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.
Using LabVIEW programming and highspeed multifunction data acquisition card PCI6251, we designed an electrocardiogram (ECG) signal generator based on Chinese typical ECG database. When the ECG signals are given off by the generator, the generator can also display the ECG information annotations at the same time, including waveform data and diagnostic results. It could be a useful assisting tool of ECG automatic diagnose instruments.
[Abstract]Currently, the academic community, industry, and governmental institutions worldwide are dedicated to developing and applying artificial intelligence and other advanced analytical tools to drive the transformation of healthcare services. However, there are still many challenges, with only a few artificial intelligence tools having achieved sufficient effectiveness in improving clinical outcomes for cardiovascular diseases and strokes to be widely used. In response, the American Heart Association has formulated related scientific statements outlining the latest research developments in artificial intelligence algorithms and data science for the diagnosis, classification, and treatment of cardiovascular diseases. These statements also summarize the current best practices, research gaps, and existing challenges of artificial intelligence tools, aiming to promote the development of this field. This article interprets this scientific statement in conjunction with the relevant research practices of the author's team.