Differentiating patients with obstructive sleep apnea from healthy controls based on heart rate - blood pressure coupling quantified by entropy-based indices
We introduce an entropy-based classification method for pairs of sequences (ECPS) for quantifying mutual dependencies in heart rate and beat-to-beat blood pressure recordings. The purpose of the method is to build a classifier for data in which each item consists of the two intertwined data series taken for each subject. The method is based on ordinal patterns, and uses entropy-like indices. Machine learning is used to select a subset of indices most suitable for our classification problem in order to build an optimal yet simple model for distinguishing between patients suffering from obstructive sleep apnea and a control group.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationSimilar Papers 제목 키워드 기반
Recognition of Patient Groups with Sleep Related Disorders using Bio-signal Processing and Deep Learning
Accurately diagnosing sleep disorders is essential for clinical assessments and treatments. Polysomnography (PSG) has long been used for detection of various sleep disorders. In this research, electrocardiography (ECG) a…
Electrocardiography (ECG)The management of obstructive sleep apnea accompanied with mandibular retrognathia across the lifespan from orthodontic perspective
Obstructive sleep apnea (OSA) is a complex disease with complex etiology, which requires multidisciplinary cooperation in diagnosis and treatment. Mandibular retrognathia is strongly associated with OSA. Orthodontists ca…
ManagementDreem Open Datasets: Multi-Scored Sleep Datasets to compare Human and Automated sleep staging
Sleep stage classification constitutes an important element of sleep disorder diagnosis. It relies on the visual inspection of polysomnography records by trained sleep technologists. Automated approaches have been design…
Automatic Sleep Stage ClassificationMultimodal Sleep Stage DetectionSleep Stage DetectionSleep StagingSingle Channel ECG for Obstructive Sleep Apnea Severity Detection using a Deep Learning Approach
Obstructive sleep apnea (OSA) is a common sleep disorder caused by abnormal breathing. The severity of OSA can lead to many symptoms such as sudden cardiac death (SCD). Polysomnography (PSG) is a gold standard for OSA di…
SpecificityThe molecular pathology of genioglossus in obstructive sleep apnea
Obstructive sleep apnea (OSA) is a sleep respiratory disease characterized by sleep snoring accompanied by apnea and daytime sleeplessness. It is a complex disease, with the multifactorial etiology, and the pathology is …