An algorithm for obtaining the frequency and the times of respiratory phases from nasal and oral acoustic signals

• Autores: Kemper, Guillermo, Oshita, Angel, Parra, Ricardo, Herrera, Carlos
• Formato: artículo
• Fecha de Publicación: 2023
• Institución: Universidad Peruana de Ciencias Aplicadas
• Repositorio: UPC-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorioacademico.upc.edu.pe:10757/668168
• Enlace del recurso: http://hdl.handle.net/10757/668168
• Nivel de acceso: acceso abierto
• Materia: Breathing rate; Buccal sound; Envelope detection; Expiration time; Inspiration time; Nasal sound; Respiration; Computational algorithm; Respiratory phases; Buccal and nasal acoustic signal; Medical applications; Remote patient monitoring; Pulmonary pathologies; COVID-19; Signal acquisition; Signal processing techniques; Validation process and results; https://purl.org/pe-repo/ocde/ford#2.02.01

This work proposes a computational algorithm which extracts the frequency, timings and signal segments corresponding to respiratory phases, through buccal and nasal acoustic signal processing. The proposal offers a computational solution for medical applications which require on-site or remote patient monitoring and evaluation of pulmonary pathologies, such as coronavirus disease 2019 (COVID-19). The state of the art presents a few respiratory evaluation proposals through buccal and nasal acoustic signals. Most proposals focus on respiratory signals acquired by a medical professional, using stethoscopes and electrodes located on the thorax. In this case the signal acquisition process is carried out through the use of a low cost and easy to use mask, which is equipped with strategically positioned and connected electret microphones, to maximize the proposed algorithm's performance. The algorithm employs signal processing techniques such as signal envelope detection, decimation, fast Fourier transform (FFT) and detection of peaks and time intervals via estimation of local maxima and minima in a signal's envelope. For the validation process a database of 32 signals of different respiratory modes and frequencies was used. Results show a maximum average error of 2.23% for breathing rate, 2.81% for expiration time and 3.47% for inspiration time.