Machine learning-aided detection of heart failure (LVEF ≤ 49%) by using ballistocardiography and respiratory effort signals.

Purpose: Under the influence of COVID-19 and the in-hospital cost, the in-home detection of cardiovascular disease with smart sensing devices is becoming more popular recently. In the presence of the qualified signals, ballistocardiography (BCG) can not only reflect the cardiac mechanical movements, but also detect the HF in a non-contact manner. However, for the potential HF patients, the additional quality assessment with ECG-aided requires more procedures and brings the inconvenience to their in-home HF diagnosis. To enable the HF detection in many real applications, we proposed a machine learning-aided scheme for the HF detection in this paper, where the BCG signals recorded from the force sensor were employed without the heartbeat location, and the respiratory effort signals separated from force sensors provided more HF features due to the connection between the heart and the lung systems. Finally, the effectiveness of the proposed HF detection scheme was verified in comparative experiments. Methods: First, a piezoelectric sensor was used to record a signal sequences of the two-dimensional vital sign, which includes the BCG and the respiratory effort. Then, the linear and the non-linear features w.r.t. BCG and respiratory effort signals were extracted to serve the HF detection. Finally, the improved HF detection performance was verified through the LOO and the LOSO cross-validation settings with different machine learning classifiers. Results: The proposed machine learning-aided scheme achieved the robust performance in the HF detection by using 4 different classifiers, and yielded an accuracy of 94.97% and 87.00% in the LOO and the LOSO experiments, respectively. In addition, experimental results demonstrated that the designed respiratory and cardiopulmonary features are beneficial to the HF detection (LVEF ≤ 49 % ). Conclusion: This study proposed a machine learning-aided HF diagnostic scheme. Experimental results demonstrated that the proposed scheme can fully exploit the relationship between the heart and the lung systems to potentially improve the in-home HF detection performance by using both the BCG, the respiratory and the cardiopulmonary-related features.

Machine learning-aided detection of heart failure (LVEF ≤ 49%) by using ballistocardiography and respiratory effort signals

Purpose: Under the influence of COVID-19 and the in-hospital cost, the in-home detection of cardiovascular disease with smart sensing devices is becoming more popular recently. In the presence of the qualified signals, ballistocardiography (BCG) can not only reflect the cardiac mechanical movements, but also detect the HF in a non-contact manner. However, for the potential HF patients, the additional quality assessment with ECG-aided requires more procedures and brings the inconvenience to their in-home HF diagnosis. To enable the HF detection in many real applications, we proposed a machine learning-aided scheme for the HF detection in this paper, where the BCG signals recorded from the force sensor were employed without the heartbeat location, and the respiratory effort signals separated from force sensors provided more HF features due to the connection between the heart and the lung systems. Finally, the effectiveness of the proposed HF detection scheme was verified in comparative experiments. Methods: First, a piezoelectric sensor was used to record a signal sequences of the two-dimensional vital sign, which includes the BCG and the respiratory effort. Then, the linear and the non-linear features w.r.t. BCG and respiratory effort signals were extracted to serve the HF detection. Finally, the improved HF detection performance was verified through the LOO and the LOSO cross-validation settings with different machine learning classifiers. Results: The proposed machine learning-aided scheme achieved the robust performance in the HF detection by using 4 different classifiers, and yielded an accuracy of 94.97% and 87.00% in the LOO and the LOSO experiments, respectively. In addition, experimental results demonstrated that the designed respiratory and cardiopulmonary features are beneficial to the HF detection (LVEF Conclusion: This study proposed a machine learning-aided HF diagnostic scheme. Experimental results demonstrated that the proposed scheme can fully exploit the relationship between the heart and the lung systems to potentially improve the in-home HF detection performance by using both the BCG, the respiratory and the cardiopulmonary-related features.

Viral Sepsis, No Neglecting Complication Following Novel Corona Virus Disease (COVID-19) (preprint)

Background Until 24:00 of April 26th 2020, 2,918,268 laboratory-confirmed cases of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection have been reported worldwide, including 203,528 deaths. Bacterial infection is the main cause of sepsis, however, sepsis caused by virus is often ignored. Increased awareness, early recognition of viral sepsis, rapid administration of appropriate antiviral drugs, and urgent treatment can significantly reduce deaths of viral sepsis.Objectives Given the rapid global spread of novel Corona Virus Disease (COVID-19), coupled with the high rate of missed diagnosis of viral sepsis caused by SARS-CoV-2 infection, it is urgent to evaluate the multiple organ failure score and viral sepsis in COVID-19 patients, so as to determine the clinical characteristics of viral sepsis more accurately and reveal the risk factors related to mortality.Methods Here we provide a full description of three cases of viral sepsis and subsequent multiple organ dysfunction (MODS) caused by SARS-CoV-2 infection imported to Guiyang from Wuhan.Results Complete laboratory examination, imaging data and treatment methods for the patients are analyzed. Sepsis-related Organ Failure Assessment score (SOFA score) and Multiple organ dysfunction scores (MOD score) were daily assessed, aim to elucidate the clinical feature of viral sepsis and MODS and to attract enough attention by clinicians.Conclusions Therefore, we strongly suggest to daily evaluate SOFA score and MOD score in severe and critically-ill COVID-19 patients, so as to early diagnose and prevention of sepsis and MODS.

Successful treatment for the first confirmed noval coronavirus-infected pneumonia in Guizhou Province / 中华危重病急救医学

Noval coronavirus-infected pneumonia is an acute respiratory infectious diease caused by an noval coronavirus, and it is highly contagious. The first confirmed coronavirus-infected pneumonia in Guizhou was admitted to the department of Critical Care Medicine, Affiliated Hospital of Guizhou Medical University. After has been given isolation, anti-viral therapy, oxygen therapy, maintaining internal environment stability, organ functions protection and psychological comfort for 8 days, the patient successfully recovered from the disease. It is suggested that early recognition, early isolation, timely antiviral treatment, organ function protection and psychological intervention are effective methods for patient with noval coronavirus-infected pneumonia.