Patient Machine Interface for the Control of Mechanical Ventilation Devices
Identifieur interne : 003089 ( Ncbi/Curation ); précédent : 003088; suivant : 003090Patient Machine Interface for the Control of Mechanical Ventilation Devices
Auteurs : Rolando Grave De Peralta ; Sara Gonzalez Andino [Suisse] ; Stephen PerrigSource :
- Brain Sciences [ 2076-3425 ] ; 2013.
Abstract
The potential of Brain Computer Interfaces (BCIs) to translate brain activity into commands to control external devices during mechanical ventilation (MV) remains largely unexplored. This is surprising since the amount of patients that might benefit from such assistance is considerably larger than the number of patients requiring BCI for motor control. Given the transient nature of MV (
Url:
DOI: 10.3390/brainsci3041554
PubMed: 24961620
PubMed Central: 4061889
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PMC:4061889Le document en format XML
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<author><name sortKey="Grave De Peralta, Rolando" sort="Grave De Peralta, Rolando" uniqKey="Grave De Peralta R" first="Rolando" last="Grave De Peralta">Rolando Grave De Peralta</name>
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<affiliation><nlm:aff id="af2-brainsci-03-01554">Geneva Sleep Lab, Department of Neuropsychiatry, Geneva University Hospital (HUG), Geneva 1225, Switzerland; E-Mail:<email>stephen.perrig@hcuge.ch</email>
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<affiliation wicri:level="1"><nlm:aff id="af3-brainsci-03-01554">Neural Microcircuitry Lab, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne 1015, Switzerland</nlm:aff>
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<sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Patient Machine Interface for the Control of Mechanical Ventilation Devices</title>
<author><name sortKey="Grave De Peralta, Rolando" sort="Grave De Peralta, Rolando" uniqKey="Grave De Peralta R" first="Rolando" last="Grave De Peralta">Rolando Grave De Peralta</name>
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<affiliation><nlm:aff id="af2-brainsci-03-01554">Geneva Sleep Lab, Department of Neuropsychiatry, Geneva University Hospital (HUG), Geneva 1225, Switzerland; E-Mail:<email>stephen.perrig@hcuge.ch</email>
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<author><name sortKey="Gonzalez Andino, Sara" sort="Gonzalez Andino, Sara" uniqKey="Gonzalez Andino S" first="Sara" last="Gonzalez Andino">Sara Gonzalez Andino</name>
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<author><name sortKey="Perrig, Stephen" sort="Perrig, Stephen" uniqKey="Perrig S" first="Stephen" last="Perrig">Stephen Perrig</name>
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<front><div type="abstract" xml:lang="en"><p>The potential of Brain Computer Interfaces (BCIs) to translate brain activity into commands to control external devices during mechanical ventilation (MV) remains largely unexplored. This is surprising since the amount of patients that might benefit from such assistance is considerably larger than the number of patients requiring BCI for motor control. Given the transient nature of MV (<italic>i.e.</italic>
, used mainly over night or during acute clinical conditions), precluding the use of invasive methods, and inspired by current research on BCIs, we argue that scalp recorded EEG (electroencephalography) signals can provide a non-invasive direct communication pathway between the brain and the ventilator. In this paper we propose a Patient Ventilator Interface (PVI) to control a ventilator during variable conscious states (<italic>i.e.</italic>
, wake, sleep, <italic>etc.</italic>
). After a brief introduction on the neural control of breathing and the clinical conditions requiring the use of MV we discuss the conventional techniques used during MV. The schema of the PVI is presented followed by a description of the neural signals that can be used for the on-line control. To illustrate the full approach, we present data from a healthy subject, where the inspiration and expiration periods during voluntary breathing were discriminated with a 92% accuracy (10-fold cross-validation) from the scalp EEG data. The paper ends with a discussion on the advantages and obstacles that can be forecasted in this novel application of the concept of BCI.</p>
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