Research on a Fog Computing Architecture and BP Algorithm Application for Medical Big Data

Although the Internet of Things has been widely applied,the problems of cloud computing in the application of digital smart medical Big Data collection,processing,analysis,and storage remain,especially the low efficiency of medical diagnosis.And with the wide application of the Internet of Things and Big Data in the medical field,medical Big Data is increasing in geometric magnitude resulting in cloud service overload,insufficient storage,communication delay,and network congestion.In order to solve these medical and network problems,a medical big-data-oriented fog computing architec-ture and BP algorithm application are proposed,and its structural advantages and characteristics are studied.This architecture enables the medical Big Data generated by medical edge devices and the existing data in the cloud service center to calculate,compare and analyze the fog node through the Internet of Things.The diagnosis results are designed to reduce the business processing delay and improve the diagnosis effect.Considering the weak computing of each edge device,the artificial intelligence BP neural network algorithm is used in the core computing model of the medical diagnosis system to improve the system computing power,enhance the medical intelligence-aided decision-making,and improve the clinical diagnosis and treatment efficiency.In the application process,combined with the characteristics of medical Big Data technology,through fog architecture design and Big Data technology integration,we could research the processing and analysis of heterogeneous data of the medical diagnosis system in the context of the Internet of Things.The results are promising:The medical platform network is smooth,the data storage space is sufficient,the data processing and analysis speed is fast,the diagnosis effect is remarkable,and it is a good assistant to doctors’treatment effect.It not only effectively solves the problem of low clinical diagnosis,treatment efficiency and quality,but also reduces the waiting time of patients,effectively solves the contradiction between doctors and patients,and improves the medical service quality and management level.

Intelligent Electrocardiogram Analysis in Medicine:Data,Methods,and Applications

Electrocardiogram(ECG)is a low-cost,simple,fast,and non-invasive test.It can reflect the heart’s electrical activity and provide valuable diagnostic clues about the health of the entire body.Therefore,ECG has been widely used in various biomedical applications such as arrhythmia detection,disease-specific detection,mortality prediction,and biometric recognition.In recent years,ECG-related studies have been carried out using a variety of publicly available datasets,with many differences in the datasets used,data preprocessing methods,targeted challenges,and modeling and analysis techniques.Here we systematically summarize and analyze the ECGbased automatic analysis methods and applications.Specifically,we first reviewed 22 commonly used ECG public datasets and provided an overview of data preprocessing processes.Then we described some of the most widely used applications of ECG signals and analyzed the advanced methods involved in these applications.Finally,we elucidated some of the challenges in ECG analysis and provided suggestions for further research.

From solar terms to medical terms(Part Ⅰ): A first step with big data

The story of the Twenty-four Solar Terms(24-ST_s) is one of the most popular elements in Chinese culture, which has a profound influence on agriculture production, health care, and even daily life in both ancient and modern China. This traditional calendric system was invented by the Chinese ancestors through combining fundamental astronomical knowledge with climatic and phenological conditions in the Yellow River Basin some 2000 years ago. Although the basic philosophy of the 24-ST_s remains valid for the country as a whole to date, their regional robustness has been increasingly challenged by accumulating observational data in terms of temporal shift and spatial inhomogeneity. To tackle these issues, we propose to recalibrate the medically related critical timings of Great Heat and Great Cold in the classic ST system by using big meteorological data, and adjust them by introducing geographically correlated analytical models. As a result, a novel calendric system, called the Twenty-four Medical Terms(24-MT_s), has been developed as an upgraded version of the traditional 24-ST_s. The proposed 24-MT_s are characterized by two striking features with respect to the 24-ST_s: A varying duration of each MT instead of a fixed one for the ST, and a geographically dependent timing for each MT instead of a unified one for the entire nation. As such, the updated 24-MT_s are expected to provide a more realistic estimate of these critical timings around the year, and hence, a more precise guidance to agronomic planning and health care activity in China.