementation runs at a frequency of 100 MHz with a low resource consumption for both architectures, and action potentials can be also observed in real time during a simulation in an OLED display.

Vorheriger Artikel Linking place records using multi-view encoders

Nächster Artikel Towards achieving a delicate blending between rule-based translator and neural machine translator

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

Alghamdi A, Hammad M, Ugail H, Abdel-Raheem A, Muhammad K, Khalifa HS, Abd El-Latif AA (2020) Detection of myocardial infarction based on novel deep transfer learning methods for urban healthcare in smart cities. Multimedia Tools Appl. https://doi.org/10.1007/s11042-020-08769-xCrossRef

Azami H, Escudero J, Darzi A, Sanei S (2015) Extracellular spike detection from multiple electrode array using novel intelligent filter and ensemble fuzzy decision making. J Neurosci Methods 239:129–138. https://doi.org/10.1016/j.jneumeth.2014.10.006CrossRef

Belitski A, Gretton A, Magri C, Murayama Y, Montemurro MA, Logothetis NK, Panzeri S (2008) Low-frequency local field potentials and spikes in primary visual cortex convey independent visual information. J Neurosci 28(22):5696–5709. https://doi.org/10.1523/JNEUROSCI.0009-08.2008CrossRef

Buzsaki G (2006) Rhythms of the brainy. Oxford University Press, New York. https://doi.org/10.1093/acprof:oso/9780195301069.001.000CrossRefMATH

Gibson S, Judy JW, Markovic D (2010) Technology-aware algorithm design for neural spike detection, feature extraction, and dimensionality reduction. IEEE Trans Neural Syst Rehabil Eng 18(5):469–478. https://doi.org/10.1109/TNSRE.2010.2051683CrossRef

Harrison R (2003) A low-power integrated circuit for adaptive detection of action potentials in noisy signals. In: Proceedings of the 25th annual international conference of the IEEE engineering in medicine and biology society (IEEE Cat. No. 03CH37439) vol 4, pp 3325–3328. https://doi.org/10.1109/IEMBS.2003.1280856

Hwan Choi J, Kyung Jung H, Kim T (2006) A new action potential detector using the MTEO and its effects on spike sorting systems at low signal-to-noise ratios. IEEE Trans Biomed Eng 53(4):738–746. https://doi.org/10.1109/TBME.2006.870239CrossRef

Jing H, He X, Han Q, Abd El-Latif AA, Niu X (2014) Saliency detection based on integrated features. Neurocomputing 129:114–121. https://doi.org/10.1016/j.neucom.2013.02.048CrossRef

Koutsos E, Paraskevopoulou SE, Constandinou TG (2013) A 1.5 \(\mu \)w NEO-based spike detector with adaptive-threshold for calibration-free multichannel neural interfaces. In: Proceedings—IEEE international symposium on circuits and systems, pp 1922–1925. https://doi.org/10.1109/ISCAS.2013.6572243

10.

Lebreton F, Pirog A, Belouah I, Bosco D, Berney T, Meda P, Bornat Y, Catargi B, Renaud S, Raoux M, Lang J (2015) Slow potentials encode intercellular coupling and insulin demand in pancreatic beta cells. Diabetologia 58(6):1291–1299. https://doi.org/10.1007/s00125-015-3558-zCrossRef

11.

Maji P, Mullins R (2018) On the reduction of computational complexity of deep convolutional neural networks. Entropy 20(4):305. https://doi.org/10.3390/e20040305

12.

Mukhopadhyay S, Ray GC (1998) A new interpretation of nonlinear energy operator and its efficacy in spike detection. IEEE Trans Biomed Eng 45(2):180–187CrossRef

13.

Obien MEJ, Deligkaris K, Bullmann T, Bakkum DJ, Frey U (2015) Revealing neuronal function through microelectrode array recordings. Front Neurosci 8:423. https://doi.org/10.3389/fnins.2014.00423CrossRef

14.

Perelman Y, Ginosar R (2007) An integrated system for multichannel neuronal recording with spike/LFP separation, integrated A/D conversion and threshold detection. IEEE Trans Biomed Eng 54(1):130–137. https://doi.org/10.1109/TBME.2006.883732CrossRef

15.

Pirog A, Bornat Y, Renaud S, Perrier R, Jaffredo M, Raoux M, Lang J (2017) A versatile electrode sorting module for MEAs: implementation in a FPGA-based real-time system. In: 2017 IEEE biomedical circuits and systems conference (BioCAS), pp 1–4

16.

Platkiewicz J, Brette R (2010) A threshold equation for action potential initiation. PLoS Comput Biol 6(7):1–16. https://doi.org/10.1371/journal.pcbi.1000850MathSciNetCrossRef

17.

Qin Q, Poularakis K, Iosifidis G, Tassiulas L (2018) SDN controller placement at the edge: optimizing delay and overheads. In: IEEE INFOCOM 2018—IEEE Conference on Computer Communications, pp 684–692. https://doi.org/10.1109/INFOCOM.2018.8485963

18.

Rummens F (2015) Embedded systems for the interfacing of electronics and biology : modeling and designing an analog adaptive detection chain. Theses, Université de Bordeaux. https://tel.archives-ouvertes.fr/tel-01294213. Version corrigée/

19.

Rummens F, Renaud S, Lewis N (2015) CMOS differential neural amplifier with high input impedance. In: 2015 IEEE 13th international new circuits and systems conference (NEWCAS), pp 8–11. https://doi.org/10.1109/NEWCAS.2015.7182037

20.

Silbergleit Lehman T, Hilton AD, Lee BC (2018) Maps: understanding metadata access patterns in secure memory. In: 2018 IEEE international symposium on performance analysis of systems and software (ISPASS), pp 33–43. https://doi.org/10.1109/ISPASS.2018.00012

21.

Strauss W (2000) Digital signal processing. IEEE Signal Process Mag 17(2):52–56. https://doi.org/10.1109/79.826412CrossRef

22.

Takekawa T, Ota K, Murayama M, Fukai T (2014) Spike detection from noisy neural data in linear-probe recordings. Eur J Neurosci 39(11):1943–1950. https://doi.org/10.1111/ejn.12614CrossRef

23.

Wu T, Xu J, Lian Y, Khalili A, Rastegarnia A, Guan C, Yang Z (2016) A 16-channel nonparametric spike detection ASIC based on EC-PC decomposition. IEEE Trans Biomed Circuits Syst 10(1):3–17. https://doi.org/10.1109/TBCAS.2015.2389266CrossRef

24.

Yang Y, Mason AJ (2017) Hardware efficient automatic thresholding for NEO-based neural spike detection. IEEE Trans Biomed Eng 64(4):826–833. https://doi.org/10.1109/TBME.2016.2580319CrossRef

25.

Zhang T, Han Q, El-Latif AAA, Bai X, Niu X (2013) 2-D cartoon character detection based on scalable-shape context and hough voting. Inf Technol J 12:2342–2349CrossRef

Titel: A real-time FPGA-based implementation for detection and sorting of bio-signals
verfasst von: Francisco Javier Iniguez-Lomeli
Yannick Bornat
Sylvie Renaud
Jose Hugo Barron-Zambrano
Horacio Rostro-Gonzalez
Publikationsdatum: 29.03.2021
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 18/2021
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-021-05853-7

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

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

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Weitere Artikel der Ausgabe 18/2021

Optimal radial topology of electric unbalanced and balanced distribution system using improved coyote optimization algorithm for power loss reduction

Primate vision: a single layer perception

A modified farmland fertility optimizer for parameters estimation of fuel cell models

A soft computing-based study on WEDM optimization in processing Inconel 625

Retraction Note To: Artificial neural network to predict the effect of heat treatments on Vickers microhardness of low-carbon Nb microalloyed steels

An extended Pythagorean fuzzy VIKOR method with risk preference and a novel generalized distance measure for multicriteria decision-making problems

Premium Partner