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INTRODUCTION: With the continuous progress of the medical Internet of Things, intelligent medical wearable devices are also gradually mature. Among them, medical wearable devices for arrhythmia detection have broad application prospects. Arrhythmia is a common cardiovascular disease. Arrhythmia causes millions of deaths every year and is one of the most noteworthy diseases. Medical mobile information systems (MMIS) provide many ECG signals, which can be used to train deep models to detect arrhythmia automatically. OBJECTIVES: Using deep models to detect arrhythmia is a research hot spot. However, the current algorithms for arrhythmia detection lack of attention to the unsupervised depth model. And they usually build a large comprehensive model for all users for arrhythmia detection, which has low flexibility and cannot extract personalized features from users. Therefore, this paper proposes a personalized arrhythmia detection system based on attention mechanism called personAD. METHODS: The personAD contains four modules: (1) Preprocessing module; (2) Training module; (3) Arrhythmia detection module and (4) User registration module. The personAD trains a separate autoencoder for each user to detect personalized arrhythmia. Using autoencoder to detect arrhythmia can avoid the imbalance of training data. The autoencoder combines a convolutional network and two attention mechanisms. RESULTS: Based on the results on MIT-BIH Arrhythmia Database, we can find that our arrhythmia detection system achieve 98.03% ACC and 99.32% AUC respectively. CONCLUSION: The personAD can effectively detect arrhythmia in ECG signals. The personAD has higher flexibility, and can easily modify the autoencoders for detecting arrhythmia for users. [ABSTRACT FROM AUTHOR]

Antisocial behavior (ASB) is one of the ten personality disorders included in ‘The Diagnostic and Statistical Manual of Mental Disorders (DSM-5) and falls in the same cluster as Borderline Personality Disorder, Histrionic Personality Disorder, and Narcissistic Personality Disorder. It is a prevalent pattern of disregard for and violation of the rights of others. Online antisocial behavior is a social problem and a public health threat. An act of ASB might be fun for a perpetrator; however, it can drive a victim into depression, self-confinement, low self-esteem, anxiety, anger, and suicidal ideation. Online platforms such as Twitter and Reddit can sometimes become breeding grounds for such behavior by allowing people suffering from ASB disorder to manifest their behavior online freely. In this paper, we propose a proactive approach based on natural language processing and deep learning that can enable online platforms to actively look for the signs of antisocial behavior and intervene before it gets out of control. By actively searching for such behavior, social media sites can prevent dire situations leading to someone committing suicide. [ABSTRACT FROM AUTHOR]