Manual and ventilator hyperinflation parameters used by intensive care physiotherapists in Sri Lanka: An online survey.

INTRODUCTION: Hyperinflation is a common procedure to clear secretion, increase lung compliance and enhance oxygenation in mechanically ventilated patients. Hyperinflation can be provided as manual hyperinflation (MHI) or ventilator hyperinflation (VHI), where outcomes depend upon the methods of application. Hence it is crucial to assess the application of techniques employed in Sri Lanka due to observed variations from recommended practices. OBJECTIVE: This study is aimed to evaluate the application and parameters used for MHI and VHI by physiotherapists in intensive care units (ICUs) in Sri Lanka. METHODOLOGY: An online survey was conducted among physiotherapists who are working in ICUs in Sri Lanka using WhatsApp groups and other social media platforms. RESULTS: A total of 96 physiotherapists responded. The survey comprised of three sections to obtain information about socio-demographic data, MHI practices and VHI practices. Most of the respondents (47%) worked in general hospitals and 74% of participants had a bachelor's degree in physiotherapy; 31.3% had 3-6 years of experience; 93.8% used hyperinflation, and 78.9% used MHI. MHI was performed routinely and as needed to treat low oxygen levels, abnormal breath sounds, and per physician orders while avoiding contraindications. Self-inflation bags are frequently used for MHI (40.6%). Only a few participants (26%) used a manometer or tracked PIP. In addition to the supine position, some participants (37.5%) used the side-lying position. Most physiotherapists followed the recommended MHI technique: slow squeeze (57.3%), inspiratory pause (45.8%), and quick release (70.8%). VHI was practised by 19.8%, with medical approval and it was frequently performed by medical staff compared to physiotherapists. Treatment time, number of breaths, and patient positioning varied, and parameters were not well-defined. CONCLUSION: The study found that MHI was not applied with the recommended PIP, and VHI parameters were not identified. The study indicates a need to educate physiotherapists about current VHI and MHI practice guidelines.

Modelling of polysomnographic respiratory measurements for artefact detection and signal restoration.

Polysomnography (PSG), which incorporates measures of sleep with measures of EEG arousal, air flow, respiratory movement and oxygenation, is universally regarded as the reference standard in diagnosing sleep-related respiratory diseases such as obstructive sleep apnoea syndrome. Over 15 channels of physiological signals are measured from a subject undergoing a typical overnight PSG session. The signals often suffer from data losses, interferences and artefacts. In a typical sleep scoring session, artefact-corrupted signal segments are visually detected and removed from further consideration. This is a highly time-consuming process, and subjective judgement is required for the job. During typical sleep scoring sessions, the target is the detection of segments of diagnostic interest, and signal restoration is not utilized for distorted segments. In this paper, we propose a novel framework for artefact detection and signal restoration based on the redundancy among respiratory flow signals. We focus on the air flow (thermistor sensors) and nasal pressure signals which are clinically significant in detecting respiratory disturbances. The method treats the respiratory system and other organs that provide respiratory-related inputs/outputs to it (e.g., cardiovascular, brain) as a possibly nonlinear coupled-dynamical system, and uses the celebrated Takens embedding theorem as the theoretical basis for signal prediction. Nonlinear prediction across time (self-prediction) and signals (cross-prediction) provides us with a mechanism to detect artefacts as unexplained deviations. In addition to detection, the proposed method carries the potential to correct certain classes of artefacts and restore the signal. In this study, we categorize commonly occurring artefacts and distortions in air flow and nasal pressure measurements into several groups and explore the efficacy of the proposed technique in detecting/recovering them. The results we obtained from a database of clinical PSG signals indicated that the proposed technique can detect artefacts/distortions with a sensitivity>88.3% and specificity>92.4%. This work has the potential to simplify the work done by sleep scoring technicians, and also to improve automated sleep scoring methods.