Author: Yolanda del Castillo
Group: Biomedical Signal Processing and Interpretation
Directors: Raimon Jané
Spinal cord injury (SCI) is one of the leading causes of disability worldwide. SCI causes motor and sensory impairment below the level of the injury, but it is also associated with many other health complications. Two of these problems are trunk muscle impairment and sleep disorders. Impaired trunk function affects postural control and sitting balance, which are critical for activities of daily living. Disturbed sleep causes fatigue and sleepiness and can lead to serious comorbidities, impacting patient recovery and outcomes. However, due to the multiple health problems secondary to SCI and the limitations in current diagnostic tools, trunk function and sleep are rarely examined after SCI. The non-invasive acquisition and analysis of biomedical signals can help to overcome this issue, providing quantitative measures to assess patient condition. Smartphones can facilitate this task, thanks to their ubiquitous presence and powerful sensors.
The aim of this PhD thesis is to propose new smartphone-based tools and biomedical signal analysis techniques for the quantitative assessment of trunk function and sleep-disordered breathing (SDB) in patients with SCI. This thesis is divided into two parts, including four publications in high-impact journals. The first part addresses the characterization of trunk function in healthy subjects and patients with cervical (cSCI) and thoracic SCI (tSCI). The second part focuses on the development of a mobile health (mHealth) system based on smartphone audio signals for obstructive sleep apnea (OSA) diagnosis, and the detection and monitoring of SDB in SCI patients.
The first part of the thesis introduces a novel methodology to quantitatively evaluate trunk function by combining electromyography (EMG) and smartphone accelerometry. In the first study, we characterized the muscle activity and movement patterns of trunk flexion during reaching in healthy humans and investigated if trunk stability was affected by a startling acoustic stimulus (SAS). We found that SAS markedly reduced the response time (RespT) and EMG onset latencies of all muscles (the so-called StartReact effect), either prime movers or stabilizers. In the second study, we evaluated trunk function and the effects of a SAS in patients with cSCI and tSCI. The results revealed deficits in postural control and compensatory strategies employed by SCI patients, such as delayed responses and high lateral deviations, with potential consequences for rehabilitation. This was the first study investigating the StartReact responses in trunk muscles in SCI. The SAS significantly shortened the RespT in tSCI, but not in cSCI, which suggests an increased cortical control in cSCI.
In the second part of the thesis, we present mHealth tools for monitoring sleep disorders and investigate the sleep patterns of SCI patients. In the first article of this part, we designed a smartphone system and novel algorithms based on acoustic analysis for OSA screening. This approach demonstrated good agreement with a commercial system for home OSA diagnosis, correctly detecting and stratifying all the OSA patients. In the last article, sleep studies were performed in SCI patients using the smartphone, showing a very high prevalence of moderate-to-severe SDB in SCI patients. This study highlighted the problem of SDB in SCI and provided simple cost-effective tools to improve the detection and management of SDB in SCI patients.
Overall, this thesis supports the use of smartphones and biomedical signal analysis for the assessment of trunk function and SDB in SCI patients. These novel approaches provide quantitative and objective measures for the evaluation and follow-up of patients in a simple and non-invasive way. We also give insights into the underlying mechanisms of postural control, respiratory function during sleep, and the changes occurring after SCI. Consequently, our results open the way for improving the management of health complications associated with SCI or other disabling conditions.
This thesis defence will take place at: Sala d’Actes de l’Escola d’Enginyeria Barcelona Est (EEBE), Edifici A, planta 0, Campus Diagonal-Besòs de la UPC, Av. d’Eduard Maristany, 16, 08019 Barcelona.
If you wish to follow this defence online, you can do it through this link: meet.google.com/roe-omfr-sse