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      Pulmonary function testing dataset of pressure and flow, dynamic circumference, heart rate, and aeration monitoring

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          Abstract

          Respiratory data was collected from 20 subjects, with an even sex distribution, in the low-risk clinical unit at the University of Canterbury. Ethical consent for this trial was granted by the University of Canterbury Human Research Ethics Committee (Ref: HREC 2023/30/LR-PS). Respiratory data were collected, for each subject, over three tests consisting of: 1) increasing set PEEP from a starting point of ZEEP using a CPAP machine; 2) test 1 repeated with two simulated apnoea's (breath holds) at each set PEEP; and 3) three forced expiratory manoeuvres at ZEEP. Data were collected using a custom pressure and flow sensor device, ECG, PPG, Garmin HRM Dual heartrate belt, and a Dräeger PulmoVista 500 Electrical Impedance Tomography (EIT) machine. Subject demographic data was also collected prior to the trial, in a questionnaire, with measurement equipment available. These data aim to inform the development of pulmonary mechanics models and titration algorithms.

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          Standardisation of the measurement of lung volumes.

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            Feature extraction and selection for objective gait analysis and fall risk assessment by accelerometry

            Background Falls in the elderly is nowadays a major concern because of their consequences on elderly general health and moral states. Moreover, the aging of the population and the increasing life expectancy make the prediction of falls more and more important. The analysis presented in this article makes a first step in this direction providing a way to analyze gait and classify hospitalized elderly fallers and non-faller. This tool, based on an accelerometer network and signal processing, gives objective informations about the gait and does not need any special gait laboratory as optical analysis do. The tool is also simple to use by a non expert and can therefore be widely used on a large set of patients. Method A population of 20 hospitalized elderlies was asked to execute several classical clinical tests evaluating their risk of falling. They were also asked if they experienced any fall in the last 12 months. The accelerations of the limbs were recorded during the clinical tests with an accelerometer network distributed on the body. A total of 67 features were extracted from the accelerometric signal recorded during a simple 25 m walking test at comfort speed. A feature selection algorithm was used to select those able to classify subjects at risk and not at risk for several classification algorithms types. Results The results showed that several classification algorithms were able to discriminate people from the two groups of interest: fallers and non-fallers hospitalized elderlies. The classification performances of the used algorithms were compared. Moreover a subset of the 67 features was considered to be significantly different between the two groups using a t-test. Conclusions This study gives a method to classify a population of hospitalized elderlies in two groups: at risk of falling or not at risk based on accelerometric data. This is a first step to design a risk of falling assessment system that could be used to provide the right treatment as soon as possible before the fall and its consequences. This tool could also be used to evaluate the risk several times during the revalidation procedure.
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              Sex differences in breathing

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                Author and article information

                Contributors
                Journal
                Data Brief
                Data Brief
                Data in Brief
                Elsevier
                2352-3409
                04 April 2024
                June 2024
                04 April 2024
                : 54
                : 110386
                Affiliations
                [a ]Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
                [b ]Institute of Technical Medicine, Furtwangen University, Villingen-Schwenningen, Germany
                Author notes
                [* ]Corresponding author. ella.guy@ 123456pg.canterbury.ac.nz
                Article
                S2352-3409(24)00355-X 110386
                10.1016/j.dib.2024.110386
                11033070
                38646196
                25246f55-2210-454d-ab61-3845a4dd5708
                © 2024 The Authors

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 20 March 2024
                : 27 March 2024
                : 29 March 2024
                Categories
                Data Article

                pulmonary mechanics,venturi,spirometry,electrical impedance tomography,vapers,smokers,asthmatics

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