Automatic Silence Events Detector from Smartphone Audio Signals: A Pilot mHealth System for Sleep Apnea Monitoring at Home

Abstract

Obstructive sleep apnea (OSA) is a prevalent disease, but most patients remain undiagnosed and untreated. Recently, mHealth tools are being proposed to screen OSA patients at home. In this work, we analyzed full-night audio signals recorded with a smartphone microphone. Our objective was to develop an automatic detector to identify silence events (apneas or hypopneas) and compare its performance to a commercial portable system for OSA diagnosis (ApneaLink TM , ResMed). To do that, we acquired signals from three subjects with both systems simultaneously. A sleep specialist marked the events on smartphone and ApneaLink signals. The automatic detector we developed, based on the sample entropy, identified silence events similarly than manual annotation. Compared to ApneaLink, it was very sensitive to apneas (detecting 86.2%) and presented an 83.4% positive predictive value, but it missed about half the hypopnea episodes. This suggests that during some hypopneas the flow reduction is not reflected in sound. Nevertheless, our detector accurately recognizes silence events, which can provide valuable respiratory information related to the disease. These preliminary results show that mHealth devices and simple microphones are promising non-invasive tools for personalized sleep disorders management at home.

Analysis of Smartphone Triaxial Accelerometry for Monitoring Sleep-Disordered Breathing and Sleep Position at Home

“…The Apnealink device was also placed over the sternum, below the smartphone, based on that described in the Apnealink guidelines. This device placement configuration had already been tested successfully in previous studies by our group [30], [31], [34], [35].…”

Enhanced Monitoring of Sleep Position in Sleep Apnea Patients: Smartphone Triaxial Accelerometry Compared with Video-Validated Position from Polysomnography

“…This application saves the data in the smartphone memory as a text file. The smartphone was placed over the sternum and held in position with an elastic strap as seen in Figure 1 a, based on the configuration proposed by Nakano et al [ 35 ], which has been tested successfully in previous publications of our group [ 36 , 37 , 38 , 40 , 41 ]. This configuration resulted in the smartphone triaxial accelerometry providing positive values when accelerations occurred from right to left (X-axis), from toe to head (Y-axis), and from front to back (Z-axis), as seen in Figure 1 b.…”

“…Multiple approaches using different combinations of these smartphone sensors to monitor sleep apnea have been documented, including previous work by our group. Some studies have made use of the embedded microphone sensor to diagnose and monitor sleep apnea [ 35 , 36 , 37 , 38 ], and others have used the embedded accelerometry sensor of the smartphone to monitor sleep apnea and position [ 39 , 40 , 41 ]. The treatment of pOSA has also been researched with smartphone applications which vibrated when the patient slept in a supine position, to promote lateral sleep positions [ 42 ].…”

SleepPos App: An Automated Smartphone Application for Angle Based High Resolution Sleep Position Monitoring and Treatment

“…Multiple approaches have already been made, including some previous work of our group, in developing tools to monitor sleep quality. These attempts included the analysis of audio to detect snoring and breathing [ 53 , 54 , 55 , 56 ], the analysis of accelerometry to detect chest movement, respiration and sleep depth [ 57 , 58 , 59 , 60 ], and, sometimes, the combination of both with portable oximetry devices. Furthermore, smartphones have been tested as vibrational position treatment devices [ 61 ].…”