Graph Signal Processing (GSP) has been used in the analysis of functional Magnetic Resonance Imaging(fMRI). As a holistic view of brain function and the connections between and within brain regions, by structuring data as node points
within the brain and modelling the edge connections between nodes. Many studies have used GSP with Blood Oxygenation Level Dependent (BOLD) imaging of
the brain and brain activation. Meanwhile, the methodology has seen little use in
muscle imaging. Similar to brain BOLD, muscle BOLD (mBOLD) also aims to
demonstrate muscle activation. Muscle BOLD depends on oxygenation, vascularization, fibre type, blood flow, and haemoglobin count. Nevertheless the mBOLD
signal still follows muscle activation closely. Electromyography (EMG) is another
modality for measuring muscle activation. Both mBOLD and EMG can be represented and analyzed with GSP. In order to better understand muscle activation
during contraction the proposed method focused on using GSP to model mBOLD
data both alone and jointly with EMG. Simultaneous mBOLD imaging and EMG
recording of the calf muscles was performed, creating a multimodal dataset. A
generalized filtering methodology was developed for the removal of the MRI gradient artifact in EMG sensors within the MR bore. The filtered data was then used
to generate a GSP model of the muscle, focusing on gastrocnemius, soleus, and
tibialis anterior muscles. The graph signals were constructed along two edge connection dimensions; coherence and fractility. For the standalone mBOLD graph signal models, the models’ goodness of fits were 1.3245 × 10-05 and 0.06466 for
coherence and fractility respectively. The multimodal models showed values of
2.3109 × -06 and 0.0014799. These results demonstrate the promise of modelling
muscle activation with GSP and its ability to incorporate multimodal data into
a singular model. These results set the stage for future investigations into using
GSP to represent muscle with mBOLD, EMG, and other biosignal modalities. / Thesis / Master of Applied Science (MASc) / Magnetic Resonance Imaging(MRI) and electromyography (EMG) are techniques
used in the analysis of muscle, for detecting injury or deepening the understanding
of muscle function. Graph Signal Processing (GSP) is a methodology used to
represent data and the information flow between positions. While GSP has been
used in modelling the brain, applications to muscle are scarce. This work aimed
to model muscle activation using GSP methods, using both MRI and EMG data.
To do so, a method for being able to simultaneously record MRI and EMG data
was developed through hardware construction and the software implementation
of EMG signal filtering. The collected data were then used to construct multiple
GSP models based on the coherence and complexity of the signals, the goodness
of fit for each of the constructed models were then compared. In conclusion, it
is feasible to use GSP to model muscle activity with multimodal MRI and EMG
data. This shows promise for future investigations into the applications of GSP to
muscle research.
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/28016 |
| Date | January 2022 |
| Creators | Sooriyakumaran, Thaejaesh |
| Contributors | Noseworthy, Michael, Biomedical Engineering |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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