A Shallow U-Net Architecture for Reliably Predicting Blood Pressure (BP) from Photoplethysmogram (PPG) and Electrocardiogram (ECG) Signals

• URL: http://arxiv.org/abs/2111.08480v1
• Date: Fri, 12 Nov 2021 19:34:20 GMT
• Title: A Shallow U-Net Architecture for Reliably Predicting Blood Pressure (BP) from Photoplethysmogram (PPG) and Electrocardiogram (ECG) Signals
• Authors: Sakib Mahmud, Nabil Ibtehaz, Amith Khandakar, Anas Tahir, Tawsifur Rahman, Khandaker Reajul Islam, Md Shafayet Hossain, M. Sohel Rahman, Mohammad Tariqul Islam, Muhammad E. H. Chowdhury
• Abstract summary: Most existing methods used in the hospitals for continuous monitoring of Blood Pressure (BP) are invasive. In this study, we explored the applicability of autoencoders in predicting BP from non-invasively collectible signals such as Photoplethysmogram ( PPG) and ECG signals. It was found that a very shallow, one-dimensional autoencoder can extract the relevant features to predict the SBP and DBP with the state-of-the-art performance on a very large dataset.
• Score: 1.1695966610359496
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Cardiovascular diseases are the most common causes of death around the world. To detect and treat heart-related diseases, continuous Blood Pressure (BP) monitoring along with many other parameters are required. Several invasive and non-invasive methods have been developed for this purpose. Most existing methods used in the hospitals for continuous monitoring of BP are invasive. On the contrary, cuff-based BP monitoring methods, which can predict Systolic Blood Pressure (SBP) and Diastolic Blood Pressure (DBP), cannot be used for continuous monitoring. Several studies attempted to predict BP from non-invasively collectible signals such as Photoplethysmogram (PPG) and Electrocardiogram (ECG), which can be used for continuous monitoring. In this study, we explored the applicability of autoencoders in predicting BP from PPG and ECG signals. The investigation was carried out on 12,000 instances of 942 patients of the MIMIC-II dataset and it was found that a very shallow, one-dimensional autoencoder can extract the relevant features to predict the SBP and DBP with the state-of-the-art performance on a very large dataset. Independent test set from a portion of the MIMIC-II dataset provides an MAE of 2.333 and 0.713 for SBP and DBP, respectively. On an external dataset of forty subjects, the model trained on the MIMIC-II dataset, provides an MAE of 2.728 and 1.166 for SBP and DBP, respectively. For both the cases, the results met British Hypertension Society (BHS) Grade A and surpassed the studies from the current literature.

Related papers

• TransfoRhythm: A Transformer Architecture Conductive to Blood Pressure Estimation via Solo PPG Signal Capturing [5.255373360156652]
  Blood pressure (BP) serves as a critical health indicator for accurate and timely diagnosis and/or treatment of hypertension. Recent advancements in Artificial Intelligence (AI) and Deep Neural Networks (DNNs) have led to a surge of interest in developing data-driven solutions. This paper represents the first study to apply the MIMIC IV dataset for cuff-less BP estimation.
  arXiv  Detail & Related papers  (2024-04-15T00:36:33Z)
• A Finger on the Pulse of Cardiovascular Health: Estimating Blood Pressure with Smartphone Photoplethysmography-Based Pulse Waveform Analysis [2.4347312660509672]
  This study introduces four innovative strategies to enhance smartphone-based photoplethysmography for blood pressure estimation (SPW-BP) We employ often-neglected data-quality improvement techniques, such as height normalization, corrupt data removal, and boundary signal reconstruction. Correlation and SHAP analysis identified key features for improving BP estimation. However, Bland-Altman analysis revealed systematic biases, and MAE analysis showed that the results did not meet AAMI and BHS accuracy standards.
  arXiv  Detail & Related papers  (2024-01-20T05:05:17Z)
• Simulation-based Inference for Cardiovascular Models [57.92535897767929]
  We use simulation-based inference to solve the inverse problem of mapping waveforms back to plausible physiological parameters. We perform an in-silico uncertainty analysis of five biomarkers of clinical interest. We study the gap between in-vivo and in-silico with the MIMIC-III waveform database.
  arXiv  Detail & Related papers  (2023-07-26T02:34:57Z)
• Foresight -- Deep Generative Modelling of Patient Timelines using Electronic Health Records [46.024501445093755]
  Temporal modelling of medical history can be used to forecast and simulate future events, estimate risk, suggest alternative diagnoses or forecast complications. We present Foresight, a novel GPT3-based pipeline that uses NER+L tools (i.e. MedCAT) to convert document text into structured, coded concepts.
  arXiv  Detail & Related papers  (2022-12-13T19:06:00Z)
• Journaling Data for Daily PHQ-2 Depression Prediction and Forecasting [47.93070579578704]
  We explore the potential of using actively-collected data to predict and forecast daily PHQ-2 scores on a newly-collected longitudinal dataset. We obtain a best MAE of 1.417 for daily prediction of PHQ-2 scores, which specifically in the used dataset have a range of 0 to 12. This illustrates the additive value that can be obtained by incorporating actively-collected data in a depression monitoring application.
  arXiv  Detail & Related papers  (2022-05-06T17:47:05Z)
• BP-Net: Efficient Deep Learning for Continuous Arterial Blood Pressure Estimation using Photoplethysmogram [0.06524460254566904]
  Blood pressure is one of the most influential bio-markers for cardiovascular diseases and stroke. Current cuffless approaches to continuous BP monitoring involve explicit feature engineering surrounding Photoplethysmogram signals. We present an end-to-end deep learning solution, BP-Net, that uses PPG waveform to estimate Systolic BP (SBP), Mean Average Pressure (MAP), and Diastolic BP (DBP) through intermediate continuous Arterial BP (ABP) waveform.
  arXiv  Detail & Related papers  (2021-11-29T14:43:58Z)
• 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)
• Prediction of the onset of cardiovascular diseases from electronic health records using multi-task gated recurrent units [51.14334174570822]
  We propose a multi-task recurrent neural network with attention mechanism for predicting cardiovascular events from electronic health records. The proposed approach is compared to a standard clinical risk predictor (QRISK) and machine learning alternatives using 5-year data from a NHS Foundation Trust.
  arXiv  Detail & Related papers  (2020-07-16T17:43:13Z)
• Estimating Blood Pressure from Photoplethysmogram Signal and Demographic Features using Machine Learning Techniques [0.0]
  Hypertension is a potentially unsafe health ailment, which can be indicated directly from the Blood pressure (BP) Continuous monitoring of BP is very important; however, BP measurements are discrete and uncomfortable to the user. To address this need, a cuffless, continuous and a non-invasive BP measurement system is proposed.
  arXiv  Detail & Related papers  (2020-05-07T09:45:02Z)
• Short Term Blood Glucose Prediction based on Continuous Glucose Monitoring Data [53.01543207478818]
  This study explores the use of Continuous Glucose Monitoring (CGM) data as input for digital decision support tools. We investigate how Recurrent Neural Networks (RNNs) can be used for Short Term Blood Glucose (STBG) prediction.
  arXiv  Detail & Related papers  (2020-02-06T16:39:44Z)

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.