An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

Deep learning approach for active classification of electrocardiogram signals

A review of smart home applications based on Internet of Things

A survey on ECG analysis

Traditional wearable devices have various shortcomings, such as uncomfortableness for long-term wearing, and insufficient accuracy, etc. Thus, health monitoring through traditional wearable devices is hard to be sustainable. In order to obtain healthcare big data by sustainable health monitoring, we design "Smart Clothing", facilitating unobtrusive collection of various physiological indicators of human body. To provide pervasive intelligence for smart clothing system, mobile healthcare cloud platform is constructed by the use of mobile internet, cloud computing and big data analytics. This paper introduces design details, key technologies and practical implementation methods of smart clothing system. Typical applications powered by smart clothing and big data clouds are presented, such as medical emergency response, emotion care, disease diagnosis, and real-time tactile interaction. Especially, electrocardiograph signals collected by smart clothing are used for mood monitoring and emotion detection. Finally, we highlight some of the design challenges and open issues that still need to be addressed to make smart clothing ubiquitous for a wide range of applications.

Smart Clothing: Connecting Human with Clouds and Big Data for Sustainable Health Monitoring

Myocardial infarction (MI), also known as a heart attack, is the leading cause of death in the U.S. It often occurs due to the occlusion of coronary arteries, thereby leading to insufficient blood and oxygen supply that damage cardiac muscle cells. Because blood vessels are branching throughout the heart, MI occurs at different spatial locations (e.g., anterior and inferior portions) of the heart. The spatial location of the diseased is rupts normal excitation and propagation of cardiac electrical activity in space and time. Most previous studies focused on the relationships between disease and time-domain biomarkers from 12-lead ECG signals (e.g., Q wave, QT interval, ST elevation/depression, T wave). Few, if any, previous approaches investigated how the spatial location of diseases will alter cardiac vectorcardiogram (VCG) signals in both space and time. This paper presents a novel spatiotemporal warping approach to quantify the dissimilarity of disease-altered patterns in 3-lead spatiotemporal VCG signals. The hypothesis testing shows that there are significant spatiotemporal differences between healthy control, MI-anterior, MI-anterior-septal, MI-anterior-lateral, MI-inferior, and MI-inferior-lateral. Furthermore, we optimize the embedding of each functional recording as a feature vector in the high-dimensional space that preserves the dissimilarity distance matrix. This novel spatial embedding approach facilitates the construction of classification models and yields an averaged accuracy of 95.1% for separating MIs and Healthy Controls (HCs) and an averaged accuracy of 95.8% in identifying anterior-related MIs and inferior-related MIs.

Spatiotemporal Differentiation of Myocardial Infarctions

Parallel computing and network analytics for fast Industrial Internet-of-Things (IIoT) machine information processing and condition monitoring

Heterogeneous recurrence analysis of heartbeat dynamics for the identification of sleep apnea events

Internet of Things (IoT) provides an unprecedented opportunity to realize smart automated systems such as smart manufacturing, smart city and smart home in the past few years. Pervasive sensing and mobile technology deployed in large-scale IoT systems lead to the accumulation of big data. In particular, wearable biosensing accelerates human-centered computing for smart health management. However, limited work has been done to develop advanced IoT technologies for smart monitoring and control of heart health. There is an urgent need to develop a new IoT technology specific to the heart, namely Internet of Hearts (IOH) that will enable and assist (1) the acquisition of electrocardiogram (ECG) signals pertinent to space-time cardiac dynamics at anytime anywhere; (2) real-time management and compact representation of multi-sensor signals; (3) big data analytics in large-scale IoT contexts. This paper presents a new technology of Mobile and E-Network Smart Health (MESH), which is composed of 4 components as follows: 1) Mobile-based ECG sensing device; 2) Space-time representation of cardiac electrical activity; 3) Optimal model-based representation of ECG signals; 4) Dynamic network embedding for disease pattern recognition. Our preliminary experimental results demonstrated that network analytics is efficient and effective for smart health management in IoT contexts. The MESH technology shows strong potentials to provide an indispensable and enabling tool for realizing smart heart health and wellbeing for the population worldwide.

Chen Kan

Papers

Spatiotemporal Differentiation of Myocardial Infarctions

Parallel computing and network analytics for fast Industrial Internet-of-Things (IIoT) machine information processing and condition monitoring

Heterogeneous recurrence analysis of heartbeat dynamics for the identification of sleep apnea events

Mobile sensing and network analytics for realizing smart automated systems towards health Internet of Things

Heterogeneous recurrence monitoring of dynamic transients in ultraprecision machining processes