Design of an electronic device for the measurement of respiratory signals

Moscoso-Barrera, William D.; Carreno-Perez, Ivan S.; Agudelo-Otálora, Luis Mauricio; Giraldo-Cadavid, Luis Fernando; Burguete, Javier

doi:10.1109/biosmart54244.2021.9677776

Cited by 2 publications

(2 citation statements)

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“…The algorithm presented in that work analyzes respiratory signals in 8-s segments with a Support Vector Machine (SVM) classifier. Some systems have also been proposed that use simple electronic systems for signal analysis [ 43 ] or diagnosis and treatment of OSA [ 44 ] based on patterns recognition: wavelet entropy, RMS value, and variance extracted from the respiratory effort signals to classify the presence or absence of apneas using machine learning with an accuracy of 82 ± 7%.…”

Section: Discussionmentioning

confidence: 99%

Simple and Autonomous Sleep Signal Processing System for the Detection of Obstructive Sleep Apneas

Moscoso-Barrera

Urrestarazu

Alegre

et al. 2022

IJERPH

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Obstructive sleep apnea (OSA) is a common sleep disorder characterized by repetitive upper airway obstruction, intermittent hypoxemia, and recurrent awakenings during sleep. The most used treatment for this syndrome is a device that generates a positive airway pressure—Continuous Positive Airway Pressure (CPAP), but it works continuously, whether or not there is apnea. An alternative consists on systems that detect apnea episodes and produce a stimulus that eliminates them. Article focuses on the development of a simple and autonomous processing system for the detection of obstructive sleep apneas, using polysomnography (PSG) signals: electroencephalography (EEG), electromyography (EMG), respiratory effort (RE), respiratory flow (RF), and oxygen saturation (SO2). The system is evaluated using, as a gold standard, 20 PSG tests labeled by sleep experts and it performs two analyses. A first analysis detects awake/sleep stages and is based on the accumulated amplitude in a channel-dependent frequency range, according to the criteria of the American Academy of Sleep Medicine (AASM). The second analysis detects hypopneas and apneas, based on analysis of the breathing cycle and oxygen saturation. The results show a good estimation of sleep events, where for 75% of the cases of patients analyzed it is possible to determine the awake/asleep states with an effectiveness of >92% and apneas and hypopneas with an effectiveness of >55%, through a simple processing system that could be implemented in an electronic device to be used in possible OSA treatments.

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Section: Discussionmentioning

confidence: 99%

Simple and Autonomous Sleep Signal Processing System for the Detection of Obstructive Sleep Apneas

Moscoso-Barrera

Urrestarazu

Alegre

et al. 2022

IJERPH

Self Cite

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“…Los resultados de los circuitos diseñados e implementados en una primera versión, antes de llevar el sistema a una sola tarjeta de control fueron mostrados y compartidos en un congreso de IEEE [105].…”

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Diseño e implementación de dos dispositivos biomédicos para pacientes con Síndrome de Apnea Obstructiva del Sueño (SAOS) basados en la detección de apneas e hipopneas y rehabilitación por medio de estimulación eléctrica

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Design of an electronic device for the measurement of respiratory signals

Cited by 2 publications

References 13 publications

Simple and Autonomous Sleep Signal Processing System for the Detection of Obstructive Sleep Apneas

Simple and Autonomous Sleep Signal Processing System for the Detection of Obstructive Sleep Apneas

Diseño e implementación de dos dispositivos biomédicos para pacientes con Síndrome de Apnea Obstructiva del Sueño (SAOS) basados en la detección de apneas e hipopneas y rehabilitación por medio de estimulación eléctrica

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