ABSTRACT
Total laryngectomy (TL) affects critical functions such as swallowing, coughing and speaking. An artificial, bio-engineered larynx (ABL), operated via myoelectric signals, may improve quality of life for TL patients. To evaluate the efficacy of using surface electromyography (sEMG) as a control signal to predict instances of swallowing, coughing and speaking, sEMG was recorded from submental, intercostal and diaphragm muscles. The cohort included TL and control participants. Swallowing, coughing, speaking and movement actions were recorded, and a range of classifiers were investigated for prediction of these actions. Our algorithm achieved F1-scores of 76.0 ± 4.4 % (swallows), 93.8 ± 2.8 % (coughs) and 70.5 ± 5.4 % (speech) for controls, and 67.7 ± 4.4 % (swallows), 71.0 ± 9.1 % (coughs) and 78.0 ± 3.8 % (speech) for TLs, using a random forest (RF) classifier. 75.1 ± 6.9 % of swallows were detected within 500 ms of onset in the controls, and 63.1 ± 6.1 % in TLs. sEMG can be used to predict critical larynx movements, although a viable ABL requires improvements. Results are particularly encouraging as they encompass a TL cohort. An ABL could alleviate many challenges faced by laryngectomees. This study represents a promising step toward realising such a device.
ABSTRACT
People who have undergone total laryngectomy typically have difficulties speaking and coughing. Coughing, the protective reflex action where air is rapidly expelled from the lungs to clear the airway, is crucial in everyday life. Insufficiency in coughing can lead to serious chest infections. In this research we present a bionic assistive coughing device (RoboCough) to improve coughing efficacy among laryngectomy patients by increasing pressure and flow rate. RoboCough was designed to mimic the function of the glottis and trachea in the upper respiratory system. Experimental results show a significant increase (t(64) = 4.9, p < 0.0001) in peak cough flow rate and peak cough pressure (t(64) = 12.6, p < 0.0001) among 33 control participants using RoboCough. A pilot study with a smaller cohort of laryngectomy patients shows improvement in peak cough pressure (p = 0.0159) using RoboCough. Preliminary results also show that post-laryngectomy coughs achieved similar peak cough flow (Z = -0.9933, p = 0.32) to the control group's natural cough. Coughing capabilities could be improved through using RoboCough. Applications of RoboCough include simulation of vocal folds and respiratory conditions, rehabilitation of ineffective coughs from laryngeal and respiratory diseases and as a test-bed for the development of medical devices for respiratory support.
ABSTRACT
Closed-loop glucose control is an emerging treatment approach to manage type 1 diabetes. Closed-loop systems consist of a continuous glucose monitor, an insulin infusion pump, and a dosing algorithm that directs insulin delivery based on sensor levels. Testing of dosing algorithms in computer simulations may replace animal testing, accelerates development, and saves resources. We propose here a novel approach to generate a virtual population, to be used in metabolic simulators, from routine experimental data through the process that we term "stochastic e-cloning." We build on a nonlinear physiologically motivated time-varying model of glucose regulation. We adopt the Bayesian approach to estimate model parameters and to obtain the joint posterior probability distribution of time-invariant and time-varying parameters with the use of the Markov chain Monte Carlo methodology. The estimation process combines prior knowledge and experimental data to generate a sample from the posterior distribution, which can be subsequently used to conduct in silico experiments reflecting population and individual variability, and associated uncertainty as closely as possible. The approach is exemplified using data collected in 12 young subjects with type 1 diabetes. We demonstrate unbiased fit to the data, physiological plausibility of parameter estimates, and results of in silico testing using a stochastic virtual subject.