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
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
A critical component of dealing with heart disease is real-time identifi-cation,which triggers rapid action.The main challenge of real-time identification is illustrated here by the rare occurrence of cardiac arrhythm...A critical component of dealing with heart disease is real-time identifi-cation,which triggers rapid action.The main challenge of real-time identification is illustrated here by the rare occurrence of cardiac arrhythmias.Recent contribu-tions to cardiac arrhythmia prediction using supervised learning approaches gen-erally involve the use of demographic features(electronic health records),signal features(electrocardiogram features as signals),and temporal features.Since the signal of the electrical activity of the heartbeat is very sensitive to differences between high and low heartbeats,it is possible to detect some of the irregularities in the early stages of arrhythmia.This paper describes the training of supervised learning using features obtained from electrocardiogram(ECG)image to correct the limitations of arrhythmia prediction by using demographic and electrocardio-graphic signal features.An experimental study demonstrates the usefulness of the proposed Arrhythmia Prediction by Supervised Learning(APSL)method,whose features are obtained from the image formats of the electrocardiograms used as input.展开更多
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.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under Grant Number(R.G.P1/155/40/2019)。
文摘A critical component of dealing with heart disease is real-time identifi-cation,which triggers rapid action.The main challenge of real-time identification is illustrated here by the rare occurrence of cardiac arrhythmias.Recent contribu-tions to cardiac arrhythmia prediction using supervised learning approaches gen-erally involve the use of demographic features(electronic health records),signal features(electrocardiogram features as signals),and temporal features.Since the signal of the electrical activity of the heartbeat is very sensitive to differences between high and low heartbeats,it is possible to detect some of the irregularities in the early stages of arrhythmia.This paper describes the training of supervised learning using features obtained from electrocardiogram(ECG)image to correct the limitations of arrhythmia prediction by using demographic and electrocardio-graphic signal features.An experimental study demonstrates the usefulness of the proposed Arrhythmia Prediction by Supervised Learning(APSL)method,whose features are obtained from the image formats of the electrocardiograms used as input.
