CNT/PDMS composite flexible dry electrodes for long-term ECG monitoring

We fabricated a carbon nanotube (CNT)/ polydimethylsiloxane (PDMS) composite-based dry ECG electrode that can be readily connected to conventional ECG devices, and showed its long-term wearable monitoring capability and robustness to motion and sweat. While the dispersion of CNTs in PDMS is challenging, we optimized the process to disperse untreated CNTs within PDMS by mechanical force only. The electrical and mechanical characteristics of the CNT/PDMS electrode were tested according to the concentration of CNTs and its thickness. The performances of ECG electrodes were evaluated by using 36 types of electrodes which were fabricated with different concentrations of CNTs, and with a differing diameter and thickness. The ECG signals were obtained by using electrodes of diverse sizes to observe the effects of motion and sweat, and the proposed electrode was shown to be robust to both factors. The CNT concentration and diameter of the electrodes were critical parameters in obtaining high-quality ECG signals. The electrode was shown to be biocompatible from the cytotoxicity test. A seven-day continuous wearability test showed that the quality of the ECG signal did not degrade over time, and skin reactions such as itching or erythema were not observed. This electrode could be used for the long-term measurement of other electrical biosignals for ubiquitous health monitoring including EMG, EEG, and ERG.     

Body movement activity recognition for ambulatory cardiac monitoring

Wearable electrocardiogram (W-ECG) recorders are increasingly in use by people suffering from cardiac abnormalities who also choose to lead an active lifestyle. The challenge presently is that the ECG signal is influenced by motion artifacts induced by body movement activity (BMA) of the wearer. The usual practice is to develop effective filtering algorithms which will eliminate artifacts. Instead, our goal is to detect the motion artifacts and classify the type of BMA from the ECG signal itself. We have recorded the ECG signals during specified BMAs, e.g., sitting still, walking, movements of arms and climbing stairs, etc. with a single-lead system. The collected ECG signal during BMA is presumed to be an additive mix of signals due to cardiac activities, motion artifacts and sensor noise. A particular class of BMA is characterized by applying eigen decomposition on the corresponding ECG data. The classification accuracies range from 70% to 98% for various class combinations of BMAs depending on their uniqueness based on this technique. The above classification is also useful for analysis of P and T waves in the presence of BMA.     

Real-Time Analysis of Physiological Data to Support Medical Applications

This paper presents a flexible framework that performs real-time analysis of physiological data to monitor people's health conditions in any context (e.g., during daily activities, in hospital environments). Given historical physiological data, different behavioral models tailored to specific conditions (e.g., a particular disease, a specific patient) are automatically learnt. A suitable model for the currently monitored patient is exploited in the real-time stream classification phase. The framework has been designed to perform both instantaneous evaluation and stream analysis over a sliding time window. To allow ubiquitous monitoring, real-time analysis could also be executed on mobile devices. As a case study, the framework has been validated in the intensive care scenario. Experimental validation, performed on 64 patients affected by different critical illnesses, demonstrates the effectiveness and the flexibility of the proposed framework in detecting different severity levels of monitored people's clinical situations.     

R-wave detection in the presence of muscle artifacts

Ambulatory electrocardiogram (ECG) recordings from patients undergoing severe physical stress are corrupted by large muscle artifacts (EMG) and other noise sources, such as baseline drifts and currents induced by motion artifacts and electromechanical devices. A simple and reliable QRS filter and R-wave detectot unit, for use under moderate to low QRS signal-to-noise ratio conditions, is described. It is found that a narrow bandpass sampled data filter design with a center frequency and bandwidth equal to 4 Hz maximizes the QRS signal-to-noise ratio under such conditions. The detector includes automatic gain control and a differencing integrator for improved R-wave detection.     

QRS Detection Based on Morphological Filter and Energy Envelope for Applications in Body Sensor Networks

Emerging body sensor networks (BSN) provide solutions for continuous health monitoring at anytime and from anywhere. The implementation of these monitoring solutions requires wearable sensor devices and thus creates new technology challenges in both software and hardware. This paper presents a QRS detection method for wearable Electrocardiogram (ECG) sensor in body sensor networks. The success of proposed method is based on the combination of two computationally efficient procedures, i.e., single-scale mathematical morphological (MM) filter and approximated envelope. The MM filter removes baseline wandering, impulsive noise and the offset of DC component while the approximated envelope enhances the QRS complexes. The performance of the algorithm is verified with standard MIT-BIH arrhythmia database as well as exercise ECG data. It achieves a low detection error rate of 0.42% based on the MIT-BIH database.     

Flexible Weaving Constructed Self‐Powered Pressure Sensor Enabling Continuous Diagnosis of Cardiovascular Disease and Measurement of Cuffless Blood Pressure

Pulse wave carries comprehensive information regarding the human cardiovascular system (CS), which is essential for directly capturing CS parameters. More importantly, cuffless blood pressure (BP) is one of the most critical markers in CS. Accurately measuring BP via the pulse wave for continuous and noninvasive diagnosis of a disease associated with hypertension remains a challenge and highly desirable. Here, a flexible weaving constructed self‐powered pressure sensor (WCSPS) is reported for measurement of the pulse wave and BP in a noninvasive manner. The WCSPS holds an ultrasensitivity of 45.7 mV Pa^?1 with an ultrafast response time of less than 5 ms, and no performance degradation is observed after up to 40 000 motion cycles. Furthermore, a low power consumption sensor system is developed for precisely monitoring pulse wave from the fingertip, wrist, ear, and ankles. A practical measurement is performed with 100 people with ages spanning from 24 to 82 years and different health statuses. The discrepancy between the measured BP results using the WCSPS and that provided by the commercial cuff‐based device is about 0.87–3.65%. This work demonstrates an efficient and cost‐effective way for human health monitoring, which would be a competitive alternative to current complex cardiovascular monitoring systems.     

Bioinspired Intervertebral Disc with Multidirectional Stiffness Prepared via Multimaterial Additive Manufacturing

Degenerative disc disease (DDD) has become a significant public health issue worldwide. This can result in loss of spinal function affecting patient health and quality of life. Artificial total disc replacement (A-TDR) is an effective approach for treating symptomatic DDD that compensates for lost functionality and helps patients perform daily activities. However, because current A-TDR devices lack the unique structure and material characteristics of natural intervertebral discs (IVDs), they fail to replicate the multidirectional stiffness needed to match physiological motions and characterize anisotropic behavior. It is still unclear how the multidirectional stiffness of the disc is affected by structural parameters and material characteristics. Herein, a bioinspired intervertebral disc (BIVD-L) based on a representative human lumbar segment is developed. The proposed BIVD-L reproduces the multidirectional stiffness needed for the most common physiological kinematic behaviors. The results demonstrate that the multidirectional stiffness of the BIVD-L can be regulated by structural and material parameters. The results of this research deepen knowledge of the biomechanical behavior of the human lumbar disc and may provide new inspirations for the design and fabrication of A-TDR devices for both engineering and functional applications.     

Flexible Hybrid Electronics: Direct Interfacing of Soft and Hard Electronics for Wearable Health Monitoring

The interfacing of soft and hard electronics is a key challenge for flexible hybrid electronics. Currently, a multisubstrate approach is employed, where soft and hard devices are fabricated or assembled on separate substrates, and bonded or interfaced using connectors; this hinders the flexibility of the device and is prone to interconnect issues. Here, a single substrate interfacing approach is reported, where soft devices, i.e., sensors, are directly printed on Kapton polyimide substrates that are widely used for fabricating flexible printed circuit boards (FPCBs). Utilizing a process flow compatible with the FPCB assembly process, a wearable sensor patch is fabricated composed of inkjet‐printed gold electrocardiography (ECG) electrodes and a stencil‐printed nickel oxide thermistor. The ECG electrodes provide 1 mVp–p ECG signal at 4.7 cm electrode spacing and the thermistor is highly sensitive at normal body temperatures, and demonstrates temperature coefficient, α ≈ –5.84% K^–1 and material constant, β ≈ 4330 K. This sensor platform can be extended to a more sophisticated multisensor platform where sensors fabricated using solution processable functional inks can be interfaced to hard electronics for health and performance monitoring, as well as internet of things applications.     

The energy-efficient implementation of an adaptive-filtering-based QRS complex detection method for wearable devices

Electrocardiogram (ECG) can be used as a valid way for diagnosing heart disease. To fulfill ECG processing in wearable devices by reducing computation complexity and hardware cost, two kinds of adaptive filters are designed to perform QRS complex detection and motion artifacts removal, respectively. The proposed design achieves a sensitivity of 99.49% and a positive predictivity of 99.72%, tested under the MIT-BIH ECG database. The proposed design is synthesized under the SMIC 65-nm CMOS technology and verified by post-synthesis simulation. Experimental results show that the power consumption and area cost of this design are of 160 μW and 1.09×10⁵ μm², respectively.     

一种心电监测和房颤预警系统的研究

随着社会进步和经济的发展,在人们生活水平提高的同时,各种心血管疾病成为人类健康的隐形杀手。本文提出了一种基于嵌入式架构的便携式心电监测和房颤预警系统,实现了受试者心电信号实时采集监测的功能,采集数据通过无线网络传输至云端进行存储,监测者可以远程获取云上存储的心电监测数据,下载并采用基于主分量解析分析去获取受试者心电房颤的特征信息,准确判断受试者是否出现房颤的情况,从而实现受试者的心脏健康状况,监测结果可以在监测设备显示屏以及远程安卓手机的APP程序中进行显示。本研究所开发的系统具有成本低廉、操作简便、运行可靠等优势。     

Flexible Piezoresistive Sensor Patch Enabling Ultralow Power Cuffless Blood Pressure Measurement

Noninvasive and real‐time cuffless blood pressure (BP) measurement realizes the idea of unobtrusive and continuous BP monitoring which is essential for diagnosis and prevention of cardiovascular diseases associated with hypertension. In this paper, a wearable sensor patch system that integrates flexible piezoresistive sensor (FPS) and epidermal electrocardiogram (ECG) sensors for cuffless BP measurement is presented. By developing parametric models on the FPS sensing mechanism and optimizing operational conditions, a highly stable epidermal pulse monitoring method is established and beat‐to‐beat BP measurement from the ECG and epidermal pulse signals is demonstrated. In particular, this study highlights the compromise between sensor sensitivity and signal stability. As compared with the current optical‐based cuffless BP measurement devices, the sensing patch requires much lower power consumption (3 nW) and is capable of detecting subtle physiological signal variations, e.g., pre and postexercises, thus providing a promising solution for low‐power, real‐time, and home‐based BP monitoring.     

Time-based compression and classification of heartbeats

Heart function measured by electrocardiograms (ECG) is crucial for patient care. ECG generated waveforms are used to find patterns of irregularities in cardiac cycles in patients. In many cases, irregularities evolve over an extended period of time that requires continuous monitoring. However, this requires wireless ECG recording devices. These devices consist of an enclosed system that includes electrodes, processing circuitry, and a wireless communication block imposing constraints on area, power, bandwidth, and resolution. In order to provide continuous monitoring of cardiac functions for real-time diagnostics, we propose a methodology that combines compression and analysis of heartbeats. The signal encoding scheme is the time-based integrate and fire sampler. The diagnostics can be performed directly on the samples avoiding reconstruction required by the competing finite rate of innovation and compressed sensing. As an added benefit, our scheme provides an efficient hardware implementation and a compressed representation for the ECG recordings, while still preserving discriminative features. We demonstrate the performance of our approach through a heartbeat classification application consisting of normal and irregular heartbeats known as arrhythmia. Our approach that uses simple features extracted from ECG signals is comparable to results in the published literature.     

Nonwoven fabric active electrodes for biopotential measurement during normal daily activity

Body movement is responsible for most of the interference during physiological data acquisition during normal daily activities. In this paper, we introduce nonwoven fabric active electrodes that provide the comfort required for clothing while robustly recording physiological data in the presence of body movement. The nonwoven fabric active electrodes were designed and fabricated using both hand- and screen-printing thick-film techniques. Nonstretchable nonwoven (Evolon 100) was chosen as the flexible fabric substrate and a silver filled polymer ink (Creative Materials CMI 112-15) was used to form a transducer layer and conductive lines on the nonwoven fabrics. These nonwoven fabric active electrodes can be easily integrated into clothing for wearable health monitoring applications. Test results indicate that nonwoven textile-based sensors show considerable promise for physiological data acquisition in wearable healthcare monitoring applications.     

Preface to the Special Topic on Devices and Circuits for Wearable and IoT Systems

The concept of Internet of Things (IoT) was first proposed by MIT Prof.Kevin Ash-ton in 1999.The implementation of IoT was mainly through RFID in its early time.With advanced technology and manufacture,diverse implementation forms ofIoT are becoming possible.Wearable devices,as an essential branch of IoT,will have broad application prospects in health monitoring and intelligent healthcare.     

采矿工人生理状况监测系统设计

采矿工人生理状况监测系统是工人矿井下作业时,进行实时、连续、长时间地采集、监测心电、呼吸、体温、血氧饱和度和体动等参数,并实现数据无线传输的系统。针对传统监控设备对工人状态掌控缺乏、矿难频发等重大问题,设计了一款无线、可穿戴、无创、低心理负荷的多参数采矿工人生理状况监测系统,以便准确地了解井下工人生理状况,及时预防危险状况发生,安全顺利地完成采矿工作。     

A Practical Traffic Scheduling Scheme for Differentiated Services of Healthcare Systems on Wireless Sensor Networks

Wireless sensor networks have recently been extensively researched due to the flexibility and cost savings they provide. One of the most promising applications of sensor networks is human health monitoring: wireless sensors are placed on the human body to form a wireless body network where the sensor node can continuously monitor real-time physiological parameters or human activities (motion detection). However, along with the flexibility, many problems arise due to a number of factors, including the bad quality of transmission media and the scarcity of resources. Moreover, sensor networks have different characteristics such as a variety of devices, different generated data, etc. From a quality of service (QoS) point of view, the healthcare domain can be seen as a real-time application demand to consider application requirements. Healthcare domains principally have stringent delay and loss requirements. Thus, considering different capabilities and ensuring time data delivery become necessary. Because wireless body area networks (WBAN) deal with human life, any delayed or lost data can endanger the user’s life. This paper proposes a differentiated traffic and scheduling scheme for WBAN. It is based on patients’ data classification and prioritization according to their current status and diseases. Through queue scheduling and path choice issues, the urgent data are delivered on time to provide a QoS guarantee for WBAN. Finally, it is shown that the proposed scheme is efficient for timely data transfer in WBAN.     

Noise cancellation signal processing method and computer system for improved real-time electrocardiogram artifact correction during MRI data acquisition

A system was developed for real-time electrocardiogram (ECG) analysis and artifact correction during magnetic resonance (MR) scanning, to improve patient monitoring and triggering of MR data acquisitions. Based on the assumption that artifact production by magnetic field gradient switching represents a linear time invariant process, a noise cancellation (NC) method is applied to ECG artifact linear prediction. This linear prediction is performed using a digital finite impulse response (FIR) matrix, that is computed employing ECG and gradient waveforms recorded during a training scan. The FIR filters are used during further scanning to predict artifacts by convolution of the gradient waveforms. Subtracting the artifacts from the raw ECG signal produces the correction with minimal delay. Validation of the system was performed both off-line, using prerecorded signals, and under actual examination conditions. The method is implemented using a specially designed Signal Analyzer and Event Controller (SAEC) computer and electronics. Real-time operation was demonstrated at 1 kHz with a delay of only 1 ms introduced by the processing. The system opens the possibility of automatic monitoring algorithms for electrophysiological signals in the MR environment.     

Multifunctional Fiber for Synchronous Bio-Sensing and Power Supply in Sweat Environment

Continuous monitoring of human sweat, which enables highly sensitive multiple biomarkers analysis with a portable power supply, is increasingly desired for the remote healthcare industry. Smart fibers with on-demand functionality are ideal candidates for fabricating noninvasive and conformal bioelectronic devices owing to their considerable flexibility. Herein, a multifunctional textile patch based on a reduced graphene oxide (rGO)/tetra-aniline (TANi) fiber for simultaneous biomarker monitoring and energy supply is reported. Benefiting from the multi-electrochemical redox states and proton doping/dedoping characteristics of TANi, rGO/TANi hybrid fibers are combined into an energy storage device and biosensor in a physiological environment. GO flakes increase the viscoelasticity of the wet-spinning dope by regulating the noncovalent interactions between TANi aggregates, thereby enhancing the mechanical strength and conductivity of the resulting rGO/TANi hybrid fiber. Consequently, the hybrid fiber exhibits high volumetric capacitance and versatile sensing capability for various physiological analytes, e.g., pH, K⁺, and glucose in sweat electrolyte. Furthermore, a wireless continuous sweat monitoring system is constructed by integrating the multifunctional fiber sensor into a printed electric circuit board with programmed functionality. Such a delicate integrated design that harnesses the signal collection and communication units is anticipated to facilitate practical applications in personalized diagnostic and physiological health.     

Body Heat Powered Wirelessly Wearable System for Real-time Physiological and Biochemical Monitoring

Through harvesting energy from the environment or human body, self-power wearable electronics have an opportunity to break through the limitations of battery supply and achieving long-term continuous operation. Here, a wireless wearable monitoring system driven entirely by body heat is implemented. Based on the principle of maximizing heat utilization, while optimizing internal resistance and heat dissipation, the stretchable TEG improves the power density of previous similar devices from only a few microwatts per square centimeter to tens and makes it possible to continuously drive wireless wearable electronic systems. Furthermore, ceaseless self-power energy gives wearable electronics unparalleled continuous working ability, which can realize the tracking and monitoring of biochemical and physiological indicators at different time scale. A practical system demonstrates the ability to real-time monitor heart rate, sweat ingredient and body motion at a high sampling rate. This study marks an important advance of self-powered wearable electronics for wirelessly real-time healthy monitoring.     

Low‐cost wireless sensor networks for remote cardiac patients monitoring applications

One of today's most pressing matters in medical care is response time to patients in need. Scope of this research is to suggest a solution that would help reduce response time in emergency situations utilizing technologies of wireless sensor networks. The enhanced power efficiency, minimized production cost, condensed physical layout, and reduced wired connections present a much more proficient and simplified approach to the continuous monitoring of patients' physiological status. The proposed sensor network system is composed of wearable vital sign sensors and a workstation monitor. The wearable platforms are to be distributed to patients of concern. The wearable platforms can provide continuous electrocardiogram (ECG) monitoring by measuring electrical potentials between various points of the body using a galvanometer. They will then relay the ECG signals wirelessly to the workstation monitor. In addition to displaying the data, the workstation will also perform signal wavelet transformation for ECG characteristic extractions. Copyright © 2007 John Wiley & Sons, Ltd.