# Hypertension Detection From High-Dimensional Representation of Photoplethysmogram Signals

arXiv: https://arxiv.org/abs/2308.02425 · HTML: https://mlsanity.com/paper/2308.02425/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 25 references that are in the map.
- First posted on arXiv 31 Jul 2023.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [MINIROCKET: A Very Fast (Almost) Deterministic Transform for Time Series Classification](https://mlsanity.com/paper/2012.08791/) (2020)
- [Recent Advances in Recurrent Neural Networks](https://mlsanity.com/paper/1801.01078/) (2017)
- [Assessment of deep learning based blood pressure prediction from PPG and rPPG signals](https://mlsanity.com/paper/2104.09313/) (2021)
- [LiteHAR: Lightweight Human Activity Recognition from WiFi Signals with Random Convolution Kernels](https://mlsanity.com/paper/2201.09310/) (2022)
- [S-Rocket: Selective Random Convolution Kernels for Time Series Classification](https://mlsanity.com/paper/2203.03445/) (2022)
- [Joint Human Orientation-Activity Recognition Using WiFi Signals for Human-Machine Interaction](https://mlsanity.com/paper/2210.05078/) (2022)
- [Random Convolution Kernels with Multi-Scale Decomposition for Preterm EEG Inter-burst Detection](https://mlsanity.com/paper/2108.02039/) (2021)
- ["Can't Take the Pressure?": Examining the Challenges of Blood Pressure Estimation via Pulse Wave Analysis](https://mlsanity.com/paper/2304.14916/) (2023)

## Built on by (most cited first, 2 of 2)

- [Exploring the limitations of blood pressure estimation using the photoplethysmography signal](https://mlsanity.com/paper/2404.16049/) (2024)
- [Deep Learning on Hester Davis Scores for Inpatient Fall Prediction](https://mlsanity.com/paper/2501.06432/) (2025)

## Abstract (arXiv)

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.