A bioengineering approach to quantifying the progression of amyotrophic lateral sclerosis by electromyography using novel wireless instrumentation and data processing methods
File(s)
Author(s)
Wickham, Aidan Paul
Type
Thesis
Abstract
This thesis presents the research undertaken towards developing new instrumentation used for capturing fasciculation potentials (FPs) by electromyography (EMG), so that their waveform morphology may be used as a clinical biomarker for tracking the progression of Amyotrophic Lateral Sclerosis (ALS).
Evidence in literature has shown that the fasciculation potential, caused by the instability of a diseased motor neuron’s membrane potential, will increase in complexity and variability as the disease progresses.
A ‘data-processing pipeline’ has been developed in order to process data from multiple-channel surface EMG recordings taken from ALS patients. The pipeline conditions the signals, detects fasciculations, and groups them by the motor unit they originated from (based on the similarity of their morphology) so that variability between waveforms can be parameterised. The data-processing pipeline uses a combination of novel algorithms and frameworks, and machine learning methods (i.e. clustering) to detect and group the FP waveforms. The data-processing pipeline has been developed into a stand-alone application.
A multiple-channel wireless device for capturing EMG signals at high resolution and sampling rate has been developed. The device captures only the FP from the EMG signal and saves it locally to an SD card. The device captures fasciculation potentials across eight channels at 16-bit resolution with a sampling rate of approximately 4 kHz per channel.
A new route for prototyping transdermal, microneedle devices has been developed using a shrinkable resin technology. The reason for using transdermal electrodes for EMG recordings is to reduce skin-electrode contact impedance by painlessly penetrating through the highly resistive first layer of skin. The method has been used to produce microneedles approximately 300 μm in height. The microneedle arrays produced have been made conductive and functionalised into a biosensor for measuring lactate.
Evidence in literature has shown that the fasciculation potential, caused by the instability of a diseased motor neuron’s membrane potential, will increase in complexity and variability as the disease progresses.
A ‘data-processing pipeline’ has been developed in order to process data from multiple-channel surface EMG recordings taken from ALS patients. The pipeline conditions the signals, detects fasciculations, and groups them by the motor unit they originated from (based on the similarity of their morphology) so that variability between waveforms can be parameterised. The data-processing pipeline uses a combination of novel algorithms and frameworks, and machine learning methods (i.e. clustering) to detect and group the FP waveforms. The data-processing pipeline has been developed into a stand-alone application.
A multiple-channel wireless device for capturing EMG signals at high resolution and sampling rate has been developed. The device captures only the FP from the EMG signal and saves it locally to an SD card. The device captures fasciculation potentials across eight channels at 16-bit resolution with a sampling rate of approximately 4 kHz per channel.
A new route for prototyping transdermal, microneedle devices has been developed using a shrinkable resin technology. The reason for using transdermal electrodes for EMG recordings is to reduce skin-electrode contact impedance by painlessly penetrating through the highly resistive first layer of skin. The method has been used to produce microneedles approximately 300 μm in height. The microneedle arrays produced have been made conductive and functionalised into a biosensor for measuring lactate.
Version
Open Access
Date Issued
2019-08
Date Awarded
2019-12
Copyright Statement
Creative Commons Attribution NonCommercial Licence
License URL
Advisor
Boutelle, Martyn
Drakakis, Emm
Sponsor
Engineering and Physical Sciences Research Council
Motor Neurone Disease Association (MNDA)
Publisher Department
Bioengineering
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)