The Beginning of the AI-Enabled Preventative PAP Therapy Era: A First-in-Human Proof of Concept Interventional Study.

Positive Airway Pressure (PAP) therapy is the most common and efficacious treatment for Obstructive Sleep Apnea (OSA). However, it suffers from poor patient adherence due to discomfort and may not fully alleviate all adverse consequences of OSA. Identifying abnormal respiratory events before they have occurred may allow for improved management of PAP levels, leading to improved adherence and better patient outcomes. Our previous work has resulted in the successful development of a Machine-Learning (ML) algorithm for the prediction of future apneic events using existing airflow and air pressure sensors available internally to PAP devices. Although researchers have studied the use of ML for the prediction of apneas, research to date has focused primarily on using external polysomnography sensors that add to patient discomfort and has not investigated the use of internal-to-PAP sensors such as air pressure and airflow to predict and prevent respiratory events. We hypothesized that by using our predictive software, OSA events could be proactively prevented while maintaining patients' sleep quality. An intervention protocol was developed and applied to all patients to prevent OSA events. Although the protocol's cool-down period limited the number of prevention attempts, analysis of 11 participants revealed that our system improved many sleep parameters, which included a statistically significant 31.6% reduction in Apnea-Hypopnea Index, while maintaining sleep quality. Most importantly, our findings indicate the feasibility of unobtrusive identification and unique prevention of each respiratory event as well as paving the path to future truly personalized PAP therapy by further training of ML models on individual patients.

Lung Mechanics Tracking with Forced Oscillation Technique (FOT) Based on CMOS Synchronous Demodulation Principle.

In this paper, an area-efficient CMOS integrated solution for lung impedance extraction is presented. The lock-in principle is leveraged for its high effective bandpass selectivity, to acquire information about the airways, through stimulation by FOT (Forced Oscillation Technique). The modulated pressure and flow signals are down-converted by a quadrature voltage commutating passive mixer-first receiver. In addition to its linearity, and unlike the Gilbert cell, it can be biased at zero dc current to alleviate flicker noise contributions. The proposed solution is designed and fabricated in 0.18µm TSMC technology. The chip occupies an active silicon area of 4.7 mm2 (including buffers and pads) and dissipates 429.63 µW. The proposed approach offers real time tracking of respiratory mechanics and is expected to be a promising solution for portable health monitoring and cost-effective biomedical devices.