COVID-19 in a Designated Infectious Diseases HospitalOutside Hubei Province,China

BackgroundA new type of novel coronavirus infection (COVID-19) occurred in Wuhan, Hubei Province. Previous investigations reported patients in Wuhan city often progressed into severe or critical and had a high mortality rate.The clinical characteristics of affected patients outside the epicenter of Hubei province are less well understood. MethodsAll confirmed COVID-19 case treated in the Third Peoples Hospital of Shenzhen,from January 11, 2020 to February 6, 2020, were included in this study. We analyzed the epidemiological and clinical features of these cases to better inform patient management in normal hospital settings. ResultsAmong the 298 confirmed cases, 233(81.5%) had been to Hubei while 42(14%) had not clear epidemiological history. Only 192(64%) cases presented with fever as initial symptom. The lymphocyte count decreased in 38% patients after admission. The number (percent) of cases classified as non-severe and severe was 240(80.6%) and 58(19.4%) respectively. Thirty-two patients (10.7%) needed ICU care. Compared to the non-severe cases, severe cases were associated with older age, underlying diseases, as well as higher levels of CRP, IL-6 and ESR. The median (IRQ) duration of positive viral test were 14(10-19). Slower clearance of virus was associated with higher risk of progression to severe clinical condition. As of February 14, 2020, 66(22.1%) patients were discharged and the overall mortality rate remains 0. ConclusionsIn a designated hospital outside the Hubei Province, COVID-19 patients were mainly characterized by mild symptoms and could be effectively manage by properly using the existing hospital system.

Extraction of evoked related potentials by using the combination of independent component analysis and wavelet analysis / 生物医学工程学杂志

In this paper we present a new method of combining Independent Component Analysis (ICA) and Wavelet de-noising algorithm to extract Evoked Related Potentials (ERPs). First, the extended Infomax-ICA algorithm is used to analyze EEG signals and obtain the independent components (Ics); Then, the Wave Shrink (WS) method is applied to the demixed Ics as an intermediate step; the EEG data were rebuilt by using the inverse ICA based on the new Ics; the ERPs were extracted by using de-noised EEG data after being averaged several trials. The experimental results showed that the combined method and ICA method could remove eye artifacts and muscle artifacts mixed in the ERPs, while the combined method could retain the brain neural activity mixed in the noise Ics and could extract the weak ERPs efficiently from strong background artifacts.

Progress in single-trial extraction of evoked potential / 生物医学工程学杂志

The robust extracting of evoked potential (EP) has become a vexed question in the process of electroencephalogram owing to the faint signal-to-noise ratio of EP. This paper presents the single trial of EP in time, transform and space field. Several methods such as adaptive filter, wavelet transform, principal component analysis (PCA) and independent component analysis (ICA) have been applied to the process of EP.