No abstract

To reduce the high mortality rate from cardiovascular disease (CVD), the electrocardiogram (ECG) beat plays a significant role in computer-aided arrhythmia diagnosis systems. However, the complex variations and imbalance of ECG beats make this a challenging issue. Since ECG beat data exist in heavily imbalanced category, an effective long short-term memory (LSTM) recurrence network model with focal loss (FL) is proposed. For this purpose, the LSTM network can disentangle the timing features in complex ECG signals, while the FL is used to resolve the category imbalance by downweighting easily identified normal ECG examples. The advantages of the proposed network have been verified in the MIT-BIH arrhythmia database. Experimental results show that the LSTM network with FL achieved a reliable solution to the problem of imbalanced datasets in ECG beat classification and was not sensitive to quality of ECG signals. The proposed method can be deployed in telemedicine scenarios to assist cardiologists into more accurately and objectively diagnosing ECG signals.

No abstract

No abstract

No abstract

Quantitative analysis and prediction can help to reduce the risk of cardiovascular disease. Quantitative prediction based on traditional model has low accuracy. The variance of model prediction based on shallow neural network is larger. In this paper, cardiovascular disease prediction model based on improved deep belief network (DBN) is proposed. Using the reconstruction error, the network depth is determined independently, and unsupervised training and supervised optimization are combined. It ensures the accuracy of model prediction while guaranteeing stability. Thirty experiments were performed independently on the Statlog (Heart) and Heart Disease Database data sets in the UCI database. Experimental results showed that the mean of prediction accuracy was 91.26% and 89.78%, respectively. The variance of prediction accuracy was 5.78 and 4.46, respectively.

No abstract

Zhongyong thinking is a common approach adopted by Chinese people to solve problems encountered in life and work. Based on the four modes of zhongyong thinking proposed by Pang (Social Sciences in China, 1, 1980, 75), this study chooses the “neither A nor B” form, which represents the “mean” (中) characteristics of zhongyong thinking, called eclectic thinking, and the “both A and B” form, which reflects the “harmony” (和) feature, called integrated thinking. This study primed eclectic thinking and integrated thinking, respectively, through self‐compiled problem situations, and 150 college students and postgraduates students were the participants. Experiment 1 explored the role of the priming of zhongyong thinking in three classic creative thinking tasks: a divergent thinking test, remote association test, and insight problem‐solving test. Experiment 2 further examined the effect of priming of zhongyong thinking on “market investment problems” with higher ecological validity. The findings show that priming integrated thinking can improve remote associates test performance and promote creative solutions to market investment problems, but there is no significant impact on the scores of divergent thinking test and insight problem‐solving; priming eclectic thinking has no significant impact on any of the subsequent creative tasks. This study shows that integrated thinking primes cognitive processing related to information association and information integration, promoting subsequent creative tasks.