Detection and Localization of Myocardial Infarction using K-nearest Neighbor Classifier

This paper presents automatic detection and localization of myocardial infarction (MI) using K-nearest neighbor (KNN) classifier. Time domain features of each beat in the ECG signal such as T wave amplitude, Q wave and ST level deviation, which are indicative of MI, are extracted from 12 leads ECG. Detection of MI aims to classify normal subjects without myocardial infarction and subjects suffering from Myocardial Infarction. For further investigation, Localization of MI is done to specify the region of infarction of the heart. Total 20,160 ECG beats from PTB database available on Physio-bank is used to investigate the performance of extracted features with KNN classifier. In the case of MI detection, sensitivity and specificity of KNN is found to be 99.9% using half of the randomly selected beats as training set and rest of the beats for testing. Moreover, Arif-Fayyaz pruning algorithm is used to prune the data which will reduce the storage requirement and computational cost of search. After pruning, sensitivity and specificity are dropped to 97% and 99.6% respectively but training is reduced by 93%. Myocardial Infarction beats are divided into ten classes based on the location of the infarction along with one class of normal subjects. Sensitivity and Specificity of above 90% is achieved for all eleven classes with overall classification accuracy of 98.8%. Some of the ECG beats are misclassified but interestingly these are misclassified to those classes whose location of infarction is near to the true classes of the ECG beats. Pruning is done on the training set for eleven classes and training set is reduced by 70% and overall classification accuracy of 98.3% is achieved. The proposed method due to its simplicity and high accuracy over the PTB database can be very helpful in correct diagnosis of MI in a practical scenario.     

基于自适应滤波的心电图中呼吸信号的提取方法

目的: 利用基于递归最小二乘(recursive least-square, RLS)算法的自适应滤波器对心电图信号进行滤波,提取心电信号中的呼吸信号. 方法: 通过对常规的肢体导联心电检测设备获取的心电信号进行处理,提取心电特征信号R波幅度序列和R波间期序列作为自适应滤波器的原始输入信号和参考信号,通过RLS算法的自适应滤波,提取心电图中调制的呼吸分量. 结果: 经过时、频域分析,自适应滤波后估算出的呼吸信号与真实的呼吸信号在时域和频域都显示出了良好的相关性. 结论: 本方法设计合理,结果准确可靠,是一种简便高效、无创、低成本的呼吸监护手段.     

A New QRS Detection Method Using Wavelets and Artificial Neural Networks

We present a new method for detection and classification of QRS complexes in ECG signals using continuous wavelets and neural networks. Our wavelet method consists of four wavelet basis functions that are suitable in detection of QRS complexes within different QRS morphologies in the signal and thresholding technique for denoising and feature extraction. The results demonstrate that the proposed method is not only efficient for normal ECG signal analysis but also for various types of arrhythmic cardiac signals embedded in noise. For the classification stage, a feedforward neural network was trained with standard backpropagation algorithm. The classifier input features consisted of compact wavelet coefficients of QRS complexes that resulted in higher classification rates. We demonstrate the efficiency of our method with the average accuracy 97.2% in classification of normal and abnormal QRS complexes.     

GPRS移动式心电监护系统的QRS波实时检测算法

目的探讨GPRS移动式心电监护系统ECG信号QRS波的实时检测算法.方法采用四点平均对CM5导联的ECG信号进行滤波,再对ECG信号的一、二阶差分值进行平滑处理,利用ECG信号的二阶差分值极小值和一阶差分过零点在较短的时间窗内实现QRS波精确定位,并设计了具有自学习和自适应功能的信号检测门限.结果算法能抑止多种噪声对ECG信号的影响,将搜索的时间窗宽度缩小到0.02 s以下,且算法只具有多项式的复杂度;经过临床测试,动态ECG信号进QRS波检测准确率可达99.8%以上.结论该算法适合于移动心电监护系统QRS波的实时检测和分析.     

Fetal Electrocardiogram Extraction and Analysis Using Adaptive Noise Cancellation and Wavelet Transformation Techniques

Birth defect-related demise is mainly due to congenital heart defects. In the earlier stage of pregnancy, fetus problem can be identified by finding information about the fetus to avoid stillbirths. The gold standard used to monitor the health status of the fetus is by Cardiotachography(CTG), cannot be used for long durations and continuous monitoring. There is a need for continuous and long duration monitoring of fetal ECG signals to study the progressive health status of the fetus using portable devices. The non-invasive method of electrocardiogram recording is one of the best method used to diagnose fetal cardiac problem rather than the invasive methods.The monitoring of the fECG requires development of a miniaturized hardware and a efficient signal processing algorithms to extract the fECG embedded in the mother ECG. The paper discusses a prototype hardware developed to monitor and record the raw mother ECG signal containing the fECG and a signal processing algorithm to extract the fetal Electro Cardiogram signal. We have proposed two methods of signal processing, first is based on the Least Mean Square (LMS) Adaptive Noise Cancellation technique and the other method is based on the Wavelet Transformation technique. A prototype hardware was designed and developed to acquire the raw ECG signal containing the mother and fetal ECG and the signal processing techniques were used to eliminate the noises and extract the fetal ECG and the fetal Heart Rate Variability was studied. Both the methods were evaluated with the signal acquired from a fetal ECG simulator, from the Physionet database and that acquired from the subject. Both the methods are evaluated by finding heart rate and its variability, amplitude spectrum and mean value of extracted fetal ECG. Also the accuracy, sensitivity and positive predictive value are also determined for fetal QRS detection technique. In this paper adaptive filtering technique uses Sign-sign LMS algorithm and wavelet techniques with Daubechies wavelet, employed along with de noising techniques for the extraction of fetal Electrocardiogram.Both the methods are having good sensitivity and accuracy. In adaptive method the sensitivity is 96.83, accuracy 89.87, wavelet sensitivity is 95.97 and accuracy is 88.5. Additionally, time domain parameters from the plot of heart rate variability of mother and fetus are analyzed.     

心室晚电位微机无损伤探测分析系统与临床应用的研究

心室晚电位(LP)是在心电图QRS波末端出现的微弱信号,常规心电图不能描记出来。LP的检测对预防恶性心律失常或猝死有重要意义。本文介绍自行设计的微机无损伤LP探测分析系统及其临床应用。该系统将心电信号转换成数字信号,然后通过正常QRS波与早搏波辨识,信号平均处理以及数字滤波,用绘图仪或打印机记录LP并印出12个分析参数,应用该系统对24名正常人与105名病人进行了临床检查,并对结果进行了分析。     

胎儿心电信号的无创提取：基于时间卷积编解码网络

目的 实现从孕妇腹壁混合心电信号中提取微弱的胎儿心电信号,为准确估计胎儿心率、分析胎儿心电波形等提供基础。方法 利用深度卷积网络（deep CNN）优越的非线性映射能力,本文提出了一种基于时间卷积编解码网络的非线性自适应噪声消除（nonlinear ANC）提取框架,以实现胎儿心电信号的有效提取。首先构建适用于处理胎儿心电信号的深度时间卷积网络（TCED-Net）模型作为非线性映射工具;然后以孕妇胸部心电信号为参考,利用该模型估计孕妇腹壁混合心电信号中的母体心电成分;最后从腹壁混合信号中减去所估计的母体心电成分,以得到完整的胎儿心电信号。实验利用合成心电数据（FECGSYNDB）和临床心电数据（NIFECGDB、PCDB）对方法性能进行测试与对比。结果 本文方法在FECGSYNDB上的胎儿R峰检测精度（[F1]值）、均方误差（MSE）和质量信噪比（qSNR）分别达到98.89 %,0.20和7.84;在NIFECGDB上的[F1]值达到99.1%;在 PCDB 上的[F1]值达到 98.61%。在不同数据集中较之 EKF（[F1=]93.84%）、ES-RNN([F1] =97.20% )和 AECG-DecompNet（[F1]=95.43%）等现有性能最佳的算法,本文方法的R峰检测精度指标分别高出5.05%、1.9%和3.18%,均优于现有最佳方法。结论 与现有算法相比,本文方法可以提取出更为清晰的胎儿心电信号,对孕期进行有效的胎儿健康监护具有一定的应用价值。     

Classification of Arrhythmia Using Hybrid Networks

Reliable detection of arrhythmias based on digital processing of Electrocardiogram (ECG) signals is vital in providing suitable and timely treatment to a cardiac patient. Due to corruption of ECG signals with multiple frequency noise and presence of multiple arrhythmic events in a cardiac rhythm, computerized interpretation of abnormal ECG rhythms is a challenging task. This paper focuses a Fuzzy C- Mean (FCM) clustered Probabilistic Neural Network (PNN) and Multi Layered Feed Forward Network (MLFFN) for the discrimination of eight types of ECG beats. Parameters such as fourth order Auto Regressive (AR) coefficients along with Spectral Entropy (SE) are extracted from each ECG beat and feature reduction has been carried out using FCM clustering. The cluster centers form the input of neural network classifiers. The extensive analysis of Massachusetts Institute of Technology- Beth Israel Hospital (MIT-BIH) arrhythmia database shows that FCM clustered PNNs is superior in cardiac arrhythmia classification than FCM clustered MLFFN with an overall accuracy of 99.05%, 97.14%, respectively.     

213例急性脑血管病心电图改变分析

目的 探讨急性脑血管病心电图变化特点.方法 对213例急性脑血管病患者的心电图检查结果进行回顾性分析.结果 213例患者的心电图异常率为67.6％ (144/213).出血组异常率为85.9％ (79/92),明显高于缺血组[53.7％ (65/121)],差异有统计学意义(P＜0.05).心电图异常主要表现为ST-T改变、Q-T间期延长、窦性心动过速、明显U波、心房颤动、房性早搏、室性早搏、传导阻滞等.治疗后复查心电图有61例恢复正常,76例改善,7例无改善甚至恶化.结论 急性脑血管病患者的心电图异常发生率高,其心电图表现可随病情的好转而改善或恢复正常.因此,临床上加强心电图的监测对指导治疗和改善预后均具有重要意义.     

高频心电图和心室晚电位与心电图异常的临床研究

目的 :探讨高频心电图 (HFECG)和心室晚电位 (VLP)对恶性室性心律失常、心脏性猝死的预测价值。方法 :分析 781例各类心血管病人的ECG ,HFECG和VLP ,并将异常信号区分为高风险和非高风险两大类。结果 :高风险异常信号的检出率ECG为 78% ,VLP为 1 3 % ,HFECG为 52 %。结论 :由梗塞 /缺血心肌病灶产生的ECG高频信号的高检出率并不能说明其具有高预测价格。计算机辅助的VLP和HFECG反映了这种碎裂延迟电位高频成份有临床应用的潜在价值 ,但标准化尚有待进一步改进     

Variable Threshold Method for ECG R-peak Detection

In this paper, a wearable belt-type ECG electrode worn around the chest by measuring the real-time ECG is produced in order to minimize the inconvenient in wearing. ECG signal is detected using a potential instrument system. The measured ECG signal is transmits via an ultra low power consumption wireless data communications unit to personal computer using Zigbee-compatible wireless sensor node. ECG signals carry a lot of clinical information for a cardiologist especially the R-peak detection in ECG. R-peak detection generally uses the threshold value which is fixed. There will be errors in peak detection when the baseline changes due to motion artifacts and signal size changes. Preprocessing process which includes differentiation process and Hilbert transform is used as signal preprocessing algorithm. Thereafter, variable threshold method is used to detect the R-peak which is more accurate and efficient than fixed threshold value method. R-peak detection using MIT-BIH databases and Long Term Real-Time ECG is performed in this research in order to evaluate the performance analysis.     

Measuring Saliency of Features Using Signal-to-noise Ratios for Detection of Electrocardiographic Changes in Partial Epileptic Patients

Medical diagnostic accuracies can be improved when the pattern is simplified through representation by important features. The feature vector, which is comprised of the set of all features used to describe a pattern, is a reduced-dimensional representation of that pattern. By identifying a set of salient features, the noise in a classification model can be reduced, resulting in more accurate classification. In this study, a signal-to-noise ratio (SNR) saliency measure was employed to determine saliency of input features of probabilistic neural networks (PNNs) used in classification of two types of electrocardiogram (ECG) beats (normal and partial epilepsy). In order to extract features representing the ECG signals, discrete wavelet transform was used. The PNNs used in the ECG signals classification were trained for the SNR screening method. The application results of the SNR screening method to the ECG signals demonstrated that classification accuracies of the PNNs with salient input features are higher than that of the PNNs with salient and non-salient input features.     

Analysis of Myocardial Infarction Using Discrete Wavelet Transform

Myocardial infarction (MI), is commonly known as a heart attack, occurs when the blood supply to the portion of the heart is blocked causing some heart cells to die. This information is depicted in the elevated ST wave, increased Q wave amplitude and inverted T wave of the electrocardiogram (ECG) signal. ECG signals are prone to noise during acquisition due to electrode movement, muscle tremor, power line interference and baseline wander. Hence, it becomes difficult to decipher the information about the cardiac state from the morphological changes in the ECG signal. These signals can be analyzed using different signal processing techniques. In this work, we have used multiresolution properties of wavelet transformation because it is suitable tool for interpretation of subtle changes in the ECG signal. We have analyzed the normal and MI ECG signals. ECG signal is decomposed into various resolution levels using the discrete wavelet transform (DWT) method. The entropy in the wavelet domain is computed and the energy–entropy characteristics are compared for 2282 normal and 718 MI beats. Our proposed method is able to detect the normal and MI ECG beat with more than 95% accuracy.     

动态心电图在心律失常及心肌缺血中的诊断价值应用

目的 分析心肌缺血和心律失常采用动态心电图诊断效果。方法 选择2018年7月至2020年6月在本院实施冠心病治疗的200例患者为研究样本。全部患者入院后均实施常规心电图及动态心电图检查,比较两种检查方式在心肌缺血和心律失常方面检出率及对不同心律失常类型的检出率。结果 动态心电图与常规心电图心肌缺血检出率为81.50%、47.50%(P0.05);动态心电图与常规心电图心律失常检出率为87.50%、56.50%(P0.05)。在房室阻滞、室性早搏二三联律、室性早搏单发、室性早搏成对、房性早搏二三联律、房性早搏单发及房性早搏成对方面动态心电图检出率均高于常规心电图(P0.05)。结论 心肌缺血和心律失常采用动态心电图实施检测可提升检测准确性,且动态心电图还可用于不同心律失常类型诊断,为临床合理制定治疗方案提供依据,有利于疾病的早期治疗,发挥一定临床应用效果。     

可穿戴式心电信号R峰检测的心拍感知卷积网络

目的 实现可穿戴式心电信号的R峰检测,为准确估计心率、心率变异性等生理参数提供基础。方法 采用全卷积网络预测R峰热图,对热图进行峰值定位获得R峰位置。引入心拍感知模块,联合心拍数量预测任务和R峰热图预测任务进行学习,提高卷积网络对全局上下文信息的提取能力。心拍感知模块预测的心拍数量还可估计R-R间期,用作峰值定位的峰间最小水平距离。为满足移动端的实时应用,采用深度可分离卷积减小模型的参数量和计算量。结果 实验仅使用可穿戴式心电数据训练模型。测试中定位误差容忍度设置为150 ms时,本文方法在可穿戴式心电数据集和公开数据集LUDB上的R峰检测灵敏度均高达100%,真阳率均超过99.9%;对于时长10 s的ECG信号,R峰检测CPU耗时约为23.2 ms。结论 本文方法对可穿戴式和常规心电信号的R峰检测均可达到良好效果,且满足R峰检测的实时性需求。     

130例慢型克山病死亡病例心电图分析

分析130例慢型克山病死亡病例心电图改变,以ST—T改变的检出率最高为73.07%,其次是室性早搏为42.3%,心室肥大41.54%。传导阻滞中以完全性右束支传导阻滞检出率最高为11.53%。多项异常心电图改变中,以ST—T改变和室性早搏合并其它异常较多。心电图异常项目越多,病人存活时间越短。     

剖胸手术围术期心律失常原因的探讨

目的:探讨剖胸手术患者围术期发生心率失常的原因。方法:回顾分析623例剖胸手术患者围手术期心电监护纪录结果,分析心率失常发生的时间及心率失常的性质等。结果:①心率失常的发生率为27％,其中窦速64.3％,房早10.1％,室早13.1％。②心率失常发生时间:全麻插管时2.9％,开胸探查时10.1％,分离切除病灶时4.1％,术后24h43.4％,术后48h19.7％,术后72h7.8％。结论:剖胸手术围术期心率失常的发生与以下因素有关:①手术创伤,手术操作刺激及麻醉深度不足;②术中缺氧;③患者高龄;④低钾血症;⑤术后疼痛刺激;⑥术前并发有心血管疾病及心电图异常,或合并有其它慢性疾病。     

心房纤颤合并二度房室传导阻滞(附27例报道)

本文报道27例心房纤颤合并二度房室传导阻滞。其诊断依据是:(1)出现三次以上的R—R间距长达1.5秒以上的心电图改变。(2)出现三次以上的结性(或室性)逸搏。21例正在应用洋地黄。停用洋地黄后,16例并发二度房室传导阻滞者得到恢复。     

医学生心电图各波时间、P─R间期、Q─T间期的研究

为了探讨青年人心电图各波时间与少儿（青春期少年，下同）及成人正常值的区别，我们通过对140名医学生（17～20岁）正常心电图各渡时间及P－R间期、Q－T间期的侧量和统计学处理，发现上述各量与少儿和成年人比较均有显著差别。医学生心电图P波时间、P－R问期的平均值及变化范围介于少儿和成人正常值之间；QRS波时间95％上下限比少儿正常值上下限高，下限比成人正常值下限低。结果证明，正常心电图各波时间、P－R间期随年龄增长而延长或有增加趋势。