Automated Cell-Type Classification and Death-Detection of Spinal Motoneurons

Mai Gamal; Mohamed H. Mousa; Seif Eldawlatly; Sherif M. Elbasiouny

doi:10.1109/CIBEC.2018.8641824

Automated Cell-Type Classification and Death-Detection of Spinal Motoneurons

Mai Gamal, Mohamed H. Mousa, Seif Eldawlatly, Sherif M. Elbasiouny

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Spinal motoneurons (MNs) play a crucial role in movement control. Decoding the firing activity of spinal MNs could help in real-life challenges, such as enhancing the control of myoelectric prostheses and diagnosing neurodegenerative diseases. In this paper, we propose a machine learning approach to automatically classify MNs based on their firing activity. Applying the proposed approach to data from a MN computational model, the classification accuracy of all examined datasets exceeded 95%. We extended the approach to detecting the death of a given MN type using clustering validity index. Results indicated that 86% of the examined death-detection cases were detected accurately. These results demonstrate that the proposed approach is a successful step in automating neuronal cell-type classification.

Original language	English
Title of host publication	2018 9th Cairo International Biomedical Engineering Conference (CIBEC)
Publisher	IEEE
Pages	57-60
Number of pages	4
ISBN (Electronic)	978-1-5386-8154-1
ISBN (Print)	978-1-5386-8155-8
DOIs	https://doi.org/10.1109/CIBEC.2018.8641824
State	Published - 2018
Event	9th Cairo International Biomedical Engineering Conference - Cairo, Egypt Duration: Dec 20 2018 → Dec 22 2018 Conference number: 9

Conference

Conference	9th Cairo International Biomedical Engineering Conference
Abbreviated title	CIBEC 2018
Country/Territory	Egypt
City	Cairo
Period	12/20/18 → 12/22/18

ASJC Scopus Subject Areas

Safety, Risk, Reliability and Quality
Computer Science Applications
Computer Vision and Pattern Recognition
Biomedical Engineering

Keywords

classification
motoneurons
neuronal cell-type

Disciplines

Biomedical Engineering and Bioengineering

Access to Document

10.1109/CIBEC.2018.8641824

Cite this

@inproceedings{5230275975b242c591447a690c1d2c36,

title = "Automated Cell-Type Classification and Death-Detection of Spinal Motoneurons",

abstract = "Spinal motoneurons (MNs) play a crucial role in movement control. Decoding the firing activity of spinal MNs could help in real-life challenges, such as enhancing the control of myoelectric prostheses and diagnosing neurodegenerative diseases. In this paper, we propose a machine learning approach to automatically classify MNs based on their firing activity. Applying the proposed approach to data from a MN computational model, the classification accuracy of all examined datasets exceeded 95\%. We extended the approach to detecting the death of a given MN type using clustering validity index. Results indicated that 86\% of the examined death-detection cases were detected accurately. These results demonstrate that the proposed approach is a successful step in automating neuronal cell-type classification.",

keywords = "classification, motoneurons, neuronal cell-type",

author = "Mai Gamal and Mousa, \{Mohamed H.\} and Seif Eldawlatly and Elbasiouny, \{Sherif M.\}",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 9th Cairo International Biomedical Engineering Conference ; Conference date: 20-12-2018 Through 22-12-2018",

year = "2018",

doi = "10.1109/CIBEC.2018.8641824",

language = "English",

isbn = "978-1-5386-8155-8",

pages = "57--60",

booktitle = "2018 9th Cairo International Biomedical Engineering Conference (CIBEC)",

publisher = "IEEE",

}

TY - GEN

T1 - Automated Cell-Type Classification and Death-Detection of Spinal Motoneurons

AU - Gamal, Mai

AU - Mousa, Mohamed H.

AU - Eldawlatly, Seif

AU - Elbasiouny, Sherif M.

N1 - Conference code: 9

PY - 2018

Y1 - 2018

N2 - Spinal motoneurons (MNs) play a crucial role in movement control. Decoding the firing activity of spinal MNs could help in real-life challenges, such as enhancing the control of myoelectric prostheses and diagnosing neurodegenerative diseases. In this paper, we propose a machine learning approach to automatically classify MNs based on their firing activity. Applying the proposed approach to data from a MN computational model, the classification accuracy of all examined datasets exceeded 95%. We extended the approach to detecting the death of a given MN type using clustering validity index. Results indicated that 86% of the examined death-detection cases were detected accurately. These results demonstrate that the proposed approach is a successful step in automating neuronal cell-type classification.

AB - Spinal motoneurons (MNs) play a crucial role in movement control. Decoding the firing activity of spinal MNs could help in real-life challenges, such as enhancing the control of myoelectric prostheses and diagnosing neurodegenerative diseases. In this paper, we propose a machine learning approach to automatically classify MNs based on their firing activity. Applying the proposed approach to data from a MN computational model, the classification accuracy of all examined datasets exceeded 95%. We extended the approach to detecting the death of a given MN type using clustering validity index. Results indicated that 86% of the examined death-detection cases were detected accurately. These results demonstrate that the proposed approach is a successful step in automating neuronal cell-type classification.

KW - classification

KW - motoneurons

KW - neuronal cell-type

UR - https://www.scopus.com/pages/publications/85063233190

UR - https://www.scopus.com/inward/citedby.url?scp=85063233190&partnerID=8YFLogxK

U2 - 10.1109/CIBEC.2018.8641824

DO - 10.1109/CIBEC.2018.8641824

M3 - Conference contribution

AN - SCOPUS:85063233190

SN - 978-1-5386-8155-8

SP - 57

EP - 60

BT - 2018 9th Cairo International Biomedical Engineering Conference (CIBEC)

PB - IEEE

T2 - 9th Cairo International Biomedical Engineering Conference

Y2 - 20 December 2018 through 22 December 2018

ER -

Automated Cell-Type Classification and Death-Detection of Spinal Motoneurons

Abstract

Conference

ASJC Scopus Subject Areas

Keywords

Disciplines

Access to Document

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