MySleep: A Sleep Monitoring Application

MySleep: A Sleep Monitoring Application

Sleep monitoring application which can record audio to detect snore and apnea events using signal processing and machine learning algorithms

Artificial Intelligence, Android, Modern Code

Description

Sleep is a physical and mental state that plays an important role in the maintenance of health and well-being. It is responsible, for example, for the functions of memory consolidation and metabolism regulation, which, when absent, can have mental consequences and severe physiological disorders, called sleep disorders. These disorders are syndromes that affect normal sleep, negatively affecting health, as in the case of Obstructive Sleep Apnea Hypopnea Syndrome (OSAHS), which is related to the momentary stopping of breathing at night.

Snoring is one of the main indicators of the presence of sleep disorders related to respiratory causes. By means of the detection and analysis of these indicators we can extract characteristics that allow the identification of respiratory disorders, which constitutes a possibility of previous detection of these, accelerating the diagnosis and access to treatment.

The goal of this research project is to develop a mobile application for monitoring the user's night of sleep in order to aid in the diagnosis and treatment of sleep disorders. The application called MySleep performs the recording and transmission of audio captured at night to a server responsible for analyzing this signal and extracting respiratory events from snoring and OSAHS in order to estimate the rate of apnea-hypopnea.

The captured audio signal is being analyzed through digital signal processing, artifical intelligence and machine learning techniques. A first version of the Android application is now available for testing, with audio recording and streaming features, and has guidance screens for signal capture and user instruction on care to be taken before recording starts. The next steps will be to extract and display quality metrics and sleep disturbances visually within the application and make sleep metrics available to hospitals and other interested parties through a web portal for follow-up and clinical decision support.

Video

Links

App demo

Default user avatar 57012e2942

Vinícius B. created project MySleep: A Sleep Monitoring Application

Medium 89e23f96 7dec 49c3 ba7e 4c94a56e01be

MySleep: A Sleep Monitoring Application

Sleep is a physical and mental state that plays an important role in the maintenance of health and well-being. It is responsible, for example, for the functions of memory consolidation and metabolism regulation, which, when absent, can have mental consequences and severe physiological disorders, called sleep disorders. These disorders are syndromes that affect normal sleep, negatively affecting health, as in the case of Obstructive Sleep Apnea Hypopnea Syndrome (OSAHS), which is related to the momentary stopping of breathing at night.

Snoring is one of the main indicators of the presence of sleep disorders related to respiratory causes. By means of the detection and analysis of these indicators we can extract characteristics that allow the identification of respiratory disorders, which constitutes a possibility of previous detection of these, accelerating the diagnosis and access to treatment.

The goal of this research project is to develop a mobile application for monitoring the user's night of sleep in order to aid in the diagnosis and treatment of sleep disorders. The application called MySleep performs the recording and transmission of audio captured at night to a server responsible for analyzing this signal and extracting respiratory events from snoring and OSAHS in order to estimate the rate of apnea-hypopnea.

The captured audio signal is being analyzed through digital signal processing, artifical intelligence and machine learning techniques. A first version of the Android application is now available for testing, with audio recording and streaming features, and has guidance screens for signal capture and user instruction on care to be taken before recording starts. The next steps will be to extract and display quality metrics and sleep disturbances visually within the application and make sleep metrics available to hospitals and other interested parties through a web portal for follow-up and clinical decision support.

No users to show at the moment.

No users to show at the moment.