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Ahstract-This paper describes the design and validation of an effective sleep stage classification strategy for patients with sleep apnea. This strategy consists of a sequential forward selection (SFS) feature selection method and a decision-tree-based support vector machines (DTB-SVM) classifier for discriminating three types of sleep based on electrocardiogram (ECG) signals. Each 5-minute epoch of ECG signal data collected during sleep was used to generate 24 features using heart rate variability (HRV) analysis. An SFS feature selection method was then employed to determine which significant features should be selected to improve classification accuracy. A DTB-SVM was then trained using selected features in order to discriminate three sleep stages, including pre-sleep wakefulness, NREM sleep and REM sleep. The average classification accuracy of the proposed strategy was 73.51 %.Our experimental results demonstrate that the proposed strategy provides moderate accuracy for detecting sleep stages in sleep apnea patients and can serve as a convenient tool for assessing sleep quality.

The COVID-19 pandemic has induced traumatic and fear responses globally. Time attitudes, which refer to one’s feelings toward the past, present and future, may have certain effects on psychological adaptations during this crisis period. This study employed a person-centered approach and a two-wave prospective design to investigate how people with different time attitude profiles change differently in their PTSD symptoms and COVID-19-related fears from a low-risk stage to the first big COVID-19 outbreak in Taiwan. Participants were 354 adults with a mean age of 27.79 years. The result provided support for the theoretical six-factor structure of the traditional Chinese Adolescent and Adult Time Inventory-Time Attitudes Scale (AATI-TA). Four clusters of time attitude profiles were identified (Positives, Negatives, Past Negatives and Pessimists). At both waves, Positives had lower levels of PTSD severity and COVID-19-related fears than most of the other groups, and the reverse was noted for Negatives. As for time effects, people across all profiles were significantly affected during the outbreak, but Negatives showed a greater increase in PTSD severity than other groups. In conclusion, mental health services should put efforts into early identification of those with highly negative time attitudes and implement interventions that nudge people toward a more balanced or positive attitude in each temporal frame, especially during adversity such as the COVID-19 pandemic.