ObjectiveThe purpose of the research is to study the distribution and early warning of electroencephalogram (EEG) in acute mountain sickness (AMS). MethodsA total of 280 healthy young men were recruited from September 2016 to October 2016. The basic data were collected by the centralized flow method, the general situation of the division of the investigators after the training, the Lewis Lake score, the computer self-rating anxiety scale and depression scale, and the collection of EEG. Follow up in three months. Results94 of the patients with AMS, morbidity is 33%, 21 (22.34%) of the patients are moderate to severe, 73 (77.66%) are mild, morbidity is 26.67%. The abnormal detection rate of electrogram was 7.9% (22/280), which were mild EEG, normal EEG abnormal rate was 8.6% (16/186), abnormal detection rate of mild AMS was 4.1% (3/73), and the abnormal detection rate was 14.3% (3/21) in the medium / heavy AMS. The latter was significantly different from the previous (P < 0.05). Three months follow-up of this group of patients with 0 case of high altitude disease. Conclusions The EEG in AMS is mainly a rhythm irregular, unstable, poor amplitude modulation; or two hemisphere volatility difference of more than 50% or slightly increased activity. The result is statistically significant, suggesting that EEG distributions has possible early warning of AMS.
Heart failure (HF) is the end-stage of all cardiac diseases, characterized by high prevalence, high mortality, and heavy social and economic burden. Early warning of HF exacerbation is of great value for outpatient management and reducing readmission rates. Currently, remote dynamic monitoring technology, which captures changes in hemodynamic and physiological parameters of HF patients, has become the primary method for early warning and is a hot research topic in clinical studies. This paper systematically reviews the progress in this field, which was categorized into invasive monitoring based on implanted devices, non-invasive monitoring based on wearable devices, and other monitoring technologies based on audio and video. Invasive monitoring primarily involves direct hemodynamic parameters such as left atrial pressure and pulmonary artery pressure, while non-invasive monitoring covers parameters such as thoracic impedance, electrocardiogram, respiration, and activity levels. These parameters exhibit characteristic changes in the early stages of HF exacerbation. Given the clinical heterogeneity of HF patients, multi-source information fusion analysis can significantly improve the prediction accuracy of early warning models. The results of this study suggest that, compared with invasive monitoring, non-invasive monitoring technology, with its advantages of good patient compliance, ease of operation, and cost-effectiveness, combined with AI-driven multimodal data analysis methods, shows significant clinical application potential in establishing an outpatient management system for HF.