RESUMEN
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 w and r0, 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 τ 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.
RESUMEN
It has been 3 years since the first appearance of COVID-19 in China. During this time, social isolation was widely used as an important method to fight it. However, this measure had many negative effects on the mental health of college students. To better understand this issue, this study aims to explore the impact of social isolation and COVID-19-related stress on psychological distress among Chinese college students. Additionally, resilience has been evaluated as a key component of stress resistance in this situation. Coronavirus Stress Measure (CSM), the Connor-Davidson Resilience Scale (CD-RISC) and the Brief Symptom Inventory (BSI) were used in this study. A total of 388 Chinese college students participated in the survey via the Internet. Two groups (isolated group vs. non-isolated group) were divided according to whether they have been isolated from their classmates and families. Data analysis adopts t-test, F test and mediate effect analysis by SPSS21.0. (1) All factors, except resilience, were found to have lower scores in the isolated group; (2) significant correlations were found between all factors; (3) resilience partially mitigated the impact of COVID-19 stress on psychological symptoms. Social isolation has been found to be a significant factor contributing to negative psychological distress in Chinese college students. The COVID-19-related stress may increase the likelihood of psychological suffering among isolated group. Resilience can help reduce the negative effects of COVID-19 stress on college students. Therefore, providing appropriate psychological support tailored to different isolation situations is crucial.