摘要:目的: 通過分析健康體檢者心電圖異常的發生率及類型,為當地人群心血管疾病的早期診斷、早期治療提供依據。 方法 : 采用光電三道心電圖機在體檢者安靜休息狀態下以常規12道描記,時間在15秒左右。按3個年齡段對健康體檢患者心電圖進行分組分析,同時對心電圖異常者做病因診斷。 結果 : 1356例完成十二導聯心電圖監測,異常心電圖占2257%,其中STT異常占首位1123%。41～60歲人群心電圖異常的檢出率男性較高,且多伴高血壓、血糖異常、血脂異常等; 61～81 歲組人群心電圖異常的檢出率最高,且多已存在糖尿病、高血壓和冠狀動脈供血不足等疾病。 結論 :定期進行心電圖檢查,對早期發現、預防、診斷心血管疾病有重要意義。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.
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.
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.
