  Hypertension is commonly referred to as the "silent killer", since it can
lead to severe health complications without any visible symptoms. Early
detection of hypertension is crucial in preventing significant health issues.
Although some studies suggest a relationship between blood pressure and certain
vital signals, such as Photoplethysmogram (PPG), reliable generalization of the
proposed blood pressure estimation methods is not yet guaranteed. This lack of
certainty has resulted in some studies doubting the existence of such
relationships, or considering them weak and limited to heart rate and blood
pressure. In this paper, a high-dimensional representation technique based on
random convolution kernels is proposed for hypertension detection using PPG
signals. The results show that this relationship extends beyond heart rate and
blood pressure, demonstrating the feasibility of hypertension detection with
generalization. Additionally, the utilized transform using convolution kernels,
as an end-to-end time-series feature extractor, outperforms the methods
proposed in the previous studies and state-of-the-art deep learning models.
