Objective:To investigate the association between sleep status and the risk for coronary heart disease in adults in Suzhou.Methods:Using the baseline and follow up information of 53 269 local residents aged 30-79 years in China Kadoorie Biobank conducted in Wuzhong District, Suzhou, 51 929 subjects were included in this study after excluding those reporting coronary heart disease, stroke and cancer at the baseline survey. A Cox proportional hazards regression model was used to analyze the association of healthy sleep score (0-3 points) and sleep factors (snoring, insomnia, long sleep duration and nap) with the risk for coronary heart disease.Results:The median follow-up time was 11.12 years, and 1 304 individuals were diagnosed with coronary heart disease during the follow-up. After adjusting for potential confounders, occasional snoring ( HR=1.20, 95% CI: 1.04-1.38), usual snoring ( HR=1.17, 95% CI: 1.02-1.33), insomnia disorder ( HR=1.41, 95% CI: 1.12-1.78), daytime dysfunction ( HR=1.56, 95% CI: 1.20-2.03) and perennial nap ( HR=1.37, 95% CI: 1.19-1.59) were associated with increased risk of coronary heart disease. Compared with those with sleep score of 0 - 1 (low sleep quality), the people with sleep score of 3 had reduced risk of coronary heart disease by 26% ( HR=0.74, 95% CI: 0.63-0.87). Stratified analysis showed that the association of healthy sleep score 3 with risk of coronary heart disease was stronger in low physically active individuals (interaction P<0.05). Conclusions:Snoring, insomnia disorders, daytime dysfunction, and perennial napping were all associated with increased risk for coronary heart disease, and keep healthy sleep mode might reduce the risk for coronary heart disease in adults.

OBJECTIVE: Non-contact continuous blood pressure monitoring is significant in vital sign monitoring. Frequency modulated continuous wave (FMCW) radar is suitable for non-contact wave signal extraction. A heartbeat-guided blood pressure monitoring algorithm using FMCW radar is proposed. METHODS: The target heart rate is detected and pulse wave signal is extracted based on FMCW reflected signals. The variational mode decomposition (VMD) is introduced to alleviate the interferences of human breath and slight body movements. The pulse wave signal is extracted based on target heart rate. Blood pressure related features of pure pulse waveform are extracted to obtain blood pressure based on its estimation model. RESULTS: Experiments are conducted indoor among 15 participants sitting in a nature state. The average accuracy of diastolic blood pressure (DBP) is 94.3% and that of systolic blood pressure is 94.4%. CONCLUSIONS The experimental results demonstrate the robustness and effectiveness of the proposed algorithm, which makes it possible to further achieve long-term real-time non-contact blood pressure monitoring.
Algorithms ; Blood Pressure ; Blood Pressure Determination ; Heart Rate ; Humans ; Radar ; Signal Processing, Computer-Assisted ; Vital Signs