What is the best way to manage bipolar disorder?
Intelligent Monitoring of Bipolar Disorder Based on Photoplethysmography
Traditionally, the assessment of psychiatric symptoms has relied on subjective reports from patients to psychiatrists. In recent years, the rapid development and miniaturization of sensing devices promote the real-time monitoring of human physiological signals.
Because the fluctuation of mental state is often reflected in the changes in physiological condition and behavior.
Moreover, the detection of such physiological data many expect to provide unprecedented insight for human psychology research. Photoplethysmography (PPG), an electrical signal of the human body collected by photoplethysmography. It can extract heart rate variability and other characteristics that help to judge the psychological state.
With the rapid development of sensors, the measurement of PPG is gradually convenient. And as a result, becomes widely used in daily life.
In conclusion, bipolar disorder, a common and serious mental disease.
Which becomes characterized by recurrent manic and depression. Considering the long recovery period of bipolar disorder treatment, the high risk of negative impact on the surrounding environment, and relative to the time lag of diagnosis and treatment, this paper studies the correlation between PPG signal monitored by portable wristwatch and bipolar disorder.
Moreover, in this paper, we collected PPG signals and manic-depressive state of 8 manic depressive patients. And 6 normal subjects for several consecutive days through PPG – a scale collection experiment and extracted PPG timing characteristics and wake-up state characteristics reflecting body activation from original PPG signals.
Lastly, in the process of the study, we used wake-up degree and time series characteristics as data sets to establish prediction models of wake-up degree disease probability and PPG time series characteristics disease severity, to explore the mapping relationship between PPG signal and bipolar disorder. Through experiments, we found that the use of wake-up state to describe the PPG signal can better predict the prevalence of bipolar disorder.