Background and Objective:

As the population becomes older and more overweight, the number of potential high-risk subjects with hypertension continues to increase. ICT technologies can provide valuable support for the early assessment of such cases since the practice of conducting medical examinations for the early recognition of high-risk subjects affected by hypertension is quite difficult, time-consuming, and expensive.

Methods:

This paper presents a novel time series-based approach for the early identification of increases in hypertension to discriminate between cardiovascular high-risk and low-risk hypertensive patients through the analyses of electrocardiographic holter signals.

Results:

The experimental results show that the proposed model achieves excellent results in terms of classification accuracy compared with the state-of-the-art. In terms of performances, our model reaches an average accuracy at 98%, Sensitivity and Specificity achieve both an average value at 97%.

Conclusion:

The analysis of the whole time series shows promising results in terms of highlighting the tiny differences between subjects affected by hypertension.

中文翻译：

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基于混合ECG的深度网络，可早期识别高血压患者的重大心血管事件的高风险

背景与目的：

随着人口的老龄化和超重，潜在的高血压高危人群的数量继续增加。ICT技术可以为此类病例的早期评估提供有价值的支持，因为进行医学检查以及早识别受高血压影响的高风险受试者的做法非常困难，耗时且昂贵。

方法：

本文提出了一种基于时间序列的新颖方法，可通过心电图动态心电图信号的分析，早期识别高血压的升高，从而区分心血管高危和低危高血压患者。

结果：

实验结果表明，与最新技术相比，该模型在分类精度方面取得了优异的结果。在性能方面，我们的模型的平均准确度达到98％，灵敏度和特异性均达到97％的平均值。

结论：

整个时间序列的分析在强调受高血压影响的受试者之间的微小差异方面显示出令人鼓舞的结果。