Entropy Change of Biological Dynamics in Asthmatic Patients and Its Diagnostic Value in Individualized Treatment: A Systematic Review.
Shixue SunYu JinChang ChenBaoqing SunZhixin CaoIek Long LoQi ZhaoJun ZhengYan ShiXiaohua Douglas ZhangPublished in: Entropy (Basel, Switzerland) (2018)
Asthma is a chronic respiratory disease featured with unpredictable flare-ups, for which continuous lung function monitoring is the key for symptoms control. To find new indices to individually classify severity and predict disease prognosis, continuous physiological data collected from monitoring devices is being studied from different perspectives. Entropy, as an analysis method for quantifying the inner irregularity of data, has been widely applied in physiological signals. However, based on our knowledge, there is no such study to summarize the complexity differences of various physiological signals in asthmatic patients. Therefore, we organized a systematic review to summarize the complexity differences of important signals in patients with asthma. We searched several medical databases and systematically reviewed existing asthma clinical trials in which entropy changes in physiological signals were studied. As a conclusion, we find that, for airflow, heart rate variability, center of pressure and respiratory impedance, their entropy values decrease significantly in asthma patients compared to those of healthy people, while, for respiratory sound and airway resistance, their entropy values increase along with the progression of asthma. Entropy of some signals, such as respiratory inter-breath interval, shows strong potential as novel indices of asthma severity. These results will give valuable guidance for the utilization of entropy in physiological signals. Furthermore, these results should promote the development of management and diagnosis of asthma using continuous monitoring data in the future.
Keyphrases
- lung function
- chronic obstructive pulmonary disease
- end stage renal disease
- cystic fibrosis
- air pollution
- clinical trial
- newly diagnosed
- heart rate variability
- ejection fraction
- allergic rhinitis
- chronic kidney disease
- healthcare
- electronic health record
- big data
- computed tomography
- randomized controlled trial
- risk assessment
- magnetic resonance
- heart rate
- artificial intelligence
- study protocol
- data analysis
- patient reported
- sleep quality
- double blind