Hydrogen sulfide （H2S） is the third gaseous signaling molecule discovered in recent years, and plays an important physiological role in the cardivascular system. To explore the effects of different doses of exogenou...Hydrogen sulfide （H2S） is the third gaseous signaling molecule discovered in recent years, and plays an important physiological role in the cardivascular system. To explore the effects of different doses of exogenous H2S on the electrocardiogram （ECG） of rats generally anesthetized by zoletil, different doses of NariS solution were used for the intervention of intraperitoneal injection 20 rain before the zoletil anesthesia. The ECGs of rats from each treatment group during the time range of 10^th-50^th min were determined under general anesthesia, and then were compared with those from the control group. The results showed that exogenous H2S could significantly reduce the Q-T interval time limit, thus played a role in slowing tachycardia or arrhythmia and other anomalies, thereby protecting the heart. S-T segment and T segment evaluation values were significantly reduced, which might be associated with bradycardia.展开更多
This study introduces a new classifier tailored to address the limitations inherent in conventional classifiers such as K-nearest neighbor(KNN),random forest(RF),decision tree(DT),and support vector machine(SVM)for ar...This study introduces a new classifier tailored to address the limitations inherent in conventional classifiers such as K-nearest neighbor(KNN),random forest(RF),decision tree(DT),and support vector machine(SVM)for arrhythmia detection.The proposed classifier leverages the Chi-square distance as a primary metric,providing a specialized and original approach for precise arrhythmia detection.To optimize feature selection and refine the classifier’s performance,particle swarm optimization(PSO)is integrated with the Chi-square distance as a fitness function.This synergistic integration enhances the classifier’s capabilities,resulting in a substantial improvement in accuracy for arrhythmia detection.Experimental results demonstrate the efficacy of the proposed method,achieving a noteworthy accuracy rate of 98% with PSO,higher than 89% achieved without any previous optimization.The classifier outperforms machine learning(ML)and deep learning(DL)techniques,underscoring its reliability and superiority in the realm of arrhythmia classification.The promising results render it an effective method to support both academic and medical communities,offering an advanced and precise solution for arrhythmia detection in electrocardiogram(ECG)data.展开更多
Cardiovascular disease persists as the primary cause of human mortality,significantly impacting healthy life expectancy.The routine electrocardiogram(ECG)stands out as a pivotal noninvasive diagnostic tool for identif...Cardiovascular disease persists as the primary cause of human mortality,significantly impacting healthy life expectancy.The routine electrocardiogram(ECG)stands out as a pivotal noninvasive diagnostic tool for identifying arrhythmias.The evolving landscape of fabric electrodes,specifically designed for the prolonged monitoring of human ECG signals,is the focus of this research.Adhering to the preferred reporting items for systematic reviews and meta-analyses(PRISMA)statement and assimilating data from 81 pertinent studies sourced from reputable databases,the research conducts a comprehensive systematic review and meta-analysis on the materials,fabric structures and preparation methods of fabric electrodes in the existing literature.It provides a nuanced assessment of the advantages and disadvantages of diverse textile materials and structures,elucidating their impacts on the stability of biomonitoring signals.Furthermore,the study outlines current developmental constraints and future trajectories for fabric electrodes.These insights could serve as essential guidance for ECG monitoring system designers,aiding them in the selection of materials that optimize the measurement of biopotential signals.展开更多
文摘Hydrogen sulfide （H2S） is the third gaseous signaling molecule discovered in recent years, and plays an important physiological role in the cardivascular system. To explore the effects of different doses of exogenous H2S on the electrocardiogram （ECG） of rats generally anesthetized by zoletil, different doses of NariS solution were used for the intervention of intraperitoneal injection 20 rain before the zoletil anesthesia. The ECGs of rats from each treatment group during the time range of 10^th-50^th min were determined under general anesthesia, and then were compared with those from the control group. The results showed that exogenous H2S could significantly reduce the Q-T interval time limit, thus played a role in slowing tachycardia or arrhythmia and other anomalies, thereby protecting the heart. S-T segment and T segment evaluation values were significantly reduced, which might be associated with bradycardia.
文摘This study introduces a new classifier tailored to address the limitations inherent in conventional classifiers such as K-nearest neighbor(KNN),random forest(RF),decision tree(DT),and support vector machine(SVM)for arrhythmia detection.The proposed classifier leverages the Chi-square distance as a primary metric,providing a specialized and original approach for precise arrhythmia detection.To optimize feature selection and refine the classifier’s performance,particle swarm optimization(PSO)is integrated with the Chi-square distance as a fitness function.This synergistic integration enhances the classifier’s capabilities,resulting in a substantial improvement in accuracy for arrhythmia detection.Experimental results demonstrate the efficacy of the proposed method,achieving a noteworthy accuracy rate of 98% with PSO,higher than 89% achieved without any previous optimization.The classifier outperforms machine learning(ML)and deep learning(DL)techniques,underscoring its reliability and superiority in the realm of arrhythmia classification.The promising results render it an effective method to support both academic and medical communities,offering an advanced and precise solution for arrhythmia detection in electrocardiogram(ECG)data.
文摘Cardiovascular disease persists as the primary cause of human mortality,significantly impacting healthy life expectancy.The routine electrocardiogram(ECG)stands out as a pivotal noninvasive diagnostic tool for identifying arrhythmias.The evolving landscape of fabric electrodes,specifically designed for the prolonged monitoring of human ECG signals,is the focus of this research.Adhering to the preferred reporting items for systematic reviews and meta-analyses(PRISMA)statement and assimilating data from 81 pertinent studies sourced from reputable databases,the research conducts a comprehensive systematic review and meta-analysis on the materials,fabric structures and preparation methods of fabric electrodes in the existing literature.It provides a nuanced assessment of the advantages and disadvantages of diverse textile materials and structures,elucidating their impacts on the stability of biomonitoring signals.Furthermore,the study outlines current developmental constraints and future trajectories for fabric electrodes.These insights could serve as essential guidance for ECG monitoring system designers,aiding them in the selection of materials that optimize the measurement of biopotential signals.
