Ignacio Garrido (Spain)

UNIVERSIDAD COMPLUTENSE DE MADRID FACULTAD DE MEDICINA

Author Of 1 Presentation

 Conference Calendar

DESIGN AND IMPLEMENTATION OF A BREATH-RATE MEASUREMENT SOLUTION BASED ON COMPUTER VISION AND MACHINE LEARNING TECHNIQUES IN CHILDREN WITH LOWER RESPIRATORY INFECTION

Date
Thu, 12.05.2022
Session Time
08:00 - 09:30
Session Type
Parallel Symposium
Session Name
0580 - PARALLEL SYMPOSIUM: Artificial Intelligence in Pediatric Diagnosis (ID 82)
Room
DIMITRIS MITROPOULOS HALL
Presenter
Lecture Time
08:57 - 09:07

Abstract

Backgrounds:

Camera-based diagnostic methods could allow an objective analysis of a patient's health remotely and contactless, which is especially interesting in telemedicine and pandemic scenarios. Artificial intelligence and computer vision can provide the diagnostic tools needed to improve patient monitoring. The main objective of this work is the design and implementation of a solution to estimate respiratory rate (RR) from a video captured through a smartphone, based on computer vision and deep learning techniques.

Methods

Prospective study of clinical information and a video of the patients’ chest with and without respiratory distress under 10 years old from November 2020 to May 2021 attending for a lower respiratory infection in a tertiary hospital in Spain. Video pre-processing was carried out using computer vision methods. As an initial approximation, remote photoplethysmographic signal (rPPG) was used with subsequent processing using the Discrete Wavelet Transform (DWT) and different methods to estimate the RR.

Results:

19 patients were included and 22 video sequences were pre-processed to carry out the estimation of the respiratory rate using the PPG signal approach. 51.3% were males and the average rate age was 2 years (DE 0.32). 61.5% patients had not relevant medical records. 48.7% were diagnosed of bronchiolitis followed by 30.1% diagnosed of asthma symptoms. Results obtained by different methods to estimate the RR can be seen in Table 1.

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Conclusions/Learning Points:

It has been possible to design a breath-rate measurement solution to estimate RR from a video, based on DWT transformation to rPPG signal. This could be the first step in order to implement a breath-rate measurement solution based on computer vision and deep learning techniques.

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