COVID-19 could cause long term peripheral nerve demyelination and axonal loss: A One Year Prospective Cohort Study

BackgroundThere is a lack of studies on large-sample, medium-, or long-term follow-up data of peripheral neuropathy (PNP) in the COVID-19 survivors. This study evaluated the characteristics and related risk factors of PNP in the medium- and long-term rehabilitation, which provided real-world study data for the complete recovery of COVID-19 patients. MethodsThis study was a prospective cohort study of the COVID-19 survivors. We collected data on baseline characteristics, symptoms at onset and after discharge during the 6-month and 12-month follow-up. Peripheral nerves were measured by electromyography and inducible potentiometer. We used multivariable logistic regression to analyze the influencing factors of PNP. Additionally, we compared the difference between the two measurements among the population who completed both measurements. Results313 patients were included in the study and all of them underwent nerve conduction study. 67 patients completed two measurements at 6-month and 12-month follow-up. Commonly reported symptoms contained memory loss (86%), hair loss (28%), anxiety (24%), and sleep difficulties (24%). 232 patients (74%) were found with PNP, including 51 (16%) with mononeuropathy and 181 (58%) with generalized PNP. Patients with measurement at 12-month follow-up had a higher prevalence of generalized PNP (p=0.006). For pathological types, 64 (20%) patients had only axonal loss, 67 (21%) had only demyelination, and 101 (32%) had a mixed type. There was no significant difference in the prevalence of accompanying symptoms after discharge between the two groups with or without PNP. After adjustment, age was positively associated with PNP (OR=1.22 per 10-year increase of age, 95% CI, 1.05-1.41). Compared with less than the median amount of IgG at discharge, higher amount of IgG was associated with decreased risk of F-wave abnormality (OR=0.32, 95%CI, 0.11-0.82), but no significant difference in other types of PNP. Conclusions and RelevanceSARS-CoV-2 could cause PNP in hospital survivors with COVID-19, which persisted and was associated with age, education, and IgG antibody at discharge, but had no significant correlation with symptoms after discharge.

Application of fuzzy reasoning to myocardial ischemia judgment based on electrocardiogram ST-T complex / 生物医学工程学杂志

Electrocardiogram (ECG) is a convenient, economic, and non-invasive detecting tool in myocardial ischemia (MI). Its clinical appearance is mainly exhibited by ST-T complex change. MI events are usually instantaneous and asymptomatic in some cases, which cannot be forecasted to have a precautionary measure in time by doctors. The automatic detection of MI by computer and a cued warning of danger in real time play an important role in diagnosing heart disease. With the help of the medical staff, some quantitative approbatory indicators, such as ST-segment deviation, the amplitude of T-wave peak and the rate of ST and heart rate (HR), were combined to judge MI using fuzzy reasoning. After MIT-BIH database and the long-term ST database (LTST) verification, sensitivity and positive predictive values reached 75% and 78% respectively, and specificity and negative predictive values were 85% and 87% respectively. In addition, the proposed method was close to human way of thinking and understanding, and easy to apply in clinical detection and engineering fields.

A squeeze approach for electrocardiogram ST-segment detection based on R-wave and T-wave / 生物医学工程学杂志

ST-segment is the main clinical appearance in myocardial ischemia detection based on electrocardiogram (ECG) signals. However, it is highly sensitive to interferences (baseline wandering, postural changes, electrode interference, etc.), which cause the feature points of ECG ST-segment to be difficult to detect accurately. Currently, the common detection methods of ST-segment are: R+x and J+x, but they are affected badly by T-wave morphological variability and J point location. For these reasons, firstly we proposed a convenient and accurate approach for T-wave onset in this paper. It did not need to locate T-wave peak and was robust to baseline wandering and T-wave morphology. Secondly, we proposed a squeeze approach for ST-segment detection based on R-wave peak and T-wave onset. After the Long-Term ST database (LTST) verification, the proposed method has shown a good timeliness and robustness, and the accuracy of ST-segment detection has reached above 92%.

Myocardial ischemia analysis based on electrocardiogram QRS complex in time domain / 国际生物医学工程杂志

Objective ST-T complex change, which represents the ventricle repolarization phase, is the main clinical indicator in detecting myocardial ischemia (MI) based on electrocardiogram (ECG) signals.However, its feature point location is not accurate due to interferences. In this paper, a new approach about myocardial ischemia analysis was proposed based on QRS complex. Methods QRS complex, representing the ventricle depolarization process, was used to analyze myocardial ischemia, and some parameters were extracted synthetically in time domain. Then they were used for statistical analysis of myocardial ischemia states and non-myocardial ischemia states. Results Five parameters had significant differences after verification of Non-MI signals in MIT-BIH database and MI signals in long-term ST database (LTST) and they were: QRS upward and downward slopes, transient heart rate, R angle and Q angle in a triangle QRS. Conclusion Five parameters extracted from QRS complex had significant differences. The proposed method provides an important basis for myocardial ischemia detection.