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Efficient ECG classification based on Chi-square distance for arrhythmia detection
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作者 Dhiah Al-Shammary Mustafa Noaman Kadhim +2 位作者 Ahmed M.Mahdi Ayman Ibaida Khandakar Ahmedb 《Journal of Electronic Science and Technology》 EI CAS CSCD 2024年第2期1-15,共15页
This study introduces a new classifier tailored to address the limitations inherent in conventional classifiers such as K-nearest neighbor(KNN),random forest(RF),decision tree(DT),and support vector machine(SVM)for ar... This study introduces a new classifier tailored to address the limitations inherent in conventional classifiers such as K-nearest neighbor(KNN),random forest(RF),decision tree(DT),and support vector machine(SVM)for arrhythmia detection.The proposed classifier leverages the Chi-square distance as a primary metric,providing a specialized and original approach for precise arrhythmia detection.To optimize feature selection and refine the classifier’s performance,particle swarm optimization(PSO)is integrated with the Chi-square distance as a fitness function.This synergistic integration enhances the classifier’s capabilities,resulting in a substantial improvement in accuracy for arrhythmia detection.Experimental results demonstrate the efficacy of the proposed method,achieving a noteworthy accuracy rate of 98% with PSO,higher than 89% achieved without any previous optimization.The classifier outperforms machine learning(ML)and deep learning(DL)techniques,underscoring its reliability and superiority in the realm of arrhythmia classification.The promising results render it an effective method to support both academic and medical communities,offering an advanced and precise solution for arrhythmia detection in electrocardiogram(ECG)data. 展开更多
关键词 Arrhythmia classification Chi-square distance Electrocardiogram(ecg)signal Particle swarm optimization(PSO)
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基于卷积神经网络的ECG心律失常分类研究
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作者 杨风健 李小琪 李洪亮 《电子设计工程》 2024年第9期165-169,共5页
基于心电信号进行心律失常自动检测和分类识别研究,辅助临床医生进行心血管相关疾病诊断。采用MIT-BIH数据库作为数据源,对该数据库心电数据进行小波分解与重构去噪后,构建卷积神经网络模型,结合Adam优化器,并优化丢弃值、训练步数和批... 基于心电信号进行心律失常自动检测和分类识别研究,辅助临床医生进行心血管相关疾病诊断。采用MIT-BIH数据库作为数据源,对该数据库心电数据进行小波分解与重构去噪后,构建卷积神经网络模型,结合Adam优化器,并优化丢弃值、训练步数和批大小三个超参数来优化模型,使用准确率、灵敏性和正预测率三个指标评价模型性能。实验结果表明,模型实现心律失常五分类的整体准确率大于99%,与现有模型性能相比,准确率提升1.2%。 展开更多
关键词 卷积神经网络 心律失常 心电信号 小波变换
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以BP神经网络为工具的短时ECG信号情感分类
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作者 张善斌 《福建电脑》 2024年第2期11-16,共6页
针对目前生理信号情感识别领域采用的生理信号种类太多或使用的生信号长度较长的问题,本文使用BP神经网络对单一、短时ECG信号进行情感识别分类,并对识别时间进行了估计。通过诱发被试喜、怒、哀、惧和平静5种基本情感状态,采集到ECG生... 针对目前生理信号情感识别领域采用的生理信号种类太多或使用的生信号长度较长的问题,本文使用BP神经网络对单一、短时ECG信号进行情感识别分类,并对识别时间进行了估计。通过诱发被试喜、怒、哀、惧和平静5种基本情感状态,采集到ECG生理信号,处理后利用神经网络建立模型。实验结果表明,本文方法得到的情感分类的平均识别率为89.14%,且生理信号进行特征提取和识别分类的时间总和小于0.15s,有效地降低了对生理信号种类和窗口长度的依赖。 展开更多
关键词 情感分类 BP神经网络 ecg信号 机器识别
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Progress on Fabric Electrodes Used in ECG Signals Monitoring 被引量:1
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作者 Zhen Liu Xiaoxia Liu 《Journal of Textile Science and Technology》 2015年第3期110-117,共8页
Wearable monitoring system is designed for skin stimulation of conductive adhesive, prolonged physiological monitoring and biocompatibility, whose core is fabric electrodes and it can feedback physiological status by ... Wearable monitoring system is designed for skin stimulation of conductive adhesive, prolonged physiological monitoring and biocompatibility, whose core is fabric electrodes and it can feedback physiological status by analysis of abnormal electrocardiogram (ECG). Fabric electrode is a sensor to collect biological signals based on textile materials including signals acquisition, processing systems and information feedback platform and so on. In this paper, the design methods and classification of medical electrodes would be introduced. It also sorted out the principle of biological electrical signals, the design methods and characteristics of different material and different structure electrodes from the point of dry electrodes and wet electrodes. There are many methods that can be used to prepare fabric electrodes. They are mainly metal plating, conductive polymer coating, magnetron sputtering, gas phase deposition and impregnation. Besides, they select the appropriate substrate, conductive medium and composite way to get light fabric electrodes which have high conductivity, good conformability. From the perspective of biological signal acquisition by fabric electrodes, this paper also sorted out the influence and approaches of biological signals and the way to feedback the physiological condition of human. As a new generation of bio-signal acquisition material, fabric electrode has met the requirements of the development of modern medicine. Fabric electrode is different from traditional conductive materials in the characteristics of comfort, intelligence, convenience, accuracy and so on. 展开更多
关键词 FABRIC ELECTRODE Biological signals SLIDE ARTIFACTS ecg
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Individual Identification Using ECG SignalsW 被引量:1
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作者 Mohamad O. Diab Alaa Seif +1 位作者 Mohamad El-Abed Maher Sabbah 《Journal of Computer and Communications》 2018年第1期74-80,共7页
The electrocardiogram (ECG) signal used for diagnosis and patient monitoring, has recently emerged as a biometric recognition tool. Indeed, ECG signal changes from one person to another according to health status, hea... The electrocardiogram (ECG) signal used for diagnosis and patient monitoring, has recently emerged as a biometric recognition tool. Indeed, ECG signal changes from one person to another according to health status, heart geometry and anatomy among other factors. This paper forms a comparative study between different identification techniques and their performances. Previous works in this field referred to methodologies implementing either set of fiducial or set non-fiducial features. In this study we show a comparison of the same data using a fiducial feature set and a non-fiducial feature set based on statistical calculation of wavelet coefficient. High identification rates were measured in both cases, non-fiducial using Discrete Meyer (dmey) wavelet outperformed the rest at 98.65. 展开更多
关键词 BIOMETRICS ecg signals Fiducial Features Discrete WAVELET Transform (DWT) Multilayer PERCEPTRON (MLP)
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ANALYSIS OF AFFECTIVE ECG SIGNALS TOWARD EMOTION RECOGNITION 被引量:2
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作者 Xu Ya Liu Guangyuan +2 位作者 Hao Min Wen Wanhui Huang Xiting 《Journal of Electronics(China)》 2010年第1期8-14,共7页
Recently,as recognizing emotion has been one of the hallmarks of affective computing,more attention has been paid to physiological signals for emotion recognition.This paper presented an approach to emotion recognitio... Recently,as recognizing emotion has been one of the hallmarks of affective computing,more attention has been paid to physiological signals for emotion recognition.This paper presented an approach to emotion recognition using ElectroCardioGraphy(ECG) signals from multiple subjects.To collect reliable affective ECG data,we applied an arousal method by movie clips to make subjects experience specific emotions without external interference.Through precise location of P-QRS-T wave by continuous wavelet transform,an amount of ECG features was extracted sufficiently.Since feature selection is a combination optimization problem,Improved Binary Particle Swarm Optimization(IBPSO) based on neighborhood search was applied to search out effective features to improve classification results of emotion states with the help of fisher or K-Nearest Neighbor(KNN) classifier.In the experiment,it is shown that the approach is successful and the effective features got from ECG signals can express emotion states excellently. 展开更多
关键词 Emotion recognition ElectroCardioCraphy ecg signal Continuous wavelet transform Improved Binary Particle Swarm Optimization (IBPSO) Neighborhood search
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Compression of ECG Signals Based on DWT and Exploiting the Correlation between ECG Signal Samples
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作者 Mohammed M. Abo-Zahhad Tarik K. Abdel-Hamid Abdelfatah M. Mohamed 《International Journal of Communications, Network and System Sciences》 2014年第1期53-70,共18页
This paper presents a hybrid technique for the compression of ECG signals based on DWT and exploiting the correlation between signal samples. It incorporates Discrete Wavelet Transform (DWT), Differential Pulse Code M... This paper presents a hybrid technique for the compression of ECG signals based on DWT and exploiting the correlation between signal samples. It incorporates Discrete Wavelet Transform (DWT), Differential Pulse Code Modulation (DPCM), and run-length coding techniques for the compression of different parts of the signal;where lossless compression is adopted in clinically relevant parts and lossy compression is used in those parts that are not clinically relevant. The proposed compression algorithm begins by segmenting the ECG signal into its main components (P-waves, QRS-complexes, T-waves, U-waves and the isoelectric waves). The resulting waves are grouped into Region of Interest (RoI) and Non Region of Interest (NonRoI) parts. Consequently, lossless and lossy compression schemes are applied to the RoI and NonRoI parts respectively. Ideally we would like to compress the signal losslessly, but in many applications this is not an option. Thus, given a fixed bit budget, it makes sense to spend more bits to represent those parts of the signal that belong to a specific RoI and, thus, reconstruct them with higher fidelity, while allowing other parts to suffer larger distortion. For this purpose, the correlation between the successive samples of the RoI part is utilized by adopting DPCM approach. However the NonRoI part is compressed using DWT, thresholding and coding techniques. The wavelet transformation is used for concentrating the signal energy into a small number of transform coefficients. Compression is then achieved by selecting a subset of the most relevant coefficients which afterwards are efficiently coded. Illustrative examples are given to demonstrate thresholding based on energy packing efficiency strategy, coding of DWT coefficients and data packetizing. The performance of the proposed algorithm is tested in terms of the compression ratio and the PRD distortion metrics for the compression of 10 seconds of data extracted from records 100 and 117 of MIT-BIH database. The obtained results revealed that the proposed technique possesses higher compression ratios and lower PRD compared to the other wavelet transformation techniques. The principal advantages of the proposed approach are: 1) the deployment of different compression schemes to compress different ECG parts to reduce the correlation between consecutive signal samples;and 2) getting high compression ratios with acceptable reconstruction signal quality compared to the recently published results. 展开更多
关键词 ecg Signal Segmentation LOSSLESS and LOSSY Compression Techniques Discrete Wavelet Transform Energy PACKING Efficiency RUN-LENGTH Coding
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模拟ECG信号在320排CT冠脉成像中的应用价值 被引量:1
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作者 成满平 蔡晓庆 +4 位作者 牛娟琴 薛巍 陈纲 岳丽娜 杜林芝 《中国CT和MRI杂志》 2023年第11期77-79,共3页
目的探讨模拟ECG信号在320排CT冠脉成像中的应用价值。方法收集和分析我院2015-01-01至2021-10-01期间,使用模拟ECG信号成像的20例患者(A组),与同时期,随机抽取的,常规技术成像的20例患者(B组)的冠脉成像结果,实行对照研究。结果A、B两... 目的探讨模拟ECG信号在320排CT冠脉成像中的应用价值。方法收集和分析我院2015-01-01至2021-10-01期间,使用模拟ECG信号成像的20例患者(A组),与同时期,随机抽取的,常规技术成像的20例患者(B组)的冠脉成像结果,实行对照研究。结果A、B两组图像质量主观法评价,图像质量无显著差异(P=0.3758>0.05);A、B两组图像质量客观法评价,升主动脉根部,右冠状动脉近端,左前降支近端,左旋支近端CT值以及升主动脉根部层面噪声均无明显差异(P>0.05);A、B两组辐射剂量对比有显著差异(P<0.01)。结论模拟ECG信号在320排CT冠脉成像中的应用是可行的,值得推广。 展开更多
关键词 模拟 ecg信号 冠状动脉CT成像 320排CT
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多模型投票的深度学习ECG分类方法设计与研究 被引量:1
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作者 李伟康 邓星 邵海见 《计算机仿真》 北大核心 2023年第8期339-344,共6页
由于经典机器学习算法在心电信号(Recording of electrocardiograms,ECG)分析中存在特征表征能力不足等原因,基于深度学习投票机制,提出了一种基于多模型投票的深度学习ECG波形分类方法。利用多个具有不同网络参数的深度神经网络对ECG... 由于经典机器学习算法在心电信号(Recording of electrocardiograms,ECG)分析中存在特征表征能力不足等原因,基于深度学习投票机制,提出了一种基于多模型投票的深度学习ECG波形分类方法。利用多个具有不同网络参数的深度神经网络对ECG信号进行分类,并通过加权投票来提高ECG信号的分类准确率。实验的平均分类准确率为98%,与传统方法以及其它深度学习方法比如支持向量机,卷积神经网络,深度神经网络以及长短期记忆网络的结果比较,验证了上述方法在分类精度上有显著提高。 展开更多
关键词 多模型 深度学习 投票机制 心电信号
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De-Noising of ECG Signals by Design of an Optimized Wavelet
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作者 Vahid Makhdoomi Kaviri Masoud Sabaghi Saeid Marjani 《Circuits and Systems》 2016年第11期3746-3755,共10页
In this paper, a different method for de-noising of ECG signals using wavelets is presented. In this strategy, we will try to design the best wavelet for de-nosing. Genetic algorithm tests wide range of quadrature fil... In this paper, a different method for de-noising of ECG signals using wavelets is presented. In this strategy, we will try to design the best wavelet for de-nosing. Genetic algorithm tests wide range of quadrature filter banks and the best of them will be chosen that minimize the Signal-to-Noise Ratio (SNR). Furthermore, the wavelet function and scaling function related to these filters are reported as the best wavelet for de-noising. Simulation results for de-noising of a noisy ECG signal show that using obtained wavelet by proposed method improves the SNR of about 2.5 dB. 展开更多
关键词 WAVELETS DE-NOISING Genetic Algorithm ecg signals
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Classification of Electrocardiogram Signals for Arrhythmia Detection Using Convolutional Neural Network
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作者 Muhammad Aleem Raza Muhammad Anwar +4 位作者 Kashif Nisar Ag.Asri Ag.Ibrahim Usman Ahmed Raza Sadiq Ali Khan Fahad Ahmad 《Computers, Materials & Continua》 SCIE EI 2023年第12期3817-3834,共18页
With the help of computer-aided diagnostic systems,cardiovascular diseases can be identified timely manner to minimize the mortality rate of patients suffering from cardiac disease.However,the early diagnosis of cardi... With the help of computer-aided diagnostic systems,cardiovascular diseases can be identified timely manner to minimize the mortality rate of patients suffering from cardiac disease.However,the early diagnosis of cardiac arrhythmia is one of the most challenging tasks.The manual analysis of electrocardiogram(ECG)data with the help of the Holter monitor is challenging.Currently,the Convolutional Neural Network(CNN)is receiving considerable attention from researchers for automatically identifying ECG signals.This paper proposes a 9-layer-based CNN model to classify the ECG signals into five primary categories according to the American National Standards Institute(ANSI)standards and the Association for the Advancement of Medical Instruments(AAMI).The Massachusetts Institute of Technology-Beth Israel Hospital(MIT-BIH)arrhythmia dataset is used for the experiment.The proposed model outperformed the previous model in terms of accuracy and achieved a sensitivity of 99.0%and a positivity predictively 99.2%in the detection of a Ventricular Ectopic Beat(VEB).Moreover,it also gained a sensitivity of 99.0%and positivity predictively of 99.2%for the detection of a supraventricular ectopic beat(SVEB).The overall accuracy of the proposed model is 99.68%. 展开更多
关键词 ARRHYTHMIA ecg signal deep learning convolutional neural network physioNet MIT-BIH arrhythmia database
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Improved Bat Algorithm with Deep Learning-Based Biomedical ECG Signal Classification Model
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作者 Marwa Obayya Nadhem NEMRI +5 位作者 Lubna A.Alharbi Mohamed K.Nour Mrim M.Alnfiai Mohammed Abdullah Al-Hagery Nermin M.Salem Mesfer Al Duhayyim 《Computers, Materials & Continua》 SCIE EI 2023年第2期3151-3166,共16页
With new developments experienced in Internet of Things(IoT),wearable,and sensing technology,the value of healthcare services has enhanced.This evolution has brought significant changes from conventional medicine-base... With new developments experienced in Internet of Things(IoT),wearable,and sensing technology,the value of healthcare services has enhanced.This evolution has brought significant changes from conventional medicine-based healthcare to real-time observation-based healthcare.Biomedical Electrocardiogram(ECG)signals are generally utilized in examination and diagnosis of Cardiovascular Diseases(CVDs)since it is quick and non-invasive in nature.Due to increasing number of patients in recent years,the classifier efficiency gets reduced due to high variances observed in ECG signal patterns obtained from patients.In such scenario computer-assisted automated diagnostic tools are important for classification of ECG signals.The current study devises an Improved Bat Algorithm with Deep Learning Based Biomedical ECGSignal Classification(IBADL-BECGC)approach.To accomplish this,the proposed IBADL-BECGC model initially pre-processes the input signals.Besides,IBADL-BECGC model applies NasNet model to derive the features from test ECG signals.In addition,Improved Bat Algorithm(IBA)is employed to optimally fine-tune the hyperparameters related to NasNet approach.Finally,Extreme Learning Machine(ELM)classification algorithm is executed to perform ECG classification method.The presented IBADL-BECGC model was experimentally validated utilizing benchmark dataset.The comparison study outcomes established the improved performance of IBADL-BECGC model over other existing methodologies since the former achieved a maximum accuracy of 97.49%. 展开更多
关键词 Data science ecg signals improved bat algorithm deep learning biomedical data data classification machine learning
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基于VMD算法的ECG信号基线漂移校正研究
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作者 顾旋 张伟 《现代计算机》 2023年第4期54-59,共6页
针对现有方法校正ECG信号基线漂移的缺陷,提出基于VMD算法校正ECG信号的基线漂移。首先获取含真实基线漂移的ECG信号;然后基于最佳参数的VMD将含噪ECG信号分解为多个IMF分量,利用各IMF分量频谱图的峰值频率判断基线漂移;最后将含基线漂... 针对现有方法校正ECG信号基线漂移的缺陷,提出基于VMD算法校正ECG信号的基线漂移。首先获取含真实基线漂移的ECG信号;然后基于最佳参数的VMD将含噪ECG信号分解为多个IMF分量,利用各IMF分量频谱图的峰值频率判断基线漂移;最后将含基线漂移的IMF分量舍弃,将其他IMF分量叠加得到去除基线漂移的ECG信号。同时将EMD算法和该方法对相同含基线漂移的ECG信号进行去噪,结果表明,该方法能更好地校正ECG信号基线漂移,且去噪后与原信号的相关系数更大。 展开更多
关键词 ecg信号 基线漂移 VMD算法 EMD算法 校正
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基于人体ECG信号监测的汽车座椅研究 被引量:1
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作者 胡瑄 《时代汽车》 2023年第1期166-168,共3页
设计了一种基于驾驶员生命信息个性化监测的汽车座椅,通过安装在汽车座椅上的集成医疗传感器系统,对驾驶员生命信号进行监测。使用容性耦合传感器,对驾驶员运用电容式心电检测技术,获取人体ECG(心率)信号。设计了系统硬件结构,设计了包... 设计了一种基于驾驶员生命信息个性化监测的汽车座椅,通过安装在汽车座椅上的集成医疗传感器系统,对驾驶员生命信号进行监测。使用容性耦合传感器,对驾驶员运用电容式心电检测技术,获取人体ECG(心率)信号。设计了系统硬件结构,设计了包括数据采集和处理在内的电路,比较了传感器组处于不同位置的下QRS(心电图波群)值,解决了传感器最佳安装位置问题。实验结果证明:该系统能够较好的记录驾驶员人体ECG信号,测试结果能够在医学上判断出驾驶员身体状况是否处于良好状态。 展开更多
关键词 汽车座椅 智能化 生命信号 ecg信号 监测技术
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应用平均幅度差函数之和分析心电信号对除颤最佳时机的预测价值
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作者 刘远山 林帆荣 +3 位作者 陈煜嘉 黄子通 蒋龙元 杨正飞 《广东医学》 CAS 2024年第9期1106-1112,共7页
目的应用平均幅度差函数之和(the sum of average magnitude difference function,SAMDF)处理室颤的心电信号,通过与常用预测除颤时间方法振幅谱面积(amplitude spectrum area,AMSA)进行对比找到预测除颤时间更优的方法。方法应用56头重... 目的应用平均幅度差函数之和(the sum of average magnitude difference function,SAMDF)处理室颤的心电信号,通过与常用预测除颤时间方法振幅谱面积(amplitude spectrum area,AMSA)进行对比找到预测除颤时间更优的方法。方法应用56头重(40±5)kg雄性家猪,诱导室颤后进行10 min未处理的室颤、6 min的心肺复苏和除颤。在室颤和心肺复苏过程当中会记录每1 min SAMDF和AMSA的数据并记录下来。进而计算受试者工作特征(receiver operating characteristic,ROC)曲线,应用单向方差分析(one-way analyses of variance,one-way ANOVA)以及正负样本散点图的比较,以此说明两者均能优化最佳除颤时间。比较除颤成功组(Group R)和除颤失败组(Group N)的SAMDF和AMSA的数值以说明两者预测除颤成功的能力。结果散点图显示SAMDF和AMSA均能够区分阳性和负样本(P<0.001)。ROC曲线显示SAMDF(AUC=0.801,P<0.001)和AMSA(AUC=0.777,P<0.001)一样有着相同的能力预测最佳除颤时间。两组SAMDF和AMSA数值比较,Group R的SAMDF和AMSA数值明显高于Group N(P<0.001)。结论SAMDF在优化预测除颤时机方面具有很高的潜力,并且可以作为AMSA等现有有效预测除颤时机特征的补充。 展开更多
关键词 平均幅度差函数之和 振幅谱面积 心电信号 预测除颤时机
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手持式心电采集仪的使用技巧
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作者 景永明 荆凡釿 +1 位作者 黄训华 樊好义 《实用心电学杂志》 2024年第2期154-157,共4页
手持式心电采集仪是一款简易的双极单导联心电图记录设备,只需双手拇指紧捏正、负两极,就能方便地记录出标准Ⅰ导联心电图,多用于监测心律失常。它属于家用医疗器械,颇受广大中老年朋友的欢迎。基于单极导联与双极导联的本质及其内在联... 手持式心电采集仪是一款简易的双极单导联心电图记录设备,只需双手拇指紧捏正、负两极,就能方便地记录出标准Ⅰ导联心电图,多用于监测心律失常。它属于家用医疗器械,颇受广大中老年朋友的欢迎。基于单极导联与双极导联的本质及其内在联系,本文衍生出标准导联与加压单极导联的记录方法;同时,在深入探究CR导联与Wilson导联内在联系的基础上,创造性地提出了手持式心电采集仪直采CR胸导联心电图的方法。理论和实践均表明,加压单极肢体导联的等效记录法与CR胸导联的双极记录法不仅能满足临床需要,而且还有其独到之处。该方法能充分发挥家用医疗器械的医用价值,值得推广普及。 展开更多
关键词 心电图机 手持式心电采集仪 双极导联 单极导联 CR胸导联 Wilson胸导联 额面六轴系统 横面六轴系统
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强运动环境下抗基线漂移可穿戴运动体征监测系统设计
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作者 姚铃丽 仲倩 +2 位作者 徐新宇 陈光良 黄睿杰 《信息化研究》 2024年第5期51-56,共6页
本文针对强运动环境设计了一种可穿戴运动体征监测系统。选用ADS1292型模数转换器芯片作为心电(ECG)信号采集模块,LMT70型温度传感器芯片作为体温信号采集模块,ICM20602型6轴陀螺仪作为加速度测量模块,可穿戴于人体实现动态心电监测、... 本文针对强运动环境设计了一种可穿戴运动体征监测系统。选用ADS1292型模数转换器芯片作为心电(ECG)信号采集模块,LMT70型温度传感器芯片作为体温信号采集模块,ICM20602型6轴陀螺仪作为加速度测量模块,可穿戴于人体实现动态心电监测、体表温度采集以及运动步数和运动距离测量。针对强运动导致的基线漂移对心电信号检测的影响,设计了归一化最小均方(NLMS)自适应滤波器进行滤除。测试结果表明,本设备测试精度较高,抗基线漂移能力较强。 展开更多
关键词 运动体征监测 心电信号 基线漂移 自适应滤波器
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ECG信号的小波变换检测方法 被引量:52
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作者 李翠微 郑崇勋 袁超伟 《中国生物医学工程学报》 EI CAS CSCD 北大核心 1995年第1期59-66,共8页
本文把小波变换应用于ECG信号的QRS波检测。利用二进样条小波对信号按Mallat算法进行变换:从二进小波变换的等效滤波器的角度,分析了信号奇异点(R峰点)与其小波变换模极大值对的零交叉点的关系。在检测中运用了一系列... 本文把小波变换应用于ECG信号的QRS波检测。利用二进样条小波对信号按Mallat算法进行变换:从二进小波变换的等效滤波器的角度,分析了信号奇异点(R峰点)与其小波变换模极大值对的零交叉点的关系。在检测中运用了一系列策略以增强算法的抗干扰能力、提高QRS波的正确检测率。经MIT/BIH标准心电数据库检测验证,QRS波正确检测率高达99.8%。 展开更多
关键词 信号检测 小波变换 心电图
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基于小波变换的自适应滤波器消除ECG中基线漂移 被引量:20
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作者 李小燕 王涛 +1 位作者 冯焕清 詹长安 《中国科学技术大学学报》 CAS CSCD 北大核心 2000年第4期450-454,共5页
设计了一种新的基于小波变换的自适应滤波器 ,其参考信号选择原始信号经小波分解后的高频部分 .该滤波器可有效消除心电信号中基线漂移 .经MIT/BIH心电数据库检验 ,取得满意的结果 .
关键词 小波变换 自适应滤波器 ecg信号 基线漂移
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基于自适应参数的心电压缩方法研究
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作者 林钰洁 王星尧 +2 位作者 陈超 李建清 刘澄玉 《生物医学工程研究》 2024年第3期214-222,共9页
为探究一种适应于临床诊断的高压缩比的心电(electrocardiograph, ECG)压缩方法,本研究提出了一个自适应压缩参数寻优器,基于压缩算法定位出压缩性能最佳的ECG信号参数组。针对算法的普适性,本研究推荐了一组适用于所有ECG信号的参数组... 为探究一种适应于临床诊断的高压缩比的心电(electrocardiograph, ECG)压缩方法,本研究提出了一个自适应压缩参数寻优器,基于压缩算法定位出压缩性能最佳的ECG信号参数组。针对算法的普适性,本研究推荐了一组适用于所有ECG信号的参数组,并利用4个指标在MIT-BIH数据库上对压缩性能进行估计。实验结果表明,平均压缩比(compression ratio, CR)达到了26.67,平均百分比均方根误差(percentage root-mean-square difference, PRD)达到了14.64%,压缩一条30 min ECG信号的平均时长为0.125 8 s。本研究改进后的压缩算法在压缩比上表现突出,对临床诊断有应用意义。 展开更多
关键词 心电信号 心电压缩 参数自适应 穿戴式心电
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