Cloud algorithm-driven oximetry-based diagnosis of obstructive sleep apnoea in symptomatic habitually snoring children

Abstract

The ability of a cloud-driven Bluetooth oximetry-based algorithm to diagnose obstructive sleep apnoea syndrome (OSAS) was examined in habitually snoring children concurrently undergoing overnight polysomnography.Children clinically referred for overnight in-laboratory polysomnographic evaluation for suspected OSAS were simultaneously hooked to a Bluetooth oximeter linked to a smartphone. Polysomnography findings were scored and the apnoea/hypopnoea index (AHIPSG) was tabulated, while oximetry data yielded an estimated AHIOXI using a validated algorithm.The accuracy of the oximeter in identifying correctly patients with OSAS in general, or with mild (AHI 1–5 events·h−1), moderate (5–10 events·h−1) or severe (>10 events·h−1) OSAS was examined in 432 subjects (6.5±3.2 years), with 343 having AHIPSG >1 event·h−1. The accuracies of AHIOXI were consistently >79% for all levels of OSAS severity, and specificity was particularly favourable for AHI >10 events·h−1 (92.7%). Using the criterion of AHIPSG >1 event·h−1, only 4.7% of false-negative cases emerged, from which only 0.6% of cases showed moderate or severe OSAS.Overnight oximetry processed via Bluetooth technology by a cloud-based machine learning-derived algorithm can reliably diagnose OSAS in children with clinical symptoms suggestive of the disease. This approach provides virtually limitless scalability and should alleviate the substantial difficulties in accessing paediatric sleep laboratories while markedly reducing the costs of OSAS diagnosis.

Assessment of Mandibular Movement Monitoring With Machine Learning Analysis for the Diagnosis of Obstructive Sleep Apnea

“…Machine learning approaches are increasingly used for diagnosis, disease risk classification, and therapeutic guidance 21,46 and are especially promising in sleep medicine. 22,23,47,48 Downloaded From: https://jamanetwork.com/ on 09/16/2020 with other OSA diagnostic systems, as well as reducing medical errors by facilitating the complex process of respiratory event scoring.…”

A machine learning-based test for adult sleep apnoea screening at home using oximetry and airflow

“…In the present study, we focus on the usefulness of blood oxygen saturation (SpO 2 ) and airflow, which are commonly involved in type IV devices. Individually, both signals have been found to provide relevant information for OSA diagnosis [9][10][11][12] . Notwithstanding, the potential complementarity of the features derived from both signals has been marginally studied 13 .…”

Assessment of Airflow and Oximetry Signals to Detect Pediatric Sleep Apnea-Hypopnea Syndrome Using AdaBoost

“…On the other hand, the MLP neural networks used in previous approaches tended to overestimate the severity of OSA [ 29 , 32 , 42 ]. Vaquerizo-Villar et al reported 12.75% of underestimated subjects and 27.30% of overestimated patients [ 29 ], while in Xu et al the rates of underestimated and overestimated severity were 15.05% and 31.25%, respectively [ 42 ]. In our study, this behavior was not observed, since AdaBoost achieved a more balanced ratio of underestimated and overestimated subjects.…”

“…These approaches included Logistic Regression (LR) [ 23 , 24 , 25 , 27 , 28 , 30 , 31 , 36 , 37 ], Linear or Quadratic Discriminant Analysis (LDA, QDA) [ 38 , 39 , 40 ], and Support Vector Machines (SVM) [ 41 ]. Other recent studies relied on more complex Multilayer Perceptron (MLP) neural networks [ 26 , 29 , 32 , 42 , 43 ]. Most of these algorithms can be hardly generalizable due to their simplicity and susceptibility to overfitting the training data [ 44 ].…”