Echocardiography (ECHO) is not widely available in primary care, the key structural (chamber enlargements) and functional abnormality are not easily available precluding the ability to diagnose HF other than through mainly symptomatic means. The opportunity for earlier detection of HF is lost. Using a unique database, the etiology of HF is explored by prevalence analysis to unravel the diagnostic makeup of each HF category. Various relationships and patterns of comorbidities have been extracted between the Electrocardiogram (ECG) and ECHO parameters that contribute to HF, those relationships are then confirmed and categorized by a Principal Component Analysis (PCA). Finally, it was summarized what type of non-invasive ECG-like device should be used in primary care to better diagnose HF. The sensitivity of abnormal ECHO reaches 92% over the abnormal ECG of 81% in the detection of HF. The first five PCA are discovered, which cover 49% of all the variance. Left atrial enlargement is the most representative finding in the overall comorbidity rate, which coincides with the probability direction of HF (3rd as input, 1st as finding in the coefficients), and reaches the highest (250%) prevalence increase in function of decreasing LVEF. The core structural and functional abnormalities diagnosed by ECHO with the ECG interpretation provide sufficient information to diagnose "consider HF" in primary care. This paper overview of a novel bio-signal-based system supported by Artificial Intelligence, able to replicate Echo-findings, predict HF and indicates its phenotype, suitable for use in Primary Care.

中文翻译：

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根据 ECG 和 ECHO 发现的特征得出的每个 HF 类别的诊断合并症构成，患病率分析

超声心动图 (ECHO) 在初级保健中并未广泛使用，关键的结构（腔室扩大）和功能异常不容易获得，从而排除了诊断 HF 的能力，而不是通过主要的症状手段。失去了早期检测 HF 的机会。使用一个独特的数据库，通过患病率分析探索 HF 的病因，以揭示每个 HF 类别的诊断构成。在心电图 (ECG) 和 ECHO 参数之间提取了导致 HF 的各种关系和合并症模式，然后通过主成分分析 (PCA) 确认和分类这些关系。最后，总结了在初级保健中应该使用哪种类型的非侵入性心电图设备来更好地诊断心力衰竭。异常 ECHO 对 HF 检测的敏感性超过异常心电图 81% 的 92%。前五个 PCA 被发现，覆盖了所有方差的 49%。左心房扩大是总体合并症率中最具代表性的发现，与 HF 的概率方向一致（第 3 为输入，第 1 为系数中的发现），并在降低 LVEF 的函数中达到最高 (250%) 患病率增加. ECHO 通过心电图解释诊断出的核心结构和功能异常为在初级保健中诊断“考虑 HF”提供了足够的信息。本文概述了一种由人工智能支持的基于生物信号的新型系统，能够复制回声发现、预测 HF 并指示其表型，适用于初级保健。