Electrocardiogram (ECG) is a noninvasive, inexpensive, and convenient test for diagnosing cardiovascular diseases and assessing the risk of cardiovascular events. Although there are clear standardized operations and procedures for ECG examination, the interpretation of ECG by even trained physicians can be biased due to differences in diagnostic experience. In recent years, artificial intelligence has become a powerful tool to automatically analyze medical data by building deep neural network models, and has been widely used in the field of medical image diagnosis such as CT, MRI, ultrasound and ECG. This article mainly introduces the application progress of deep neural network models in ECG diagnosis and prediction of cardiovascular diseases, and discusses its limitations and application prospects.
The monitoring of pregnant women is very important. It plays an important role in reducing fetal mortality, ensuring the safety of perinatal mother and fetus, preventing premature delivery and pregnancy accidents. At present, regular examination is the mainstream method for pregnant women's monitoring, but the means of examination out of hospital is scarce, and the equipment of hospital monitoring is expensive and the operation is complex. Using intelligent information technology (such as machine learning algorithm) can analyze the physiological signals of pregnant women, so as to realize the early detection and accident warning for mother and fetus, and achieve the purpose of high-quality monitoring out of hospital. However, at present, there are not enough public research reports related to the intelligent processing methods of out-of-hospital monitoring for pregnant women, so this paper takes the out-of-hospital monitoring for pregnant women as the research background, summarizes the public research reports of intelligent processing methods, analyzes the advantages and disadvantages of the existing research methods, points out the possible problems, and expounds the future development trend, which could provide reference for future related researches.
Portable electrocardiogram monitor is an important equipment in the clinical diagnosis of cardiovascular diseases due to its portable, real-time features. It has a broad application and development prospects in China. In the present review, previous researches on the portable electrocardiogram monitors have been arranged, analyzed and summarized. According to the characteristics of the electrocardiogram (ECG), this paper discusses the ergonomic design of the portable electrocardiogram monitor, including hardware and software. The circuit components and software modules were parsed from the ECG features and system functions. Finally, the development trend and reference are provided for the portable electrocardiogram monitors and for the subsequent research and product design.
目的 探讨冠状动脉钙化检测联合动态心电图对冠心病的诊断价值及临床应用。 方法 对2010年5月－2011年8月住院的108例拟诊冠心病的患者同期进行128排螺旋CT冠状动脉钙化积分检测、动态心电图和冠状动脉造影，对比研究冠状动脉钙化检测联合动态心电图预测冠心病的价值。 结果 冠状动脉钙化阳性预测冠心病的灵敏度、特异度、阳性预测值和阴性预测值分别为75.6%、81.0%、73.9%、82.3%；动态心电图阳性预测冠心病的灵敏度、特异度、阳性预测值和阴性预测值分别为73.3%、76.2%、68.8%、80.0%；冠状动脉钙化检测联合动态心电图的系列实验的特异度和阳性预测值分别达到96.8%和92.9%，平行试验的灵敏度和阴性预测值分别达到93.3%和92.7%，均显著高于单项试验的相应指标（P＜0.05）。 结论 高分辨率螺旋CT冠状动脉钙化检测联合动态心电图显著提高冠心病的诊断价值，可作为老年患者及基层医院冠心病首选的筛选检查。
Deep learning method can be used to automatically analyze electrocardiogram (ECG) data and rapidly implement arrhythmia classification, which provides significant clinical value for the early screening of arrhythmias. How to select arrhythmia features effectively under limited abnormal sample supervision is an urgent issue to address. This paper proposed an arrhythmia classification algorithm based on an adaptive multi-feature fusion network. The algorithm extracted RR interval features from ECG signals, employed one-dimensional convolutional neural network (1D-CNN) to extract time-domain deep features, employed Mel frequency cepstral coefficients (MFCC) and two-dimensional convolutional neural network (2D-CNN) to extract frequency-domain deep features. The features were fused using adaptive weighting strategy for arrhythmia classification. The paper used the arrhythmia database jointly developed by the Massachusetts Institute of Technology and Beth Israel Hospital (MIT-BIH) and evaluated the algorithm under the inter-patient paradigm. Experimental results demonstrated that the proposed algorithm achieved an average precision of 75.2%, an average recall of 70.1% and an average F1-score of 71.3%, demonstrating high classification accuracy and being able to provide algorithmic support for arrhythmia classification in wearable devices.
Objective To conduct a systematic review on the Electrocardiogram (ECG) changes in the workers exposed to high temperatures by means of meta-analysis.Methods The retrospective cohort studies on the relationship between high temperature and ECG abnormalities published from 1990 to May 2009 were searched in CNKI, VIP, WanFang database and CBM database. The literatures meeting the inclusive criteria were selected, the quality was assessed, the data were extracted, and the meta-analyses were conducted with RevMan 4.2.2 software. Results A total of 20 studies were included. The results of meta-analyses showed: the ECG abnormality rate of the high-temperature group was obviously superior to that of the control group with significant difference (OR=2.76, 95%CI 2.37 to 3.20, Plt;0.000 01). The high-temperature severely affected left ventricular hypertrophy (OR=3.49, 95%CI 2.83 to 4.31, Plt;0.000 01), sinus bradycardia (OR=2.83, 95%CI 2.33 to 3.43, Plt;0.000 01), and changes in ST-T segment (OR=2.63, 95%CI 1.48 to 4.68, P=0.000 10), which indicated that the abnormal changes of ECG, such as left ventricular hypertrophy, sinus tachycardia, sinus bradycardia, and changes in ST-T segment could be the sensitive indexes to monitor cardiovascular disease of workers exposed to high-temperature. Conclusion The incidence of ECG abnormalities caused by high-temperature operation is obviously superior to that of the control group, so it is required to strengthen the health monitoring and labor protection for the workers exposed to high temperature.
Lorenz plot (LP) method which gives a global view of long-time electrocardiogram signals, is an efficient simple visualization tool to analyze cardiac arrhythmias, and the morphologies and positions of the extracted attractors may reveal the underlying mechanisms of the onset and termination of arrhythmias. But automatic diagnosis is still impossible because it is lack of the method of extracting attractors by now. We presented here a methodology of attractor extraction and recognition based upon homogeneously statistical properties of the location parameters of scatter points in three dimensional LP (3DLP), which was constructed by three successive RR intervals as X, Y and Z axis in Cartesian coordinate system. Validation experiments were tested in a group of RR-interval time series and tags data with frequent unifocal premature complexes exported from a 24-hour Holter system. The results showed that this method had excellent effective not only on extraction of attractors, but also on automatic recognition of attractors by the location parameters such as the azimuth of the points peak frequency (APF) of eccentric attractors once stereographic projection of 3DLP along the space diagonal. Besides, APF was still a powerful index of differential diagnosis of atrial and ventricular extrasystole. Additional experiments proved that this method was also available on several other arrhythmias. Moreover, there were extremely relevant relationships between 3DLP and two dimensional LPs which indicate any conventional achievement of LPs could be implanted into 3DLP. It would have a broad application prospect to integrate this method into conventional long-time electrocardiogram monitoring and analysis system.
目的:通过冠脉造影探讨心电图对冠心病的诊断价值。方法:对226例可疑冠心病患者进行心电图与冠脉造影进行对比分析。结果:心电图诊断冠心病的灵敏度为 86.49％,特异度为 65.38％,假阳性率为3462％,假阴性率为 13.51％。心电图随着冠状动脉病变支数增加而检出冠心病的阳性率增高。结论:心电图是临床诊断冠心病最快捷、简便、经济而无创的有效方法,但仍存在一定的局限性。
The selection of fiducial points has an important effect on electrocardiogram (ECG) denoise with cubic spline interpolation. An improved cubic spline interpolation algorithm for suppressing ECG baseline drift is presented in this paper. Firstly the first order derivative of original ECG signal is calculated, and the maximum and minimum points of each beat are obtained, which are treated as the position of fiducial points. And then the original ECG is fed into a high pass filter with 1.5 Hz cutoff frequency. The difference between the original and the filtered ECG at the fiducial points is taken as the amplitude of the fiducial points. Then cubic spline interpolation curve fitting is used to the fiducial points, and the fitting curve is the baseline drift curve. For the two simulated case test, the correlation coefficients between the fitting curve by the presented algorithm and the simulated curve were increased by 0.242 and 0.13 compared with that from traditional cubic spline interpolation algorithm. And for the case of clinical baseline drift data, the average correlation coefficient from the presented algorithm achieved 0.972.