Polyaziridine-Encapsulated Phosphorene-Incorporated Flexible 3D Porous Graphene for Multimodal Sensing and Energy Storage Applications

Heteroatom-incorporated graphene represents a prominent family of materials utilized as active electrodes for multimodal sensing and energy storage applications. Herein, a novel polyaziridine-encapsulated phosphorene (PEP)-incorporated flexible 3D porous graphene (3DPG) electrode is developed using facile, cost-effective laser writing, and drop-casting techniques. Owing to the excellent electrochemical characteristics and surface functionality of the highly stable PEP, the fabricated PEP/3DPG is evaluated as a potential electrode for immunosensing, electrocardiogram (ECG) recording, and microsupercapacitors (MSCs). Under optimized conditions, the produced PEP/3DPG-based carcinoembryonic immunosensor exhibits linear ranges of 0.1–700 pg mL⁻¹ and 1–100 ng mL⁻¹ with a detection limit of 0.34 pg mL⁻¹ and high selectivity. The finger touch-based ECG sensor demonstrates a relatively low and stable impedance at the skin-electrode interface; therefore, the signal-to-noise ratio of the ECG signal received from the fabricated sensor (13.5 dB) is comparable to that of conventional Ag/AgCl electrodes (13.9 dB). Besides, the highest areal capacitance of the prepared MSC reached a magnitude of 16.94 mF cm⁻², which is six times higher than that of a non-doped 3DPG-based MSC. These results demonstrate the effectiveness of the described fabrication procedure and the high utilization potential of the encapsulated phosphorene-doped 3D graphene in multimodal applications.     

具有运动伪迹消除功能的移动心电检测系统设计

设计并实现了一种可在人体移动状态下进行检测、采集及分析人体心电信号的系统,硬件设计上,采用高集成度专用心电信号前端处理集成电路进行心电信号的滤波及放大;软件设计上,采用小波阈值降噪对心电信号进行初级滤波,然后采用人体移动加速度传感器获取人体运动信息对心电信号进行自适应滤波,消除运动伪迹.在系统设计上,终端检测设备与手机终端采用蓝牙通信,便于用户测量.实验结果表明:本设计方案能根据人体运动状况,有效消除运动伪迹,保留心电信号的波形,可满足常规的移动心电检测应用.     

Automated Deep Learning Based Cardiovascular Disease Diagnosis Using ECG Signals

Automated biomedical signal processing becomes an essential process to determine the indicators of diseased states. At the same time, latest developments of artificial intelligence (AI) techniques have the ability to manage and analyzing massive amounts of biomedical datasets results in clinical decisions and real time applications. They can be employed for medical imaging; however, the 1D biomedical signal recognition process is still needing to be improved. Electrocardiogram (ECG) is one of the widely used 1-dimensional biomedical signals, which is used to diagnose cardiovascular diseases. Computer assisted diagnostic models find it difficult to automatically classify the 1D ECG signals owing to time-varying dynamics and diverse profiles of ECG signals. To resolve these issues, this study designs automated deep learning based 1D biomedical ECG signal recognition for cardiovascular disease diagnosis (DLECG-CVD) model. The DLECG-CVD model involves different stages of operations such as pre-processing, feature extraction, hyperparameter tuning, and classification. At the initial stage, data pre-processing takes place to convert the ECG report to valuable data and transform it into a compatible format for further processing. In addition, deep belief network (DBN) model is applied to derive a set of feature vectors. Besides, improved swallow swarm optimization (ISSO) algorithm is used for the hyperparameter tuning of the DBN model. Lastly, extreme gradient boosting (XGBoost) classifier is employed to allocate proper class labels to the test ECG signals. In order to verify the improved diagnostic performance of the DLECG-CVD model, a set of simulations is carried out on the benchmark PTB-XL dataset. A detailed comparative study highlighted the betterment of the DLECG-CVD model interms of accuracy, sensitivity, specificity, kappa, Mathew correlation coefficient, and Hamming loss.     

用于心电信号采集的织物电极技术研究进展

针对传统Ag/AgCl胶状电极在长期心电监护中出现的问题和近年来可穿戴式健康监测的发展需求,对可用于心电信号采集的新型传感器技术——织物电极技术进行了综述。介绍了干电极、织物电极的概念;从有源电极和无源电极两方面分别对织物电极技术研究进展进行了分析;从制作工艺、导体材质、依托载体、电化学性能等织物电极技术进行了讨论;分析了目前织物电极在市场应用中的制约因素,并对未来织物电极技术的发展方向进行了展望。     

基于自适应多元多尺度熵的心电信号分类研究

针对目前单通道心电信号识别精度不高,现存多元分解方法效果不佳、多元非线性心电信号分析复杂等问题,提出了一种基于自适应多元多尺度色散熵的心电信号分类方法。首先利用频谱分析,创新性地引入了正弦辅助多元经验模态分解方法,对心电信号进行分解得到多元模态分量;然后结合多模态分解和色散熵的优越性,通过累加多元本征模态分量代替粗粒化采样,提出了自适应多元多尺度色散熵的方法获取特征熵值。最后将特征输入到多个分类器上进行分类,通过实验对比分析,在模拟信号和MIT-BIH数据上验证该方案的有效性。     

移动式人体生理体征测量系统

研究设计一种家用,对多种人体生理数据进行监测的监护系统。提出一种以MSP430微控制器为处理核心,以CC2540低功耗蓝牙4．0为无线数据传输的设计方案,包括工作原理、硬件设计和软件的设计实现。实验结果表明该系统能够可靠地采集并监测用户的ECG（心电图）、PPG（血氧波形图）、心率、spO2（血氧饱合度）、体温及血压数据,能与智能手机通讯并上传数据到远端的监护中心进行进一步分析与诊断。本系统为物联网技术和嵌入式系统在远程医疗健康监护中的应用提供了一套稳定可靠的解决方案。     

基于嵌入式技术的便携式心电监护仪设计

介绍一种基于嵌入式技术的心电监护仪的设计与实现方法,它具有实时采集、显示、心电信号自动分析处理、报警等功能。系统以基于ARM9内核的32位低功耗微处理器作为控制核心,在心电信号处理中应用了50Hz滤波及合适的R波检测等算法。该系统具有良好的可靠性和精确性,且操作简单,体积小,方便携带,很适合家庭和个人的需要,可作为医生对病人诊断、治疗、观察监护的辅助性设备。     

WSN24-Link无线传感器网络在心电监护系统中的应用

随着无线通信技术和电子器件技术的快速发展,促进了由传感器组成的无线传感网（WSN）的应用。由许多无线传感器组成的无线传感器网络被广泛应用于各种领域,如医疗、军事、化工、商业等。介绍了应用WSN24-Link无线传感器网络实现远程心电监护的设计,讨论了传感器网络的工作原理、硬件结构和软件系统。     

多参数监测仪生理信号检测技术的研究

介绍了多参数监测仪主要生理信号检测技术的方法及特点，分析这些方法在实际应用中的效果和研究方向。     

心电信号QRS波群检测算法研究

心电信号特征参数的提取和识别是心电图分析和诊断的基础．在心电信号的分析中,快速准确地检出QRS波群非常重要,它是计算相关参数和诊断的前提．该文对QRS波群的检测算法进行了研究,对传统差分阈值法在R波检测中存在的一些问题加以改进,将正向和倒置R波分开检测,提出了在自适应差分阈值法检测正向R波的基础上,用幅值基线比较法检测倒置R波的检测方法．在Q、S波检测方面,文章以差分法为基础,给出了Q、S波定位的一种简便易行的方法．利用美国麻省理工学院的MIT—BIH心电数据库和临床实测数据对以上方法进行验证,QRS波群的检出率高达99．4％以上．实验结果表明,该方法简单有效、准确率高,适于实际应用。