Sleep disorders are related to many different diseases, and they could have a significant impact in patients' health, causing an economic impact to the society and to the national health systems. In the United States, according to information from the Center for Disease Control and Prevention, those disorders are affecting 50-70 million in the adult population. Sleep disorders are causing annually around 40,000 deaths due to cardiovascular problems, and they cost the health system more than 16 billion. In other countries, such as in Spain, those disorders affect up to 48% of the adult population. The main objective of this chapter is to review and evaluate the different machine learning techniques utilized by researchers and medical professionals to identify, assess, and characterize sleep disorders. Moreover, some future research directions are proposed considering the evaluated area.
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Information and communication technologies are transforming the way we understand health, especially through a hyper-connected world in which medical professionals and patients face new roles. Solutions increasingly consider the patient to be the center of the operations and services, and offer personalized and tailored services.
Digital health focuses on using the set of Information Technologies, also known as ICTs, as tools to face different challenges in the health area. These challenges are related to prevention, diagnosis, treatment, follow-up and ultimately with health management, saving costs for the health system and considerably improving its effectiveness.
Some of the most outstanding benefits of digital health are the creation of individualized treatment plans, direct doctor-patient contact and in real time, avoiding unnecessary travel, thus facilitating care for patients living in remote areas, allowing family members to know the patient's condition, it facilitates the prevention of deterioration of the patient through the analysis of indicative symptoms and signs, optimizes the use of health resources ... etc. The power of telemedicine is so significant that years ago a second opinion was sought in the face of an uncomfortable diagnosis, while today the necessary specialists are located to obtain dozens of opinions in a matter of minutes.
Telemedicine has experienced significant growth in recent years and today it has proven its effectiveness as a follow-up option in settings such as: chronic disease management, home hospitalization, outpatient post-surgery follow-up and patient care to elderly patients. The great advance in telemedicine solutions, especially the great explosion of applications focused on the subject, is allowing patient monitoring without altering their daily life. Telemedicine is considered one of the most important innovations in health services from the technological, cultural and social perspectives.
There are a number of factors that guarantee that the great growth of telemedicine is maintained over time as well as its expansion, especially in the European environment. These factors are: the progressive aging of the population and therefore the increased suffering from chronic diseases, the growing need to carry out efficient resource management, the endless waiting lists for consultations with some specialties or the shortage of specialist doctors in some regions.
In Europe, the use of telemedicine applications is very popular, especially in certain specialties. Some of the most prominent are radiology, cardiology, primary care, neurology, surgery ... etc.
Therefore, the growth in life expectancy necessarily increases the number of patients with chronic diseases and the number of visits to different specialists. Telemedicine can offer an increase in the quality of life by reducing the number of face-to-face visits with the doctor, while offering them autonomy and guaranteeing them specialized follow-up.
Researchers are investigating many different diseases related with biotechnology and telemedicine. Some of the most innovative approaches are related to cancer, Parkinson, Alzheimer, or chronic diseases.
The usage of biosignals is becoming increasingly useful to diagnose diseases, assess treatments and also create personalized treatments. Some example of the most utilized signals are the electroencephalography (EEG), the electrocardiogram (ECG) or the blood pressure (BP).
Sleep disorders are conditions that result in changes in the way that someone usually sleeps (Mayo Clinic, 2020). There are different types of disorder such as insomnia, sleep apnea, restless leg syndrome or narcolepsy, and there are different treatments to mitigate the effect of most of them. During the last years, sleep disorders have been an important focus on telemedicine diagnosis objective and a lot of enhancements have been performed from a technological and clinical point of view to support in the diagnosis and treatment of those patterns.
Overall, the chapter has been organized as follows. First, literature review supporting the research provided. Then, the research methodology and data collection have been highlighted along with the main results of this study, together with the discussion. Finally, conclusions, as well as limitations and future lines of research have been proposed.