Hypertension Detection From High-Dimensional Representation of Photoplethysmogram Signals

• URL: http://arxiv.org/abs/2308.02425v1
• Date: Mon, 31 Jul 2023 00:09:23 GMT
• Title: Hypertension Detection From High-Dimensional Representation of Photoplethysmogram Signals
• Authors: Navid Hasanzadeh, Shahrokh Valaee, Hojjat Salehinejad
• Abstract summary: Early detection of hypertension is crucial in preventing significant health issues. Some studies suggest a relationship between blood pressure and certain vital signals, such as Photoplethysmogram ( PPG) In this paper, a high-dimensional representation technique based on random convolution kernels is proposed for hypertension detection using PPG signals.
• Score: 38.497450879376
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

Related papers

• The Pitfalls and Promise of Conformal Inference Under Adversarial Attacks [90.52808174102157]
  In safety-critical applications such as medical imaging and autonomous driving, it is imperative to maintain both high adversarial robustness to protect against potential adversarial attacks. A notable knowledge gap remains concerning the uncertainty inherent in adversarially trained models. This study investigates the uncertainty of deep learning models by examining the performance of conformal prediction (CP) in the context of standard adversarial attacks.
  arXiv  Detail & Related papers  (2024-05-14T18:05:19Z)
• Towards Understanding the Robustness of Diffusion-Based Purification: A Stochastic Perspective [65.10019978876863]
  Diffusion-Based Purification (DBP) has emerged as an effective defense mechanism against adversarial attacks. In this paper, we argue that the inherentity in the DBP process is the primary driver of its robustness.
  arXiv  Detail & Related papers  (2024-04-22T16:10:38Z)
• Bias in Pruned Vision Models: In-Depth Analysis and Countermeasures [93.17009514112702]
  Pruning, setting a significant subset of the parameters of a neural network to zero, is one of the most popular methods of model compression. Despite existing evidence for this phenomenon, the relationship between neural network pruning and induced bias is not well-understood.
  arXiv  Detail & Related papers  (2023-04-25T07:42:06Z)
• PPG Signals for Hypertension Diagnosis: A Novel Method using Deep Learning Models [0.0]
  A novel method is proposed for classifying hypertension stages using Photoplethysmography (Pool) signals and deep learning models. The PPG signal is a non-invasive method of measuring blood pressure through the use of light sensors that measure the changes in blood volume in the microvasculature of tissues. The results show the proposed method achieves high accuracy in classifying hypertension stages, demonstrating the potential of PPG signals and deep learning models in hypertension diagnosis and management.
  arXiv  Detail & Related papers  (2023-04-14T06:40:10Z)
• Identifying Rhythmic Patterns for Face Forgery Detection and Categorization [46.21354355137544]
  We propose a framework for face forgery detection and categorization consisting of: 1) a Spatial-Temporal Filtering Network (STFNet) for PPG signals, and 2) a Spatial-Temporal Interaction Network (STINet) for constraint and interaction of PPG signals. With insight into the generation of forgery methods, we further propose intra-source and inter-source blending to boost the performance of the framework.
  arXiv  Detail & Related papers  (2022-07-04T04:57:06Z)
• InsightNet: non-contact blood pressure measuring network based on face video [9.824584818736032]
  Blood pressure data are mainly acquired through contact sensors, which require high maintenance and may be inconvenient and unfriendly to some people. In this paper, an efficient non-contact blood pressure measurement network based on face videos is proposed for the first time.
  arXiv  Detail & Related papers  (2022-03-07T07:06:42Z)
• A Deep Learning Approach to Predict Blood Pressure from PPG Signals [10.028103259763352]
  Blood Pressure (BP) is one of the four primary vital signs indicating the status of the body's vital (life-sustaining) functions. We propose an advanced data-driven approach that uses a three-layer deep neural network to estimate BP based on PPG signals.
  arXiv  Detail & Related papers  (2021-07-30T22:45:34Z)
• Ambulatory blood pressure monitoring versus office blood pressure measurement: Are there sex differences? [55.41644538483948]
  Office Blood Pressure Measurement (OBP) is a technique performed in-office with the sphygmomanometer, while Ambulatory Blood Pressure Monitoring (ABPM) is a technique that measures blood pressure during 24h. The aim of this study is to examine the possible influence of sex on the discrepancies between OBP and ABPM in 872 subjects with known or suspected hypertension.
  arXiv  Detail & Related papers  (2021-06-04T10:09:44Z)
• Estimation of Continuous Blood Pressure from PPG via a Federated Learning Approach [5.275287291173557]
  Ischemic heart disease is the highest cause of mortality globally each year. To understand the dynamics of the healthy and unhealthy heart doctors commonly use electrocardiogram (ECG) and arterial pressure (BP) readings. These methods are often quite invasive, in particular when arterial pressure (ABP) readings are taken and not to mention very costly. We train our framework across distributed models and data sources to mimic a large-scale collaborative learning experiment that could be implemented across low-cost wearables.
  arXiv  Detail & Related papers  (2021-02-24T12:11:23Z)
• Continuous Monitoring of Blood Pressure with Evidential Regression [19.92542487970484]
  Photoplethysmogram (MIC) signal-based blood pressure estimation is a promising candidate for modern BP measurements. The proposed method provides the reliability of the predicted BP by estimating its uncertainty to help diagnose medical condition.
  arXiv  Detail & Related papers  (2021-02-06T09:09:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.