Objective To investigate the impact of short-term exposure to atmospheric particulates (PM10/PM2.5) on stroke risk.Methods The literature databases were retrieved.A Meta-analysis of the literature on atmospheric particulate matters and stroke onset met the inclusion criteria was conducted.Results A total of 42 articles were included.Meta-analysis showed that the PM10 increase per 10 μg/m3,the short-term risk of stroke increased 0.6％ (odds ratio [OR] 1.006,95％ confidence interval [CI] 1.003-1.009);PM2.5 increase per 10 μg/m3,the short-term risk of stroke increased 1.3％ (OR 1.013,95％ CI 1.006-1.019).Subgroup analysis showed that the PM10 exposure was only associated with the increased risk of ischemic stroke (OR 1.025,95％ CI 1.010-1.041),and the PM2.5 exposure was only associated with the increased risk of hemorrhagic stroke (OR 1.031,95％ CI 1.010-1.052).Conclusions In the short term,the increased PM10 or PM2.5 concentration is associated with the increased risk of stroke.PM2.5 caused the risk of stroke onset is higher than that of PM10.

The diaphragm is the main respiratory muscle in the body. The onset detection of the surface diaphragmatic electromyography (sEMGdi) can be used in the respiratory rehabilitation training of the hemiparetic stroke patients, but the existence of electrocardiography (ECG) increases the difficulty of onset detection. Therefore, a method based on sample entropy (SampEn) and individualized threshold, referred to as SampEn method, was proposed to detect onset of muscle activity in this paper, which involved the extraction of SampEn features, the optimization of the SampEn parameters and , the selection of individualized threshold and the establishment of the judgment conditions. In this paper, three methods were used to compare onset detection accuracy with the SampEn method, which contained root mean square (RMS) with wavelet transform (WT), Teager-Kaiser energy operator (TKE) with wavelet transform and TKE without wavelet transform, respectively. sEMGdi signals of 12 healthy subjects in 2 different breathing ways were collected for signal synthesis and methods detection. The cumulative sum of the absolute value of error was used as an judgement value to evaluate the accuracy of the four methods. The results show that SampEn method can achieve higher and more stable detection precision than the other three methods, which is an onset detection method that can adapt to individual differences and achieve high detection accuracy without ECG denoising, providing a basis for sEMGdi based respiratory rehabilitation training and real time interaction.