Hypertension Detection From High-Dimensional Representation of Photoplethysmogram Signals
This addresses early detection of hypertension, a critical health issue, but appears incremental as it builds on existing relationships between signals and blood pressure.
The paper tackled hypertension detection from photoplethysmogram signals by proposing a high-dimensional representation technique using random convolution kernels, showing feasibility with generalization and outperforming previous methods and state-of-the-art deep learning models.
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.