1
|
Elgendi M, Haugg F, Fletcher RR, Allen J, Shin H, Alian A, Menon C. Recommendations for evaluating photoplethysmography-based algorithms for blood pressure assessment. COMMUNICATIONS MEDICINE 2024; 4:140. [PMID: 38997447 PMCID: PMC11245506 DOI: 10.1038/s43856-024-00555-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 06/19/2024] [Indexed: 07/14/2024] Open
Abstract
Photoplethysmography (PPG) is a non-invasive optical technique that measures changes in blood volume in the microvascular tissue bed of the body. While it shows potential as a clinical tool for blood pressure (BP) assessment and hypertension management, several sources of error can affect its performance. One such source is the PPG-based algorithm, which can lead to measurement bias and inaccuracy. Here, we review seven widely used measures to assess PPG-based algorithm performance and recommend implementing standardized error evaluation steps in their development. This standardization can reduce bias and improve the reliability and accuracy of PPG-based BP estimation, leading to better health outcomes for patients managing hypertension.
Collapse
Affiliation(s)
- Mohamed Elgendi
- Biomedical and Mobile Health Technology Lab, Department of Health Sciences and Technology, ETH Zurich, Zurich, 8008, Switzerland.
| | - Fridolin Haugg
- Biomedical and Mobile Health Technology Lab, Department of Health Sciences and Technology, ETH Zurich, Zurich, 8008, Switzerland
| | - Richard Ribon Fletcher
- Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - John Allen
- Research Centre for Intelligent Healthcare, Coventry University, CV1 5FB, Coventry, UK
| | - Hangsik Shin
- Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea
| | - Aymen Alian
- Yale School of Medicine, Yale University, New Haven, CT, 06510, USA
| | - Carlo Menon
- Biomedical and Mobile Health Technology Lab, Department of Health Sciences and Technology, ETH Zurich, Zurich, 8008, Switzerland
| |
Collapse
|
2
|
Kohjitani H, Koshimizu H, Nakamura K, Okuno Y. Recent developments in machine learning modeling methods for hypertension treatment. Hypertens Res 2024; 47:700-707. [PMID: 38216731 DOI: 10.1038/s41440-023-01547-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 10/22/2023] [Accepted: 11/09/2023] [Indexed: 01/14/2024]
Abstract
Hypertension is the leading cause of cardiovascular complications. This review focuses on the advancements in medical artificial intelligence (AI) models aimed at individualized treatment for hypertension, with particular emphasis on the approach to time-series big data on blood pressure and the development of interpretable medical AI models. The digitalization of daily blood pressure records and the downsizing of measurement devices enable the accumulation and utilization of time-series data. As mainstream blood pressure data shift from snapshots to time series, the clinical significance of blood pressure variability will be clarified. The time-series blood pressure prediction model demonstrated the capability to forecast blood pressure variabilities with a reasonable degree of accuracy for up to four weeks in advance. In recent years, various explainable AI techniques have been proposed for different purposes of model interpretation. It is essential to select the appropriate technique based on the clinical aspects; for example, actionable path-planning techniques can present individualized intervention plans to efficiently improve outcomes such as hypertension. Despite considerable progress in this field, challenges remain, such as the need for the prospective validation of AI-driven interventions and the development of comprehensive systems that integrate multiple AI methods. Future research should focus on addressing these challenges and refining the AI models to ensure their practical applicability in real-world clinical settings. Furthermore, the implementation of interdisciplinary collaborations among AI experts, clinicians, and healthcare providers are crucial to further optimizing and validate AI-driven solutions for hypertension management.
Collapse
Affiliation(s)
- Hirohiko Kohjitani
- Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
| | - Hiroshi Koshimizu
- Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Kazuki Nakamura
- Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Yasushi Okuno
- Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
| |
Collapse
|
3
|
Park JS, Hong KS. Robust blood pressure measurement from facial videos in diverse environments. Heliyon 2024; 10:e26007. [PMID: 38434043 PMCID: PMC10906170 DOI: 10.1016/j.heliyon.2024.e26007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Revised: 02/02/2024] [Accepted: 02/06/2024] [Indexed: 03/05/2024] Open
Abstract
Blood pressure (BP) management is important worldwide, and BP monitoring is a crucial aspect of maintaining good health. Traditional BP meter measures BP independently in various situations, such as at home or work, using a cuff to maintain a stable condition. However, these devices can causes a foreign body sensation and discomfort, and are not always practical for periodic monitoring. As a result, studies have been conducted on the use of photoplethysmography (PPG) for measuring BP. However, PPG also has limitations similar to those of traditional BP meters, as it requires the placement of sensors on two regions of the body (fingers or toes). To address this issue, researchers have conducted studies on non-contact methods for measuring BP using face and hand videos. These studies have utilized two cameras to measure PTT and have focused on internal environments, resulting in low accuracy of BP measurement in external environments. We proposes a method for robust BP measurement using pulse wave velocity (PWV) and PTT calculated from facial videos. PTT is estimated by measuring the phase difference between two different regions of interest (ROIs) and PWV is calculated using PTT and the actual distance between two ROIs. In addition, our proposed method extracts the pulse wave from the ROI to measure BP. The actual distance between the ROIs and PTT are estimated using the two extracted pulse waves, and BP is then measured using PWV and PTT. To evaluate the BP measurement performance, the BP calculated from both BP meters and facial videos (in indoor, outdoor, driving car, and flying drone environments) are compared. Our results reveal that the proposed method can robustly measure BP in diverse environments.
Collapse
Affiliation(s)
- Jin-soo Park
- Department of Electrical and Computer Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon-si, 16419, Republic of Korea
| | - Kwang-seok Hong
- School of Electronic Electrical Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon-si, 16419, Republic of Korea
| |
Collapse
|
4
|
Xing X, Dong WF, Xiao R, Song M, Jiang C. Analysis of the Chaotic Component of Photoplethysmography and Its Association with Hemodynamic Parameters. ENTROPY (BASEL, SWITZERLAND) 2023; 25:1582. [PMID: 38136462 PMCID: PMC10742563 DOI: 10.3390/e25121582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 11/20/2023] [Accepted: 11/21/2023] [Indexed: 12/24/2023]
Abstract
Wearable technologies face challenges due to signal instability, hindering their usage. Thus, it is crucial to comprehend the connection between dynamic patterns in photoplethysmography (PPG) signals and cardiovascular health. In our study, we collected 401 multimodal recordings from two public databases, evaluating hemodynamic conditions like blood pressure (BP), cardiac output (CO), vascular compliance (C), and peripheral resistance (R). Using irregular-resampling auto-spectral analysis (IRASA), we quantified chaotic components in PPG signals and employed different methods to measure the fractal dimension (FD) and entropy. Our findings revealed that in surgery patients, the power of chaotic components increased with vascular stiffness. As the intensity of CO fluctuations increased, there was a notable strengthening in the correlation between most complexity measures of PPG and these parameters. Interestingly, some conventional morphological features displayed a significant decrease in correlation, indicating a shift from a static to dynamic scenario. Healthy subjects exhibited a higher percentage of chaotic components, and the correlation between complexity measures and hemodynamics in this group tended to be more pronounced. Causal analysis showed that hemodynamic fluctuations are main influencers for FD changes, with observed feedback in most cases. In conclusion, understanding chaotic patterns in PPG signals is vital for assessing cardiovascular health, especially in individuals with unstable hemodynamics or during ambulatory testing. These insights can help overcome the challenges faced by wearable technologies and enhance their usage in real-world scenarios.
Collapse
Affiliation(s)
- Xiaoman Xing
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Sciences and Technology of China, Suzhou 215163, China
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Wen-Fei Dong
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Renjie Xiao
- Medical Health Information Center, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Mingxuan Song
- Suzhou GK Medtech Science and Technology Development (Group) Co., Ltd., Suzhou 215163, China
| | - Chenyu Jiang
- Jinan Guoke Medical Technology Development Co., Ltd., Jinan 250100, China
| |
Collapse
|
5
|
Attivissimo F, D’Alessandro VI, De Palma L, Lanzolla AML, Di Nisio A. Non-Invasive Blood Pressure Sensing via Machine Learning. SENSORS (BASEL, SWITZERLAND) 2023; 23:8342. [PMID: 37837172 PMCID: PMC10574845 DOI: 10.3390/s23198342] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 09/21/2023] [Accepted: 10/07/2023] [Indexed: 10/15/2023]
Abstract
In this paper, a machine learning (ML) approach to estimate blood pressure (BP) using photoplethysmography (PPG) is presented. The final aim of this paper was to develop ML methods for estimating blood pressure (BP) in a non-invasive way that is suitable in a telemedicine health-care monitoring context. The training of regression models useful for estimating systolic blood pressure (SBP) and diastolic blood pressure (DBP) was conducted using new extracted features from PPG signals processed using the Maximal Overlap Discrete Wavelet Transform (MODWT). As a matter of fact, the interest was on the use of the most significant features obtained by the Minimum Redundancy Maximum Relevance (MRMR) selection algorithm to train eXtreme Gradient Boost (XGBoost) and Neural Network (NN) models. This aim was satisfactorily achieved by also comparing it with works in the literature; in fact, it was found that XGBoost models are more accurate than NN models in both systolic and diastolic blood pressure measurements, obtaining a Root Mean Square Error (RMSE) for SBP and DBP, respectively, of 5.67 mmHg and 3.95 mmHg. For SBP measurement, this result is an improvement compared to that reported in the literature. Furthermore, the trained XGBoost regression model fulfills the requirements of the Association for the Advancement of Medical Instrumentation (AAMI) as well as grade A of the British Hypertension Society (BHS) standard.
Collapse
Affiliation(s)
| | | | | | - Anna Maria Lucia Lanzolla
- Department of Electrical and Information Engineering, Polytechnic University of Bari, 70125 Bari, Italy; (F.A.); (V.I.D.); (L.D.P.); (A.D.N.)
| | | |
Collapse
|
6
|
Saglietto A, Scarsoglio S, Canova D, De Ferrari GM, Ridolfi L, Anselmino M. Beat-to-beat finger photoplethysmography in atrial fibrillation patients undergoing electrical cardioversion. Sci Rep 2023; 13:6751. [PMID: 37185372 PMCID: PMC10130175 DOI: 10.1038/s41598-023-33952-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 04/21/2023] [Indexed: 05/17/2023] Open
Abstract
Atrial fibrillation (AF)-induced peripheral microcirculatory alterations have poorly been investigated. The present study aims to expand current knowledge through a beat-to-beat analysis of non-invasive finger photoplethysmography (PPG) in AF patients restoring sinus rhythm by electrical cardioversion (ECV). Continuous non-invasive arterial blood pressure and left middle finger PPG pulse oximetry waveform (POW) signals were continuously recorded before and after elective ECV of consecutive AF or atrial flutter (AFL) patients. The main metrics (mean, standard deviation, coefficient of variation), as well as a beat-to-beat analysis of the pulse pressure (PP) and POW beat-averaged value (aPOW), were computed to compare pre- and post-ECV phases. 53 patients (mean age 69 ± 8 years, 79% males) were enrolled; cardioversion was successful in restoring SR in 51 (96%) and signal post-processing was feasible in 46 (87%) patients. In front of a non-significant difference in mean PP (pre-ECV: 51.96 ± 13.25, post-ECV: 49.58 ± 10.41 mmHg; p = 0.45), mean aPOW significantly increased after SR restoration (pre-ECV: 0.39 ± 0.09, post-ECV: 0.44 ± 0.06 a.u.; p < 0.001). Moreover, at beat-to-beat analysis linear regression yielded significantly different slope (m) for the PP (RR) relationship compared to aPOW(RR) [PP(RR): 0.43 ± 0.18; aPOW(RR): 1.06 ± 0.17; p < 0.001]. Long (> 95th percentile) and short (< 5th percentile) RR intervals were significantly more irregular in the pre-ECV phases for both PP and aPOW; however, aPOW signal suffered more fluctuations compared to PP (p < 0.001 in both phases). Present findings suggest that AF-related hemodynamic alterations are more manifest at the peripheral (aPOW) rather than at the upstream macrocirculatory level (PP). Restoring sinus rhythm increases mean peripheral microvascular perfusion and decreases variability of the microvascular hemodynamic signals. Future dedicated studies are required to determine if AF-induced peripheral microvascular alterations might relate to long-term prognostic effects.
Collapse
Affiliation(s)
- Andrea Saglietto
- Division of Cardiology, Cardiovascular and Thoracic Department, ″Citta della Salute e della Scienza″ Hospital, Turin, Italy
- Department of Medical Sciences, University of Turin, Turin, Italy
| | - Stefania Scarsoglio
- Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Corso Duca Degli Abruzzi 24, 10129, Turin, Italy.
| | - Daniela Canova
- Department of Medical Sciences, University of Turin, Turin, Italy
| | - Gaetano Maria De Ferrari
- Division of Cardiology, Cardiovascular and Thoracic Department, ″Citta della Salute e della Scienza″ Hospital, Turin, Italy
- Department of Medical Sciences, University of Turin, Turin, Italy
| | - Luca Ridolfi
- Department of Environmental, Land and Infrastructure Engineering, Politecnico di Torino, Turin, Italy
| | - Matteo Anselmino
- Division of Cardiology, Cardiovascular and Thoracic Department, ″Citta della Salute e della Scienza″ Hospital, Turin, Italy
- Department of Medical Sciences, University of Turin, Turin, Italy
| |
Collapse
|
7
|
Yan L, Wei M, Hu S, Sheng B. Photoplethysmography Driven Hypertension Identification: A Pilot Study. SENSORS (BASEL, SWITZERLAND) 2023; 23:3359. [PMID: 36992070 PMCID: PMC10056023 DOI: 10.3390/s23063359] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 03/16/2023] [Accepted: 03/20/2023] [Indexed: 06/19/2023]
Abstract
To prevent and diagnose hypertension early, there has been a growing demand to identify its states that align with patients. This pilot study aims to research how a non-invasive method using photoplethysmographic (PPG) signals works together with deep learning algorithms. A portable PPG acquisition device (Max30101 photonic sensor) was utilized to (1) capture PPG signals and (2) wirelessly transmit data sets. In contrast to traditional feature engineering machine learning classification schemes, this study preprocessed raw data and applied a deep learning algorithm (LSTM-Attention) directly to extract deeper correlations between these raw datasets. The Long Short-Term Memory (LSTM) model underlying a gate mechanism and memory unit enables it to handle long sequence data more effectively, avoiding gradient disappearance and possessing the ability to solve long-term dependencies. To enhance the correlation between distant sampling points, an attention mechanism was introduced to capture more data change features than a separate LSTM model. A protocol with 15 healthy volunteers and 15 hypertension patients was implemented to obtain these datasets. The processed result demonstrates that the proposed model could present satisfactory performance (accuracy: 0.991; precision: 0.989; recall: 0.993; F1-score: 0.991). The model we proposed also demonstrated superior performance compared to related studies. The outcome indicates the proposed method could effectively diagnose and identify hypertension; thus, a paradigm to cost-effectively screen hypertension could rapidly be established using wearable smart devices.
Collapse
Affiliation(s)
- Liangwen Yan
- School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China
| | - Mingsen Wei
- School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China
| | - Sijung Hu
- School of Electronic, Electrical and Systems Engineering, Loughborough University, Ashby Road, Loughborough, Leicestershire LE11 3TU, UK
| | - Bo Sheng
- School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China
| |
Collapse
|