Chronic respiratory diseases (CRDs) affect the structures of the respiratory system and include asthma, chronic obstructive pulmonary disease (EPOC), and sleep apnea syndrome (SAS). The burden of CRDs leads to major adverse effects on the quality of life of affected individuals, causing premature deaths and a serious economic impact on societies. Most recent advances in the study of CRDs are related to the identification of risk factors other than lung function, such as comorbid diseases, seriously affecting prognosis. The DEEPDREAM project aimed at investigating the mechanisms underlying CRDs and their connection with the development and progression of cardiovascular diseases.
Prediction is crucial to reduce the risk of cardiovascular complications caused by CRDs. It is known that intermittent hypoxia caused by obstructive sleep apnea (OSA) contributes to impaired autonomic cardiac function, promoting the development arrhythmias and hypertension. A set of biomarkers can be proposed to perform an accurate prediction of future adverse cardiac events whether the pathophysiological mechanisms linking OSA severity with cardiac function alterations are understood. In the case of COPD, which is characterized by progressive airflow limitation, the patients may often experience a sudden worsening of symptoms, well-known as an “exacerbation”, which represent one of the main causes of mortality. COPD can also cause many other health problems like pneumonia, lung cancer, pulmonary hypertension, and raise the risk of heart disease. A common characteristic of these CRDs is their complexity and multifactorial etiology, involving dynamic interactions between respiration and ventilation control, and among the cardiovascular, respiratory, and nervous systems.
To capture the complex interactions among the involved physiological systems and signals in CRDs, advanced methods were implemented in DEEPDREAM. Data-driven computational modeling was used to figure out relevant predictive biomarkers and to model complex relationships between involved physiological systems, by using statistical learning techniques for all available physiological data. By using probabilistic graphical models such as Bayesian Networks (BN), we can get not only correlations but also cause and effect, therefore, opening the way toward a more precise characterization and interpretation of the cardiorespiratory control system in CRDs.
The analysis of polysomnographic (PSG) recordings represents nowadays the gold-standard for the diagnosis of OSA but it is a time-consuming task. CRDs are within an area where the effective use of the Big-Data technologies holds great promise for risk assessment and personalized medical treatment. In promoting Big-Data as a source of innovation in healthcare, we made use of Big-Data approaches for structuring and organizing our large physiological datasets of OSA patients, as well as for boosting the computational tasks require to produce the insights. On the other hand, since PSG admission is a concern for many patients, accurate and automatic methods represent a great potential to be implemented on lighter hardware or wearable devices with a few sensors, making them suitable for home sleep monitoring.
The combination of advanced computational modeling with signal processing techniques applied to big physiological data, allowed us to assess the relationship between the neural, respiratory, and cardiac systems that take place during chronic respiratory diseases, offering a concrete possibility to go one step further in the early prevention of CVDs and their related complications.
