Multimodal Signal Analysis for Unobtrusive Characterization of Obstructive Sleep Apnea

Obstructive sleep apnea (OSA) is the most prevalent sleep related breathing disorder, nevertheless subjects suffering from it often remain undiagnosed due to the cumbersome diagnosis procedure. Moreover, the prevalence of OSA is increasing and a better phenotyping of patients is needed in order to prioritize treatment. The goal of this thesis was to tackle those challenges in OSA diagnosis, by means of advanced signal processing algorithms, proposed in this thesis. Additionally, two main algorithmic contributions, which are generally applicable were proposed. The binary interval coded scoring algorithm was extended to multilevel problems and novel monotonicity constraints were introduced. Moreover, improvements to the random-forest based feature selection were proposed including the use of the Cohen’s kappa value, patient independent validation, and further feature pruning steered by the correlation between features.

The first part of this thesis focused on the development of reliable, multimodal OSA screening methods based on unobtrusive measurements such as oxygen saturation (SpO2), electrocardiography (ECG), pulse photoplethysmography (PPG), and respiratory measures. The novel SpO2 model was the best performing OSA screening method, obtaining accuracies of over 88%, outperforming most of the state-of-the-art algorithms. Different multimodal OSA detection approaches were explored, but this performance could not be further improved. Finally, a main contribution of this PhD was to test the developed ECG and PPG OSA detection algorithms on unobtrusive signals, including capacitively-coupled ECG and bioimpedance, and wearable PPG recordings. Although these experiments showed promising results, the limitations of the current algorithms on the unobtrusive data were also highlighted.

In the second part of this PhD a contribution towards a better characterization of OSA patients beyond the AHI was proposed. Novel pulse oximetry markers were developed and investigated to assess the cardiovascular status of OSA subjects. It was found that patients with cardiovascular comorbidities experienced more severe oxygen desaturations and incomplete resaturations to the baseline SpO2 values. The novel multilevel interval coded scoring was used to train a model to predict the cardiovascular status of OSA patients based on the age, BMI and the SpO2 parameters. The final model obtained good classification performances on a clinical population, but the predictive power of this model should be further validated.