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Automated Neurons Recognition and Sorting for Diamond Based Microelectrode Arrays Recording: A Feasibility Study
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SYSNO ASEP 0504888 Druh ASEP C - Konferenční příspěvek (mezinárodní konf.) Zařazení RIV D - Článek ve sborníku Název Automated Neurons Recognition and Sorting for Diamond Based Microelectrode Arrays Recording: A Feasibility Study Tvůrce(i) Klempíř, O. (CZ)
Krupička, R. (CZ)
Petráková, V. (CZ)
Krůšek, Jan (FGU-C) RID, ORCID
Dittert, Ivan (FGU-C) ORCID
Taylor, Andrew (FZU-D) RID, ORCIDZdroj.dok. World Congress on Medical Physics and Biomedical Engineering 2018, 2. - Singapore : Springer, 2019 / Lhotská Lenka ; Sukupová Lucie ; Lackovič Igor ; Ibbott Geoffrey S. - ISSN 1680-0737 - ISBN 978-981-10-9037-0 Rozsah stran s. 281-286 Poč.str. 6 s. Forma vydání Tištěná - P Akce World Congress on Medical Physics and Biomedical Engineering 2018 Datum konání 03.06.2018 - 08.06.2018 Místo konání Praha Země CZ - Česká republika Typ akce WRD Jazyk dok. eng - angličtina Země vyd. SG - Singapur Klíč. slova boron doped diamond ; microelectrode arrays ; neural recording ; spike sorting Vědní obor RIV FH - Neurologie, neurochirurgie, neurovědy Obor OECD Neurosciences (including psychophysiology CEP GA17-15319S GA ČR - Grantová agentura ČR Institucionální podpora FGU-C - RVO:67985823 ; FZU-D - RVO:68378271 UT WOS 000449742700052 EID SCOPUS 85048222745 DOI 10.1007/978-981-10-9038-7_52 Anotace Microelectrode arrays (MEA) are extensively used for recording and stimulating neural activity in vitro and in vivo. Depositing nanostructured boron doped diamond (BDD) onto the neuroelectrodes makes it possible to obtain dual mode low-noise neuroelectrical and neurochemical information simultaneously. The signal processing procedure requires finding and distinguishing individual neurons spikes in the recordings. Spike identification is usually done manually which is inaccurate and inappropriate for complex datasets. In this paper, we present a methodology and two algorithms for neurons recognition and evaluation based on unsupervised learning. Forty-five extracellular randomly selected signals from 26 unique measurements of embryonic hippocampal rat neurons (20 kHz, 6 min) were recorded on the commercial 60 TiN channels MEA. The signals were filtered in the 300-3000 Hz band and an amplitude detector (4x std of the background noise) was used for spike detection. WaveClus features were computed and its 3 PCA components were extracted for every spike. The optimal number of clusters were evaluated by an expert rater. K-means + gap criterion (alg. 1) and the Gaussian Mixture Model + Bayesian Information Criterion (alg. 2) were implemented and compared. The total IntraClass Correlation showed a significant inter-rater agreement for all 3 rater procedures (ICC = 0.69, p < 0.001), when post hoc weighted Cohen's Kappas for 2 raters were 0.85 (expert vs. alg. 1, p < 0.001) and 0.62 (expert vs. alg. 2, p < 0.001). This will contribute to the objective definition of dual mode BDD MEA performance criteria and for a comparison with the current system. Pracoviště Fyziologický ústav Kontakt Lucie Trajhanová, lucie.trajhanova@fgu.cas.cz, Tel.: 241 062 400 Rok sběru 2020
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