Imperial College London
Alternate

ProfessorEmm MicDrakakis

Faculty of Engineering, Department of Bioengineering

Professor of Bio-Circuits and Systems
 
 
 
//

Contact

 

e.drakakis Website

 
 
//

Location

 

B207Bessemer BuildingSouth Kensington Campus

//

Summary

 

Publications

Citation

BibTex format

@article{Bashford:2019:10.1016/j.clinph.2019.03.032,
author = {Bashford, J and Wickham, A and Iniesta, R and Drakakis, E and Boutelle, M and Mills, K and Shaw, C},
doi = {10.1016/j.clinph.2019.03.032},
journal = {Clinical Neurophysiology},
pages = {1083--1090},
title = {SPiQE: An automated analytical tool for detecting and characterising fasciculations in amyotrophic lateral sclerosis},
url = {http://dx.doi.org/10.1016/j.clinph.2019.03.032},
volume = {130},
year = {2019}
}
Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - OBJECTIVES: Fasciculations are a clinical hallmark of amyotrophic lateral sclerosis (ALS). Compared to concentric needle EMG, high-density surface EMG (HDSEMG) is non-invasive and records fasciculation potentials (FPs) from greater muscle volumes over longer durations. To detect and characterise FPs from vast data sets generated by serial HDSEMG, we developed an automated analytical tool. METHODS: Six ALS patients and two control patients (one with benign fasciculation syndrome and one with multifocal motor neuropathy) underwent 30-minute HDSEMG from biceps and gastrocnemius monthly. In MATLAB we developed a novel, innovative method to identify FPs amidst fluctuating noise levels. One hundred repeats of 5-fold cross validation estimated the model's predictive ability. RESULTS: By applying this method, we identified 5,318 FPs from 80 minutes of recordings with a sensitivity of 83.6% (+/- 0.2 SEM), specificity of 91.6% (+/- 0.1 SEM) and classification accuracy of 87.9% (+/- 0.1 SEM). An amplitude exclusion threshold (100μV) removed excessively noisy data without compromising sensitivity. The resulting automated FP counts were not significantly different to the manual counts (p=0.394). CONCLUSION: We have devised and internally validated an automated method to accurately identify FPs from HDSEMG, a technique we have named Surface Potential Quantification Engine (SPiQE). SIGNIFICANCE: Longitudinal quantification of fasciculations in ALS could provide unique insight into motor neuron health.
AU  - Bashford,J
AU  - Wickham,A
AU  - Iniesta,R
AU  - Drakakis,E
AU  - Boutelle,M
AU  - Mills,K
AU  - Shaw,C
DO  - 10.1016/j.clinph.2019.03.032
EP  - 1090
PY  - 2019///
SN  - 1388-2457
SP  - 1083
TI  - SPiQE: An automated analytical tool for detecting and characterising fasciculations in amyotrophic lateral sclerosis
T2  - Clinical Neurophysiology
UR  - http://dx.doi.org/10.1016/j.clinph.2019.03.032
UR  - https://www.ncbi.nlm.nih.gov/pubmed/31078984
UR  - http://hdl.handle.net/10044/1/69831
VL  - 130
ER  -
Download