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.     

QT间期测量的小波分析方法

目的　准确获取人体心电信号 (ECG)中的 QT间期值。方法　利用小波变换多尺度多分辨的特点 ,将心电信号进行多尺度分解 ,把不同频带的信号显现在小波分解各个尺度上。特征尺度上准确定位 QRS波及T波的起始点 ,从而获得 QT间期的精确值。结果　 1在 2 m V的心电信号上引入峰峰值为 1m V的误差信号 ,利用二进小波进行分解和重构得到相对精确的心电波形 ,测得 QRS波、P波、T波的宽度最大误差分别为 5 .75 % ,5 .2 % ,3.8%。 2用小波分析方法对 MIT/ BIH数据库中的 ECG信号进行处理后 ,测量得到各间期的平均值及标准偏差 ,可以反映出 QT间期及相应各间期的稳定性。结论　人体心电信号随着检测状态及时间的变化具有明显的非平稳性及包含许多干扰的特点。利用小波变换将心电信号进行处理能够获得 QT间期的精确值 ,为临床诊断提供了更加准确的依据     

QT间期测量的小波分析方法

目的：准确获取人体心电信号（ECG）中的QT间期值。方法：利用小波变换多尺度多分辨的特点，将心电信号进行多尺度分解，把不同频带的信号显现在小波分解各个尺度上。特征尺度上准确喧位QRS波及T波的起始点，从而获得QT间期的精确值。结果：（1）在2mV的心电信号上引入峰峰值为1mV的误差信号，利用二进小波进行分解和重构得到相对精确的忙电波形，测得QRS波、P波、T波的宽度最大误差分别为5．75％，5．2％，3．8％。（2）用小波分析方法对MIT／BIH数据库中的ECG信号进行处理后，测量得到各间期的平均值及标准偏差，可以反映出QT间期及相应各间期的稳定性。结论：人体心电信号随着检测状态及时间的变化具有明显的非平稳性及包含许多干扰的特点。利用小波变换将心电信号进行处理能够获得QT间期的精确值，为临床诊断提供了更加准确的依据。     

心房颤动合并预激综合征正道与旁道交替性顺向传导一例

目的探讨心房颤动合并预激综合征,分析预激综合征(WPW)合并心房颤动(Af)的心电图(ECG),WPW合并AF、Af的心电图有明显特征,表现为快速、极速的心室率,除AF波的等比例下传外,R-R间距不齐,QRS波增宽起始呈δ波,QRS波表现为完全WPW图形、完全典型WPW图形、完全WPW图形与典型WPW图形及正常图形的不同程度组合特征[1]。作出相关鉴别诊断,治疗与预后的估价。     

基于改进的快速LADT和神经网络结合的QRS波检测方法

目的 研究QRS波的具有较高正确率的识别方法。方法 本研究用一种改进的快速LADT新方法，并结合神经网络检测ECG信号的QRS波。结果 经MIT-BIH心电数据库检验表明：QRS波正确识别率高，实时性好。结论 本研究提出的方法达到了既减小运算量又能准确识别QRS波的目的。     

犬心肌缺血再灌注损伤心电图Ⅱ导联QRS波群的演变

目的观察杂种犬心肌缺血再灌注损伤过程心电图(ECG)的动态变化规律,为研究干细胞移植对犬心肌缺血再灌注损伤后的治疗作用提供基础。方法选用杂种犬24只,结扎冠状动脉左前降支中远1/3处,分3组分别于30min、60min、90min后松开。应用MPA-2000生物信号分析系统,在结扎前后及松开后连续动态记录ECGII导联QRS波群的变化。结果在开始结扎阻断冠状动脉血流时,QRS主波向上Rs型;重新开放血流血管再通之初,83.3%(20/24)的犬QRS波表现为主波向下rS型或QS型,伴QRS波增宽,然后r波波幅逐渐增大,逐渐演变成主波向上的Rs型,QRS时程恢复正常。15min、30min和大于30min的演变率分别为25.00%(6/24)、41.7%(10/24)和16.7%(4/24);且演变率与冠脉阻断时间相关。结论犬心肌缺血再灌注后ECG变化有一定规律,QRS波群变化趋势有可能作为心肌细胞功能恢复程度以及心肌保护效果较为直观的判断指标。     

冠状动脉搭桥术后心电图QRS低电压对预后影响

目的探讨冠状动脉搭桥术(CABG)后心电图(ECG)QRS低电压对预后影响.方法观察60例CABG患者术前及术后12导联ECG QRS电压变化及严重心律失常、EF值降低、心功能分级Ⅲ、合并心脏瓣膜病、病死率等.结果 CABG患者术后ECG QRS低电压与严重心律失常、EF值降低、心功能Killip≥Ⅲ、并发瓣膜病、住院病死率与Ⅱ组比较发生率高(P<0.01或P<0.05).结论 CABG后出现心电图QRS波群电压是临床近期预后险恶的独立指标.     

心电图检查QRS波群终末部分改变与急性ST段抬高型心肌梗死患者心肌梗死溶栓治疗临床试验风险评分的关联性及临床意义

目的 研究心电图(ECG)检查QRS波群终末部分改变与急性ST段抬高型心肌梗死(STEMI)患者心肌梗死溶栓治疗临床试验(TIMI)风险评分的关联性及临床意义.方法 选取2019年3月至2020年3月河南省襄城县人民医院STEMI患者114例,均行心电图检查,按照是否发生QRS波群终末部分改变分为改变组(43例)、未改...     

特殊类型的QRS波群正常化的分类及机制探讨

目的:探讨特殊类型QRS波群正常化的分类及形成机制.方法:分析26例特殊类型QRS波群正常化的常规12导联心电图(ECG)记录.结果:束支或房室旁道频率依赖性传导阻滞最常见,共18例次,占69.23%.束支内超常传导2例次,占7.69%;室性融合波4例次,占15.38%.同时或先后出现束支内超常传导及室性融合波的2例次,占7.69%,这些都是特殊类型的QRS波群正常化的原因.结论:形成QRS波群正常化具有多种机制,掌握它们有助于进一步分析和诊断心律失常的心电图改变,从而为临床制订合理的治疗方案提供正确的依据.     

27例高钾血症不典型心电图临床分析

高钾血症系指血清钾离子浓度高于5．5mmol／L^[1]，常引起心电图（ECG）出现高尖之帐蓬状T波，Q—T间期缩短及更为严重的QRS波时限增宽等典型变化^[2,3]。但临床上由于多种因素影响，血清钾浓度〉5．5mmol／L时ECG并非完全呈上述典型之变化，有时可呈不典型ECG表现即血清钾浓度增高（〉5．5mmol／L）但ECG不呈高血钾图形。本文对27例高钾血症不典型ECG进行分析。     

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.     

60岁以上老年人260例心电图分析

【目的】探讨60岁以上的老年人心电图特点,分析异常心电图发生的规律及病因。【方法】采用12导联心电图对260例60岁以上的老年人进行体检,分析异常心电图与其年龄、性别和基础疾病之间的关系。【结果】260例老年人中心电图异常者205人共227例次。随着年龄增加,异常心电图比例增加;各年龄阶段男性异常者比例均高于女性;异常心电图以ST段改变及心律失常多见。常见基础疾病为高血压、冠心病及肺心病。【结论】随着年龄增长,老年人心电图异常及心血管疾病比例明显增加;心电图检查应该列为老年人体检的常规项目。     

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.     

Performance Evaluation of Time-Frequency Distributions for ECG Signal Analysis

The non-stationary and multi-frequency nature of biomedical signal activities makes the use of time-frequency distributions (TFDs) for analysis inevitable. Time-frequency analysis provides simultaneous interpretations in both time and frequency domain enabling comprehensive explanation, presentation and interpretation of electrocardiogram (ECG) signals. The diversity of TFDs and specific properties for each type show the need to determine the best TFD for ECG analysis. In this study, a performance evaluation of five TFDs in term of ECG abnormality detection is presented. The detection criteria based on extracted features from most important ECG signal components (QRS) to detect normal and abnormal cases. This is achieved by estimating its energy concentration magnitude using the TFDs. The TFDs analyse ECG signals in one-minute interval instead of conventional time domain approach that analyses based on beat or frame containing several beats. The MIT-BIH normal sinus rhythm ECG database total records of 18 long-term ECG sampled at 128 Hz have been analysed. The tested TFDs include Dual-Tree Wavelet Transform, Spectrogram, Pseudo Wigner-Ville, Choi-Williams, and Born-Jordan. Each record is divided into one-minute slots, which is not considered previously, and analysed. The sample periods (slots) are randomly selected ten minutes interval for each record. This result with 99.44% detection accuracy for 15,735 ECG beats shows that Choi-Williams distribution is most reliable to be used for heart problem detection especially in automated systems that provide continuous monitoring for long time duration.     

Presence of Fragmented QRS Complexes in Patients with Obstructive Sleep Apnea Syndrome

Background:

Obstructive sleep apnea syndrome (OSAS) is a disease with increasing prevalence, which is mainly characterized by increased cardiopulmonary mortality and morbidity. It is well-known that OSAS patients have increased prevalence of cardiovascular diseases including coronary heart disease, heart failure, and arrhythmias. The aim of this study was to evaluate the presence of prolonged and fragmented QRS complexes, which have previously been associated with cardiovascular mortality, in OSAS patients.

Methods:

Our study included 51 patients (mean age 41.6 ± 10.1 years) who were recently diagnosed with OSAS (apnea-hypopnea index [AHI] ≥5 events/h) and never received therapy. The control group consisted of 34 volunteers (mean age 43.1 ± 11.6 years) in whom OSAS was excluded (AHI <5 events/h). The longest QRS complexes was measured in the 12-lead electrocardiogram (ECG) and the presence of fragmentation in QRS complexes was investigated.

Results:

Fragmented QRS frequency was significantly higher in patients with OSAS (n = 31 [61%] vs. n = 12 [35%], P = 0.021). QRS and QTc durations were also significantly longer in OSAS patients than controls (99.8 ± 13.9 ms vs. 84.7 ± 14.3 ms, P < 0.001; 411.4 ± 26.9 ms vs. 390.1 ± 32.2 ms, P = 0.001, respectively). Analysis of the patient and controls groups combined revealed a weak-moderate correlation between AHI and QRS duration (r = 0.292, P = 0.070). OSAS group had no correlation between AHI and QRS duration (r = −0.231, P = 0.203).

Conclusions:

In our study fragmented QRS frequency and QRS duration were found to increase in OSAS patients. Both parameters are related with increased cardiovascular mortality. Considering the prognostic importance of ECG parameters, it may be reasonable to recommend more detailed evaluation of OSAS patients with fragmented or prolonged QRS complexes with respect to presence of cardiovascular diseases.     

Automatic Classification of Heartbeats Using Wavelet Neural Network

The electrocardiogram (ECG) signal is widely employed as one of the most important tools in clinical practice in order to assess the cardiac status of patients. The classification of the ECG into different pathologic disease categories is a complex pattern recognition task. In this paper, we propose a method for ECG heartbeat pattern recognition using wavelet neural network (WNN). To achieve this objective, an algorithm for QRS detection is first implemented, then a WNN Classifier is developed. The experimental results obtained by testing the proposed approach on ECG data from the MIT-BIH arrhythmia database demonstrate the efficiency of such an approach when compared with other methods existing in the literature.     

心电特征参数检测

心电特征参数检测在心律失常监护、分析上具有重要作用。本文根据波形边界识别函数,采用模拟方法设计了QRS波群宽度捡测电路,能对大部分复杂的心电信号准确地检测出QRS波群宽度。     

心电图诊断左室肥大不同标准的比较研究

目的探讨心电图（ECG）12导联QRS波总振幅（∑QRS）诊断左室肥大（LVH）的价值。方法应用∑QRS作为诊断LVH指标,以超声心动图（UCG）测定左心室重量（LVM）及左室重量指数（LVMI）作为诊断LVH的参考标准,与Cornell指数和Sokolow指数诊断LVH的敏感性、特异性和准确性进行比较。结果3种标准诊断的特异性均（98%,灵敏度和准确性比较：∑QRS（Cornell指数（Sokolow指数,∑QRS与Cornell指数和Sokolow指数比较差异均有统计学意义（P〈0.05）;ECG胸导联与肢导联QRS波振幅比值越大,其诊断LVH的特异性越高,但灵敏度越低。结论采用∑QRS作为评价指标,可提高ECG诊断LVH的敏感性和特异性,胸导联与肢导联QRS振幅比值能校正个体间的差异,与其他诊断条件综合应用可提高ECG对LVH诊断的准确性。     

穴位埋线对慢性胃炎患者胃电图及胃肠激素的影响

[目的]观察穴位埋线治疗慢性胃炎的临床疗效。[方法]将70例慢性胃炎患者随机分为治疗组(埋线组)与对照组 (针刺组)进行相应治疗,并观察胃电图及血浆胃泌素(Gas)、P物质(SP)含量的改变。 [结果]埋线组有效率为 88.89％,优于针刺组的76.47％,具有显著性差异(P<0.05);两组内治疗前后餐前、餐后胃电幅值和血浆Gas、SP的差 异都具有显著性(P<0.01或P<0.05),餐前胃电频率改变不明显(P>0.05),但餐后频率改变较明显(P<0.05);两组 之间相比,治疗后两组血浆Gas、SP含量差异有显著性(P<0.05),但两组治疗前后的餐前、餐后胃电幅值及胃电频率差 异都没有显著性(P>0.05)。[结论]穴位埋线治疗慢性胃炎有肯定疗效,能调节胃肠激素,增加胃电幅值,增快胃电频 率,从而调节了胃肠机能活动。     

Design of a Biorthogonal Wavelet Transform Based R-Peak Detection and Data Compression Scheme for Implantable Cardiac Pacemaker Systems

Bradycardia can be modulated using the cardiac pacemaker, an implantable medical device which sets and balances the patient’s cardiac health. The device has been widely used to detect and monitor the patient’s heart rate. The data collected hence has the highest authenticity assurance and is convenient for further electric stimulation. In the pacemaker, ECG detector is one of the most important element. The device is available in its new digital form, which is more efficient and accurate in performance with the added advantage of economical power consumption platform. In this work, a joint algorithm based on biorthogonal wavelet transform and run-length encoding (RLE) is proposed for QRS complex detection of the ECG signal and compressing the detected ECG data. Biorthogonal wavelet transform of the input ECG signal is first calculated using a modified demand based filter bank architecture which consists of a series combination of three lowpass filters with a highpass filter. Lowpass and highpass filters are realized using a linear phase structure which reduces the hardware cost of the proposed design approximately by 50%. Then, the location of the R-peak is found by comparing the denoised ECG signal with the threshold value. The proposed R-peak detector achieves the highest sensitivity and positive predictivity of 99.75 and 99.98 respectively with the MIT-BIH arrhythmia database. Also, the proposed R-peak detector achieves a comparatively low data error rate (DER) of 0.002. The use of RLE for the compression of detected ECG data achieves a higher compression ratio (CR) of 17.1. To justify the effectiveness of the proposed algorithm, the results have been compared with the existing methods, like Huffman coding/simple predictor, Huffman coding/adaptive, and slope predictor/fixed length packaging.