A New Wearable System for Home Sleep Apnea Testing, Screening, and Classification

Abstract

We propose an unobtrusive, wearable, and wireless system for the pre-screening and follow-up in the domestic environment of specific sleep-related breathing disorders. This group of diseases manifests with episodes of apnea and hypopnea of central or obstructive origin, and it can be disabling, with several drawbacks that interfere in the daily patient life. The gold standard for their diagnosis and grading is polysomnography, which is a time-consuming, scarcely available test with many wired electrodes disseminated on the body, requiring hospitalization and long waiting times. It is limited by the night-by-night variability of sleep disorders, while inevitably causing sleep alteration and fragmentation itself. For these reasons, only a small percentage of patients achieve a definitive diagnosis and are followed-up. Our device integrates photoplethysmography, an accelerometer, a microcontroller, and a bluetooth transmission unit. It acquires data during the whole night and transmits to a PC for off-line processing. It is positioned on the nasal septum and detects apnea episodes using the modulation of the photoplethysmography signal during the breath. In those time intervals where the photoplethysmography is detecting an apnea, the accelerometer discriminates obstructive from central type thanks to its excellent sensitivity to thoraco-abdominal movements. Tests were performed on a hospitalized patient wearing our integrated system and the type III home sleep apnea testing recommended by The American Academy of Sleep Medicine. Results are encouraging: sensitivity and precision around 90% were achieved in detecting more than 500 apnea episodes. Least thoraco-abdominal movements and body position were successfully classified in lying down control subjects, paving the way toward apnea type classification.

An Overview of Wearable Piezoresistive and Inertial Sensors for Respiration Rate Monitoring

“…The correlation coefficient between the two measurements was 0.61, the bias 0.41, and the standard deviation 2.5 BrPM. Also, in [115], the authors presented MORFEA, a new device able to identify some sleep disorders characterized by apnea periods. The device was built to be used in the home environment, resulting unobtrusive but very sensitive.…”

“…In particular, they are commonly used to suppress artefacts induced by body movements different from the respiration ones or dynamically adapt the processing chain's parameters as a function of the carried out activity (walking, sleeping, stay seated, etc.). Also, in [115], the authors presented MORFEA, a new device able to identify some sleep disorders characterized by apnea periods. The device was built to be used in the home environment, resulting unobtrusive but very sensitive.…”

“…Like those in [74,75], the belt-type devices offer greater reliability since they reduce the artifact since the IMUs are in solidarity with the body but lack wearability since the band could obstruct the user's movements. Besides, the devices in [113,115], applied on the nasal septum, represent an optimal solution for monitoring respiratory activity during daily life or sleep to detect respiratory dysfunctions. Finally, the in-ear device in [116] is a practical and accurate solution for detecting RR by processing the data provided by a product already present on the market (Nokia eSense).…”

“…In [115], A. Manoni et al presented a detailed algorithm to distinguish between obstructive and central apneas from RR data, measured with the MORFEA device discussed in the previous section. The algorithm detected abrupt amplitude variations in the PPG signal and delated them to remove motion artefacts.…”

“…However, the detected acceleration waveforms are subjected to artefacts due to body movements and other physiological signals with a size comparable with the breathing ones, inducing errors in RR measurement [144]. The frequency-domain approaches, like [115,131,134,135], based on popular FFT-based estimators and peak detection, represent an alternative solution, allowing a straightforward reduction of motion-induced artefacts, but requiring a higher computational load [145]. Nevertheless, STFT can reduce the computational requirements by windowing the acceleration trends on shorter time segments [146].…”