A simple mathematical model of spontaneous swallow effects on breathing based on new experimental data

A variable heart rate multi-compartmental coupled model of the cardiovascular and respiratory systems

Current mathematical models of the cardiovascular system that are based on systems of ordinary differential equations are limited in their ability to mimic important features of measured patient data, such as variable heart rates (HR). Such limitations present a significant obstacle in the use of such models for clinical decision-making, as it is the variations in vital signs such as HR and systolic and diastolic blood pressure that are monitored and recorded in typical critical care bedside monitoring systems. In this paper, novel extensions to well-established multi-compartmental models of the cardiovascular and respiratory systems are proposed that permit the simulation of variable HR. Furthermore, a correction to current models is also proposed to stabilize the respiratory behaviour and enable realistic simulation of vital signs over the longer time scales required for clinical management. The results of the extended model developed here show better agreement with measured bio-signals, and these extensions provide an important first step towards estimating model parameters from patient data, using methods such as neural ordinary differential equations. The approach presented is generalizable to many other similar multi-compartmental models of the cardiovascular and respiratory systems.

Respiratory Inductance Plethysmography for Automated Swallowing Detection

In the context of remote medical monitoring of swallowing, the authors investigate the potential of non-invasive respiratory inductance plethysmography (RIP) technique to automatically detect swallow events in a wide context of respiration and phonation. Signal acquisitions were carried out on 5 healthy volunteers equipped with RIP and electroglottograph as swallowing reference. They were asked for spontaneous breathing, speaking and diverse bolus ingesting. The RIP signal was then segmented into cycles, each cycle being annotated according to one of the three class of interest, respectively ventilation i.e. spontaneous breathing (1257 cycles), swallowing (221) and phonation (216). Automated classification was performed using quadratic discriminant analysis. Focusing on swallowing class, the authors achieve an accuracy of 79% from the full wide protocol. It increases up to 86% with prior removal of vocalizations. These preliminary results in healthy subjects make RIP a promising candidate as a non-invasive and convenient technology for medical follow-up of swallowing.