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.
Keywords: Deep learning; Early hypertension identification; Signal processing; Time series classification; eHealth.
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