摘要:目的: 通过分析健康体检者心电图异常的发生率及类型,为当地人群心血管疾病的早期诊断、早期治疗提供依据。 方法 : 采用光电三道心电图机在体检者安静休息状态下以常规12道描记,时间在15秒左右。按3个年龄段对健康体检患者心电图进行分组分析,同时对心电图异常者做病因诊断。 结果 : 1356例完成十二导联心电图监测,异常心电图占2257%,其中STT异常占首位1123%。41～60岁人群心电图异常的检出率男性较高,且多伴高血压、血糖异常、血脂异常等; 61～81 岁组人群心电图异常的检出率最高,且多已存在糖尿病、高血压和冠状动脉供血不足等疾病。 结论 :定期进行心电图检查,对早期发现、预防、诊断心血管疾病有重要意义。Abstract: Objective: To provide evidences for the early diagnosis and treatment of cardiovascular diseases through the analysis of the electrocardiographic abnormality and category. Methods : Analyzing the health examination electrocardiogram according to age and etiological diagnosis were committing to cases with electrocardiographic abnormality. Results : 1356 cases finished the electrocardiography. The rate of electrocardiographic abnormality was 2257%, and the STT abnormality hold the first place (1123%). The rate of electrocardiographic abnormality increased with the increasing age and it is highest in the 61~81 ages. Conclusion : Regular health examination by electrocardiography is important for early diagnosis, prevention and treatment of potential cardiovascular disease.
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。
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.
Heart rate variability (HRV) is the difference between the successive changes in the heartbeat cycle, and it is produced in the autonomic nervous system modulation of the sinus node of the heart. The HRV is a valuable indicator in predicting the sudden cardiac death and arrhythmic events. Traditional analysis of HRV is based on a multi-electrocardiogram (ECG), but the ECG signal acquisition is complex, so we have designed an HRV analysis system based on photoplethysmography (PPG). PPG signal is collected by a microcontroller from human’s finger, and it is sent to the terminal via USB-Serial module. The terminal software not only collects the data and plot waveforms, but also stores the data for future HRV analysis. The system is small in size, low in power consumption, and easy for operation. It is suitable for daily care no matter whether it is used at home or in a hospital.
Arrhythmia is a kind of common cardiac electrical activity abnormalities. Heartbeats classification based on electrocardiogram (ECG) is of great significance for clinical diagnosis of arrhythmia. This paper proposes a feature extraction method based on manifold learning, neighborhood preserving embedding (NPE) algorithm, to achieve the automatic classification of arrhythmia heartbeats. With classification system, we obtained low dimensional manifold structure features of high dimensional ECG signals by NPE algorithm, then we inputted the feature vectors into support vector machine (SVM) classifier for heartbeats diagnosis. Based on MIT-BIH arrhythmia database, we clustered 14 classes of arrhythmia heartbeats in the experiment, which yielded a high overall classification accuracy of 98.51%. Experimental result showed that the proposed method was an effective classification method for arrhythmia heartbeats.
Objective To analyze the electrocardiogram (ECG) and troponin (cTnI) in patients with acute coronary syndrome (ACS), so as to assess their value in diagnosing the extent of vascular lesions. Methods The results of ECG, cTnI and coronary angiography (CAG) were analyzed in 37 patients with ACS. Chi-square test and a logistic regression model were used for statistical analysis. Results In patients with positive ECG or cTnI, the results of Chi-square test showed that the incidences of coronary occlusion (P=0.016, 0.003, respectively) and coronary stenosis (P=0.121, 0.013, respectively) were significantly higher than for those with negative ECG or cTnI. The results of logistic regression analysis indicated that only cTnI was significantly correlated with coronary occlusion (P=0.013) and moderate to severe coronary stenosis (P=0.021). ECG has significant consistency with cTnI (Kappa=0.617, Plt;0.001). Conclusion Both ECG and the qual itative cTnI test can reflect the extent of vascular lesions in patients with ACS.
ObjectiveTo investigate the correlation between intima-media thickness (IMT) of carotid artery in color ultrasonography and the heart rate variability. MethodsA retrospective analysis was performed in 64 patients from West China Hospital of Sichuan University between March and May 2013. Carotid intima-media thickness was measured with color ultrasonography and dynamic electrocardiogram, and the heart rate variability was assayed at the same time. ResultsIMT in the cardiovascular disease group, combination group, coronary heart disease group and hypertension group was significantly thicker than the control group (P<0.05). The differences of SDNN and SDANN were statistically significant (P<0.05) between the combination group and the control group. There were 23 cases with IMT ≥ 1.0 mm in the cardiovascular disease group including 8 cases in the combination group, 10 cases in the coronary heart disease group and 5 cases in the hypertension group. IMT in those groups were all significantly higher than that in the control group with only 2 cases having IMT ≥ 1.0 mm (P<0.05). There were 18 cases with SDNN<100 ms in the cardiovascular disease group including 7 cases in the combination group, 6 cases in the coronary heart disease group and 5 cases in the hypertension group, but there was no statistically significant difference compared with that in the control group with only 11 cases (P>0.05). Negative correlation was found between IMT and SDNN, SDANN in the cardiovascular diseases group (r=-0.574, -0.544; P<0.01) and negative correlation was found between IMT and SDANN in the control group (r=-0.392, P<0.05). ConclusionThe carotid artery lesions and autonomic nerve especially sympathetic nerve dysfunction are obvious in patients with cardiovascular diseases and there is a negative correlation between them.
Atrial fibrillation (AF) is one of the most common arrhythmias. Today, there are a large number of AF patients worldwide, and incidence increases with the increase of age. However, the current diagnosis rate of AF via auxiliary examination is relatively low. In view of the widespread application of artificial intelligence (AI) in the medical field, the diagnosis of AF using AI has also become a research hotspot. This article briefly introduces the relevant aspects of AI and reviews the application of AI in AF prediction.