A Low Power Cardiovascular Healthcare System With Cross-Layer Optimization From Sensing Patch to Cloud Platform

A Novel Instruction Driven 1-D CNN Processor for ECG Classification

Electrocardiography (ECG) has emerged as a ubiquitous diagnostic tool for the identification and characterization of diverse cardiovascular pathologies. Wearable health monitoring devices, equipped with on-device biomedical artificial intelligence (AI) processors, have revolutionized the acquisition, analysis, and interpretation of ECG data. However, these systems necessitate AI processors that exhibit flexible configuration, facilitate portability, and demonstrate optimal performance in terms of power consumption and latency for the realization of various functionalities. To address these challenges, this study proposes an instruction-driven convolutional neural network (CNN) processor. This processor incorporates three key features: (1) An instruction-driven CNN processor to support versatile ECG-based application. (2) A Processing element (PE) array design that simultaneously considers parallelism and data reuse. (3) An activation unit based on the CORDIC algorithm, supporting both Tanh and Sigmoid computations. The design has been implemented using 110 nm CMOS process technology, occupying a die area of 1.35 mm2 with 12.94 µW power consumption. It has been demonstrated with two typical ECG AI applications, including two-class (i.e., normal/abnormal) classification and five-class classification. The proposed 1-D CNN algorithm performs with a 97.95% accuracy for the two-class classification and 97.9% for the five-class classification, respectively.

EEG-Based Mental Workload Classification Method Based on Hybrid Deep Learning Model Under IoT

Automatically detecting human mental workload to prevent mental diseases is highly important. With the development of information technology, remote detection of mental workload is expected. The development of artificial intelligence and Internet of Things technology will also enable the identification of mental workload remotely based on human physiological signals. In this article, a method based on the spatial and time-frequency domains of electroencephalography (EEG) signals is proposed to improve the classification accuracy of mental workload. Moreover, a hybrid deep learning model is presented. First, the spatial domain features of different brain regions are proposed. Simultaneously, EEG time-frequency domain information is obtained based on wavelet transform. The spatial and time-frequency domain features are input into two types of deep learning models for mental workload classification. To validate the performance of the proposed method, the Simultaneous Task EEG Workload public database is used. Compared with the existing methods, the proposed approach shows higher classification accuracy. It provides a novel means of assessing mental workload.

A Flexible and Miniaturized Chest Patch for Real-time PPG/ECG/Bio-Z Monitoring

We proposed a lightweight and wearable chest patch for real-time monitoring of three vital signals, photoplethysmography (PPG), electrocardiography (ECG), and bioimpedance (Bio-Z). It comprises a flexible electrode patch and a miniaturized wireless signal acquisition module. Heart rate (HR), heart rate variability (HRV), and blood pressure (BP) can be extracted from the raw signals. The flexible electrode patch is comfortable for the user while maintaining stable contact with human skin, guaranteeing the wearability. Size of the signal acquisition module is only 17.3mm×14.5mm×9mm, and it weighs only 3.2g, including an 80mAh lithium polymer battery, which keeps the entire patch working for more than 4 hours. A host controller, involving a graphic user interface (GUI) is developed to receive and visualize the data from the chest patch. The proposed device successfully collected three vital signals with high signal quality and showed its potential in healthcare applications.

Platform for Healthcare Promotion and Cardiovascular Disease Prevention

This article presents the hardware-software design and implementation of an open, integrated, and scalable healthcare platform oriented to multiple point-care scenarios for healthcare promotion and cardiovascular disease prevention. The platform has the capability to provide continuous monitoring, extended device integration, strategies based on artificial intelligence for the information analysis and cybersecurity support, delivering a secure end-to-end hardware-software solution. This platform is used to perform the remote patient health monitoring and supervision by doctors, triage procedures in hospitals, or self-care monitoring using personal devices such as tablets and cellphones. The proposed hardware architecture facilitates the integration of biomedical data acquired from different health-point cares, collecting relevant information for the detection of cardiovascular risk through deep-learning algorithms. All these characteristics make our development a strong tool to perform epidemiological profiling and future implementation of strategies for comprehensive cardiovascular risk intervention. The components of the platform are described, and their main functionalities are highlighted.

Wearable Wireless Body Area Networks for Medical Applications

In recent times, there has been a significant growth in networks known as the wireless body area networks (WBANs). A WBAN connects distributed nodes throughout the human body, which can be placed on the skin, under the skin, or on clothing and can use the human body's electromagnetic waves. An approach to reduce the size of different telecommunication equipment is constantly being sought; this allows these devices to be closer to the body or even glued and embedded within the skin without making the user feel uncomfortable or posing as a danger for the user. These networks promise new medical applications; however, these are always based on the freedom of movement and the comfort they offer. Among the advantages of these networks is that they can significantly increase user's quality of life. For example, a person can carry a WBAN with built-in sensors that calculate the user's heart rate at any given time and send these data over the internet to user's doctor. This study provides a systematic review of WBAN, describing the applications and trends that have been developed with this type of network and, in addition, the protocols and standards that must be considered.