Wavelet Transformation Based Watermarking Technique for Human Electrocardiogram (ECG)

Nowadays, watermarking has become a technology of choice for a broad range of multimedia copyright protection applications. Watermarks have also been used to embed prespecified data in biomedical signals. Thus, the watermarked biomedical signals being transmitted through communication are resistant to some attacks. This paper investigates discrete wavelet transform based watermarking technique for signal integrity verification in an Electrocardiogram (ECG) coming from four ECG classes for monitoring application of cardiovascular diseases. The proposed technique is evaluated under different noisy conditions for different wavelet functions. Daubechies (db2) wavelet function based technique performs better than those of Biorthogonal (bior5.5) wavelet function. For the beat-to-beat applications, all performance results belonging to four ECG classes are highly moderate.     

胎儿心电技术在胎儿监护中的研究和应用进展

胎儿心电技术是一种新兴的胎儿监测方法。其通过基于胎儿心电技术的母胎Holter监测仪对孕妇进行持续的非产时和(或)产时胎儿心率监测,其与传统胎儿电子监护相比具有一定优势。该方法可以记录并采集胎儿心电数据,通过仪器直接得到或经过软件计算得到相关胎儿心率量化参数,并且国内外对各项量化参数的特点及其与围生儿不良结局的关系进行了相关研究,并获得一些研究成果。希望借助这种监测方法可以协助医师更准确地判断胎儿宫内情况,进而予以适当干预,降低剖宫产率,促进自然分娩,改善母儿不良预后。     

Quality Aware Compression of Electrocardiogram Using Principal Component Analysis

Electrocardiogram (ECG) compression finds wide application in various patient monitoring purposes. Quality control in ECG compression ensures reconstruction quality and its clinical acceptance for diagnostic decision making. In this paper, a quality aware compression method of single lead ECG is described using principal component analysis (PCA). After pre-processing, beat extraction and PCA decomposition, two independent quality criteria, namely, bit rate control (BRC) or error control (EC) criteria were set to select optimal principal components, eigenvectors and their quantization level to achieve desired bit rate or error measure. The selected principal components and eigenvectors were finally compressed using a modified delta and Huffman encoder. The algorithms were validated with 32 sets of MIT Arrhythmia data and 60 normal and 30 sets of diagnostic ECG data from PTB Diagnostic ECG data ptbdb, all at 1 kHz sampling. For BRC with a CR threshold of 40, an average Compression Ratio (CR), percentage root mean squared difference normalized (PRDN) and maximum absolute error (MAE) of 50.74, 16.22 and 0.243 mV respectively were obtained. For EC with an upper limit of 5 % PRDN and 0.1 mV MAE, the average CR, PRDN and MAE of 9.48, 4.13 and 0.049 mV respectively were obtained. For mitdb data 117, the reconstruction quality could be preserved up to CR of 68.96 by extending the BRC threshold. The proposed method yields better results than recently published works on quality controlled ECG compression.     

Automated Screening of Arrhythmia Using Wavelet Based Machine Learning Techniques

Arrhythmia is one of the preventive cardiac problems frequently occurs all over the globe. In order to screen such disease at early stage, this work attempts to develop a system approach based on registration, feature extraction using discrete wavelet transform (DWT), feature validation and classification of electrocardiogram (ECG). This diagnostic issue is set as a two-class pattern classification problem (normal sinus rhythm versus arrhythmia) where MIT-BIH database is considered for training, testing and clinical validation. Here DWT is applied to extract multi-resolution coefficients followed by registration using Pan Tompkins algorithm based R point detection. Moreover, feature space is compressed using sub-band principal component analysis (PCA) and statistically validated using independent sample t-test. Thereafter, the machine learning algorithms viz., Gaussian mixture model (GMM), error back propagation neural network (EBPNN) and support vector machine (SVM) are employed for pattern classification. Results are studied and compared. It is observed that both supervised classifiers EBPNN and SVM lead to higher (93.41% and 95.60% respectively) accuracy in comparison with GMM (87.36%) for arrhythmia screening.     

用动态心电图和常规心电图诊断冠心病心律失常的效果对比

目的 对比冠心病心律失常应用动态心电图及常规心电图诊断的结果.方法 随机将该院于2014年7月—2015年12月收治的116例冠心病心律失常患者分为A组和B组,分别应用动态心电图和常规心电图诊断,观察诊断结果 .结果 A组心肌缺血阳性检出率67.2%,B组心肌缺血阳性检出率56.9%,差异无统计学意义(P>0.05);A组与B组患者室性早搏二三联律、房性早搏二三联律、房性早搏成对、室性早搏成对、短阵室上速检出率分别为43.1%、46.6%、23.5%、25.9%、24.1%,24.1%、25.9%、8.6%、10.3%、6.9%,A组均高于B组,差异具有统计学意义(P<0.05).结论 与常规心电图相比,动态心电图诊断冠心病心律失常的准确率更高,应在临床中进一步的推广应用.     

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.     

胎儿心电信号的提取及远程监护系统的研究

实时准确地进行胎儿心电信号无创检测和远程监护是胎儿监护的发展方向.本文介绍了目前胎儿心电信号的提取方法及远程监护系统的研究进展.     

胎头深陷行子宫下段剖宫产取胎方法临床分析

目的 探讨产程中剖宫产胎头深陷娩头困难时的两种取胎方法,避免子宫切口延伸的发生,降低产褥病率发生率.方法 随机选取2014年7月-2016年7月于扬州市妇幼保健院病房分娩的产程中胎头深陷行子宫下段剖宫产术,常规方法娩头困难50例,一组应用足牵引臀位取胎法(观察组25例),另一组取胎前经阴道上推胎头头位取出法(对照组25例),对两种方法进行临床分析.结果 观察组患者术中出血量(241.2±12.81)mL﹑手术时间(46.6±1.34)min﹑子宫切口延伸2例﹑产褥病率4例,对照组出血量(304±57.29)mL﹑手术时间(59.2±7.71)min﹑子宫切口延伸8例﹑产褥病率7例,观察组患者术中出血量﹑手术时间﹑子宫切口延伸﹑产褥病率发生率均低于对照组,差异有统计学意义(P<0.05).结论 对于胎头深陷剖宫产选用足牵引臀位取胎法,减少了术中出血量﹑手术时间﹑子宫切口延伸和产褥病率发生率,在临床中选用,相对比较安全.     

基于小波分析的变频调速电动机运行噪声-负载特性

分析了交-交变频器输出电压、电流和时间谐波磁势的谐波族特征,指出了该特征对电动机运行噪声的影响。提出了一种改进M allat算法,并运用该算法对运行噪声进行了小波分析。该算法的计算量和占用的存储空间较标准M allat算法均减少了49.6%。同时,运用基于小波分析的频带能量法提取运行噪声的特征。经实验验证:电动机负载增加,运行噪声的86~172H z频带能量呈现一致性增加,该频带能量可以作为反映负载变化的主要特征。     

冠状动脉造影与动态心电图对比分析

目的:探讨动态心电图检查对冠心病的诊断价值.方法:对怀柔区第一医院323例冠状动脉造影者与动态心电图结果进行分析对比.结果:323例冠状动脉造影阳性223例,阳性率69.04%.动态心电图阳性234例,阳性率72.45%.且在男女各年龄组中[除女性(40～50)岁年龄组]冠状动脉造影阳性率与动态心电图阳性率结果存在.致性(P<0.05).(动态心电图检查阴性89例中,冠状动脉造影阳性12例,其中3例为严重的冠状动脉双支病变,2例严重的三支病变,需做冠状动脉搭桥术治疗).而在冠状动脉造影100例阴性患者中有23例动态心电图呈阳性表现.结论:动态心电图检查是临床冠心病无创性检查方法之一,它的阳性更多的体现患者心脏血液供应的24小时病变情况,对临床诊断和治疗有着重要的意义.     

高原鼠兔心电图观测研究

为探讨高原鼠兔作为实验性心电观察的应用前景,描记了30例成年高原鼠兔心电图。结果表明,高原鼠兔心电图基本波形与人类相似,均为窦性心律。其心率较快,359.6±28.3次/min;P波通常出现在T波的降支上;其Q—T间期,K值较大鼠、小鼠和长爪沙鼠大;心电轴为59.5±43.6度;有S—T段的个体出现率为73％。高原鼠兔较大鼠、小鼠和长爪沙鼠的心电图更接近于人,提示它更适于做临床心电图的实验研究。     

麻醉状态下WHBE兔的心电图观测

目的探讨WHBE兔作为实验性心电图观察的应用前景。方法取成年WHBE兔、日本大耳白兔和青紫兰兔,经戊巴比妥钠溶液耳静脉注射麻醉后固定,应用多道生理记录仪描记了实验兔的心电图。结果WHBE兔心电图的基本波形与人类相似,均为窦性心律。其心率较快,288．89±28．14次／min;P波通常出现在T波的降支上;其Q—T间期,K值较日本大耳白兔和青紫兰兔小;心电轴为40．00±36．93度。结论WHBE兔胸导联的波形明显而有规律,所测得的电压比较高,因此胸导联在临床上实际意义较大,提示它适于做临床心电图的实验研究。     

同种异体原位心脏移植术后心电图分析

目的：探讨同种异体原位心脏移植术后患者的心电图改变特点，方法：对3例心脏移植的患者术后分别进行体表12导联心电图跟踪观察，主要观察心率、心律、ST-T演变情况，结果：共同表现的ST段抬高系心肌再灌注后损伤性改变，不完全性右束支传导阻滞与手术中机械刺激或损伤有关，由于去神经作用心率变化较小，应用标准术式患者易出现心律失常，应用双腔静脉原位心脏移植术后心律失常发生率较少，结论：心脏移植术前、术后心电图追踪观察有助于指导治疗和评估预后。     

脑梗死患者经颅多普勒超声与心电图分析

目的探讨经颅多普勒超声（TCD）对脑梗死患者的临床应用价值,为防治脑血管病提供依据。方法应用TCD检测43例脑梗死合并心电图异常患者（A组）及33例脑梗死无心电图异常患者（B组）大脑中动脉（MCA）、大脑前动脉（ACA）、大脑后动脉（PCA）椎动脉（VA）、基底动脉（BA）的收缩期血流速度（Vs）、舒张末期血流速度（Vd）、平均血流速度（Vm）、搏动指数（PI）、阻力指数（RI）,并对两组患者结果进行比较和分析。结果A组各观察动脉的PI、RI均高于B组（P〈0.05）;A组各动脉血流速度加快、血流速度减慢、血管狭窄发生率均高于B组（P〈0.05）。结论伴心电图异常的脑梗死病人其脑血管受损程度较严重,需要临床医生注意防范再梗死的危险。     

迈瑞PM系列心电监护仪常见故障分析与处理

本文主要介绍了我院PM系列心电监护仪的三例故障分析及处理方法。     

Medical Knowledge Extraction and Analysis from Electronic Medical Records Using Deep Learning

Objectives Medical knowledge extraction (MKE) plays a key role in natural language processing (NLP) research in electronic medical records (EMR), which are the important digital carriers for recording medical activities of patients. Named entity recognition (NER) and medical relation extraction (MRE) are two basic tasks of MKE. This study aims to improve the recognition accuracy of these two tasks by exploring deep learning methods.Methods This study discussed and built two application scenes of bidirectional long short-term memory combined conditional random field (BiLSTM-CRF) model for NER and MRE tasks. In the data preprocessing of both tasks, a GloVe word embedding model was used to vectorize words. In the NER task, a sequence labeling strategy was used to classify each word tag by the joint probability distribution through the CRF layer. In the MRE task, the medical entity relation category was predicted by transforming the classification problem of a single entity into a sequence classification problem and linking the feature combinations between entities also through the CRF layer.Results Through the validation on the I2B2 2010 public dataset, the BiLSTM-CRF models built in this study got much better results than the baseline methods in the two tasks, where the F1-measure was up to 0.88 in NER task and 0.78 in MRE task. Moreover, the model converged faster and avoided problems such as overfitting.Conclusion This study proved the good performance of deep learning on medical knowledge extraction. It also verified the feasibility of the BiLSTM-CRF model in different application scenarios, laying the foundation for the subsequent work in the EMR field.     

Analysis of the Electrogastrogram Using Discrete Wavelet Transform and Statistical Methods to Detect Gastric Dysrhythmia

Electrogastrography (EGG) is a method of recording stomach electrical activity from cutaneous electrodes placed on the abdominal surface. Compared with other electrophysiological measurements, such as electrocardiography, the progress of the applicability of the EGG has been very slow. Unlike imaging or manometrical studies, stomach motility disorders are not diagnosed based only on abnormal EGG parameters. Limitations of EGG recording, processing, computation, acceptable normal parameters, technique and reading should be known to conduct subjective assessments when EGG is used to resolve stomach dysfunction. Therefore appropriate application of non-invasive EGG should go on providing more information and insight in understanding these limitations. And so the aim of this study were to contribute the evolution of the EGG to enter the clinical world as a routine check-up method and to develop new time-frequency analysis method for the detection of gastric dysrhythmia from the EGG.     

Non-Invasive Diagnosis of Stress Urinary Incontinence Sub Types Using Wavelet Analysis, Shannon Entropy and Principal Component Analysis

Urinary incontinence is a common female disorder. Although generally not a serious condition, it negatively affects the lifestyle and daily activity of subjects. Stress urinary incontinence (SUI) is the most versatile of several incontinence types and is distinguished by physical degeneration of the continence-providing mechanism. Some surgical treatment methods exist, but the success of the surgery mainly depends upon a correct diagnosis. Diagnosis has two major steps: subjects who are suffering from true SUI must be identified, and the SUI sub-type must be determined, because each sub-type is treated with a different surgery. The first step is straightforward and uses standard identification methods. The second step, however, requires invasive, uncomfortable urodynamic studies that are difficult to apply. Many subjects try to cope with the disorder rather than seek treatment from health care providers, in part because of the invasive diagnostic methods. In this study, a diagnostic method with a success rate comparable to that of urodynamic studies is presented. This new method has some advantages over the current one. First, it is noninvasive; data are collected using Doppler ultrasound recording. Second, it requires no special tools and is easy to apply, relatively inexpensive, faster and more hygienic.     

食品中农药残留的前处理技术进展

本文较为详细地讨论了近年我国常用农药残留的前处理技术，介绍了近年来发展起来的农药残留前处理的新技术。     

双侧束支主干阻滞心电图与临床分析

目的：探讨双侧束支主干阻滞心电图表现形式与临床的相关性。方法分析34例双侧束支主干阻滞表现形式及组合类型,结合病因及临床意义进行分析。结果一度房室传导阻滞（ AVB）伴一侧束支完全阻滞17例,不同比例双侧束支主干二度阻滞14例,双侧束支不同比例二度合并高度阻滞3例。均患有严重器质性心脏病,97%见于60岁以上老年患者。结论双侧束支主干阻滞是最严重的一类双束支阻滞。