Differentiating responses to obstructive sleep apnoea using pulse transit time

Conventional overnight polysomnography (PSG) used to determine the respiratory behaviour during sleep can be a complex and expensive procedure. Pulse transit time analysis (PTT) has shown potential to detect obstructive apnoeic and hypopnoeic events in adults. This study was undertaken to determine the potential of PTT to differentiate responses to upper airway obstruction. 103 obstructive respiratory events occurred in PSG studies performed on 11 children (10 male and 1 female, mean age 7.5years). PTT measurements were evaluated against the corresponding PSG results pre-scored by 2 blinded observers. Broadly, there were 2 types of responses. They can be either short period of rapid PTT decreases (Type 1) or prolonged but gradual PTT decreases (Type 2). Type 1 obstructive events showed a mean change of 51.77% (p

Confounding physiologic parameters in pulse transit time monitoring of children

Pulse Transit Time (PTT) measurement has showed potential in non-invasive monitoring of changes in blood pressure. In children, the common peripheral sites used for these studies are a finger or toe. Presently, there are no known studies conducted to investigate any possible physiologic parameters affecting PTT measurement at these sites for children. In this study, PTT values of both peripheral sites were recorded from 64 children in their sitting posture. Their mean age with standard deviation (SD) was 8.2 2.6years (ranged 3 to 12years). Subjects' peripheries path length, heart rate (HR), systolic (SBP) and diastolic blood pressure (DBP) were measured to investigate any contributions to PTT measurement. The peripheral pulse timing characteristic measured by photoplethysmography (PPG) shows a 59.5 8.5ms (or 24.8 0.4%) difference between the two peripheries (p

Relations between physiologic parameters and pulse transit time during loaded breathing

Pulse transit time (PTT) is a non-invasive measure, defined as time taken for the pulse pressure waves to travel from the R-wave of electrocardiogram to a selected peripheral site. Baseline PTT value is known to be influenced by physiologic variables like heart rate (HR), blood pressure (BP) and arterial compliance (AC). However, few quantitative data are available describing the factors which can influence PTT measurements in a child during breathing. The aim of this study was to investigate the effects of changes in breathing efforts on PTT baseline and fluctuations. Two different inspiratory resistive loading (IRL) devices were used to simulate loaded breathing in order to induce these effects. It is known that HR can influence the normative PTT value however the effect of HR variability (HRV) is not well-studied. Two groups of 3 healthy children ( 0.05) HR changes during all test activities. Results showed that HRV is not the sole contributor to PTT variations and suggest that changes in other physiologic parameters are also equally important. Hence, monitoring PTT measurement can be indicative of these associated changes during tidal or increased breathing efforts in healthy children.

An automated failure mode and effect analysis based on high-level design specificication with behavior trees

Formal methods have significant benefits for developing safety critical systems, in that they allow for correctness proofs, model checking safety and liveness properties, deadlock checking, etc. However, formal methods do not scale very well and demand specialist skills, when developing real-world systems. For these reasons, development and analysis of large-scale safety critical systems will require effective integration of formal and informal methods. In this paper, we use such an integrative approach to automate Failure Modes and Effects Analysis (FMEA), a widely used system safety analysis technique, using a high-level graphical modelling notation (Behavior Trees) and model checking. We inject component failure modes into the Behavior Trees and translate the resulting Behavior Trees to SAL code. This enables us to model check if the system in the presence of these faults satisfies its safety properties, specified by temporal logic formulas. The benefit of this process is tool support that automates the tedious and error-prone aspects of FMEA.