Study of physiological and pathological information mining with biological information similarity analysis

Abstract

Chronic diseases, such as sleep apnea and Parkinson's disease, are characterized by insidious onset, complex etiology, slow course, and easy-to-cause other complications, which seriously affect life quality of the patients. Real-time monitoring of biological information can effectively reveal the occurrence and development of chronic diseases. It also helps in aspects of early diagnosis and treating options. In current study, the dynamic change rules of biological signals caused by chronic diseases have been explored, from which one can realize the auxiliary diagnosis and evaluation of these diseases. Attention has been focused on physiological fluctuation and coordination of biological information similarity, including pulse fluctuation detection in patients with sleep apnea and plantar pressure coordination assessment in patients with Parkinson's disease. In the biological similarity study, the heart rate from sleep apnea patients has been recorded two minutes before and after breath pulse. Information of the average plantar pressure from both foot of Parkinson patients has also been recorded and analyzed. Results show that: for sleep apnea patients, their heart rate fluctuation level has significantly reduced. That is because the human body enhances its sympathetic nerve activity to open the airway. The heart rate starts to change periodically, resulting in its fluctuations tending to be consistent. Compared with ordinary people, PD patients have weaker biological information similarities of plantar pressure on one foot. Also, information similarity between left and right feet of PD patients was more diversified. It revealed that the left and right foot plantar pressure fluctuated more and tended to be more consistent together with gait disorder and weakened balance. Such results show that the similarity of biological information can effectively excavate the fluctuation and coordination of physiological signals, and effectively contribute to the recognition and auxiliary diagnosis of chronic diseases. Data mining methods applied here helps to explore the physiological and pathological mechanism of the studied chronic diseases and sheds light on early diagnosis and severity assessment. It becomes more promising to develop algorithms, software, and hardware systems that is helpful for patients and facilitate promotion of human life quality and health cause.