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Chen Y, Yang X, Song R, Liu X, Zhang J. Predicting Arterial Stiffness From Single-Channel Photoplethysmography Signal: A Feature Interaction-Based Approach. IEEE J Biomed Health Inform 2024; 28:3928-3941. [PMID: 38551821 DOI: 10.1109/jbhi.2024.3383234] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/03/2024]
Abstract
Arterial stiffness (AS) serves as a crucial indicator of arterial elasticity and function, typically requiring expensive equipment for detection. Given the strong correlation between AS and various photoplethysmography (PPG) features, PPG emerges as a convenient method for assessing AS. However, the limitations of independent PPG features hinder detection accuracy. This study introduces a feature selection method leveraging the interactive relationships between features to enhance the accuracy of predicting AS from a single-channel PPG signal. Initially, an adaptive signal interception method was employed to capture high-quality signal fragments from PPG sequences. 58 PPG features, deemed to have potential contributions to AS estimation, were extracted and analyzed. Subsequently, the interaction factor (IF) was introduced to redefine the interaction and redundancy between features. A feature selection algorithm (IFFS) based on the IF was then proposed, resulting in a combination of interactive features. Finally, the Xgboost model is utilized to estimate AS from the selected features set. The proposed approach is evaluated on datasets of 268 male and 124 female subjects, respectively. The results of AS estimation indicate that IFFS yields interacting features from numerous sources, rejects redundant ones, and enhances the association. The interaction features combined with the Xgboost model resulted in an MAE of 122.42 and 142.12 cm/sec, an SDE of 88.16 and 102.56 cm/sec, and a PCC of 0.88 and 0.85 for the male and female groups, respectively. The findings of this study suggest that the stated method improves the accuracy of predicting AS from single-channel PPG, which can be used as a non-invasive and cost-effective screening tool for atherosclerosis.
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Hellqvist H, Karlsson M, Hoffman J, Kahan T, Spaak J. Estimation of aortic stiffness by finger photoplethysmography using enhanced pulse wave analysis and machine learning. Front Cardiovasc Med 2024; 11:1350726. [PMID: 38529332 PMCID: PMC10961400 DOI: 10.3389/fcvm.2024.1350726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 02/16/2024] [Indexed: 03/27/2024] Open
Abstract
Introduction Aortic stiffness plays a critical role in the evolution of cardiovascular diseases, but the assessment requires specialized equipment. Photoplethysmography (PPG) and single-lead electrocardiogram (ECG) are readily available in healthcare and wearable devices. We studied whether a brief PPG registration, alone or in combination with single-lead ECG, could be used to reliably estimate aortic stiffness. Methods A proof-of-concept study with simultaneous high-resolution index finger recordings of infrared PPG, single-lead ECG, and finger blood pressure (Finapres) was performed in 33 participants [median age 44 (range 21-66) years, 19 men] and repeated within 2 weeks. Carotid-femoral pulse wave velocity (cfPWV; two-site tonometry with SphygmoCor) was used as a reference. A brachial single-cuff oscillometric device assessed aortic pulse wave velocity (aoPWV; Arteriograph) for further comparisons. We extracted 136 established PPG waveform features and engineered 13 new with improved coupling to the finger blood pressure curve. Height-normalized pulse arrival time (NPAT) was derived using ECG. Machine learning methods were used to develop prediction models. Results The best PPG-based models predicted cfPWV and aoPWV well (root-mean-square errors of 0.70 and 0.52 m/s, respectively), with minor improvements by adding NPAT. Repeatability and agreement were on par with the reference equipment. A new PPG feature, an amplitude ratio from the early phase of the waveform, was most important in modelling, showing strong correlations with cfPWV and aoPWV (r = -0.81 and -0.75, respectively, both P < 0.001). Conclusion Using new features and machine learning methods, a brief finger PPG registration can estimate aortic stiffness without requiring additional information on age, anthropometry, or blood pressure. Repeatability and agreement were comparable to those obtained using non-invasive reference equipment. Provided further validation, this readily available simple method could improve cardiovascular risk evaluation, treatment, and prognosis.
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Affiliation(s)
- Henrik Hellqvist
- Division of Cardiovascular Medicine, Department of Clinical Sciences, Danderyd Hospital, Karolinska Institutet, Stockholm, Sweden
| | - Mikael Karlsson
- Marcus Wallenberg Laboratory for Sound and Vibration Research, Department of Engineering Mechanics, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Johan Hoffman
- Division of Computational Science and Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Thomas Kahan
- Division of Cardiovascular Medicine, Department of Clinical Sciences, Danderyd Hospital, Karolinska Institutet, Stockholm, Sweden
| | - Jonas Spaak
- Division of Cardiovascular Medicine, Department of Clinical Sciences, Danderyd Hospital, Karolinska Institutet, Stockholm, Sweden
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Shi Y, Wu LD, Feng XH, Kan JY, Kong CH, Ling ZY, Zhang JX, Chen SL. Estimated Pulse Wave Velocity Predicts All-Cause and Cardiovascular-Cause Mortality in Individuals With Hypertension - Findings From a National Health and Nutrition Examination Study 1999-2018. Circ J 2024; 88:417-424. [PMID: 38267051 DOI: 10.1253/circj.cj-23-0674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/26/2024]
Abstract
BACKGROUND This study aimed to investigate the association between estimated pulse wave velocity (ePWV) and mortality outcomes among individuals with hypertension. METHODS AND RESULTS Based on the National Health and Nutrition Examination Survey (NHANES) 1999-2018, a total of 14,396 eligible participants with hypertension were enrolled. The ePWV was calculated using the equation based on blood pressure and age. The mortality outcomes of included participants were directly acquired from the National Death Index database. The multivariable Cox regression analysis was used to examine the relationship between ePWV and mortality outcomes. Moreover, the restricted cubic spline (RCS) was also used to explore this relationship. Receiver operating characteristics curves (ROC) were adopted to evaluate the prognostic ability of ePWV for predicting mortality outcomes of patients with hypertension. The median follow-up duration was 10.8 years; individuals with higher an ePWV had higher risks of mortality from both all causes (HR: 2.79, 95% CI: 2.43-3.20) and cardiovascular diseases (HR: 3.41, 95% CI: 2.50-4.64). After adjusting for confounding factors, each 1 m/s increase in ePWV was associated with a 43% increase in all-cause mortality risk (HR: 1.43, 95% CI: 1.37-1.48) and a 54% increase in cardiovascular mortality risk (HR: 1.54, 95% CI: 1.43-1.66). CONCLUSIONS This study indicates that ePWV is a novel prognostic indicator for predicting the risks of mortality among patients with hypertension.
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Affiliation(s)
- Yi Shi
- Department of Cardiology, Nanjing First Hospital, Nanjing Medical University
| | - Li-Da Wu
- Department of Cardiology, Nanjing First Hospital, Nanjing Medical University
| | - Xiao-Hua Feng
- Department of Geriatrics, The First Affiliated Hospital of Nanjing Medical University
| | - Jun-Yan Kan
- Department of Cardiology, Nanjing First Hospital, Nanjing Medical University
| | - Chao-Hua Kong
- Department of Cardiology, Nanjing First Hospital, Nanjing Medical University
| | - Zhi-Yu Ling
- Department of Cardiology, The Second Affiliated Hospital of Chongqing Medical University
| | - Jun-Xia Zhang
- Department of Cardiology, Nanjing First Hospital, Nanjing Medical University
| | - Shao-Liang Chen
- Department of Cardiology, Nanjing First Hospital, Nanjing Medical University
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Wang R, Zhu J, Meng Y, Wang X, Chen R, Wang K, Li C, Shi J. Adaptive machine learning method for photoacoustic computed tomography based on sparse array sensor data. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 242:107822. [PMID: 37832425 DOI: 10.1016/j.cmpb.2023.107822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 08/18/2023] [Accepted: 09/17/2023] [Indexed: 10/15/2023]
Abstract
BACKGROUND AND OBJECTIVE Photoacoustic computed tomography (PACT) is a non-invasive biomedical imaging technology that has developed rapidly in recent decades, especially has shown potential for small animal studies and early diagnosis of human diseases. To obtain high-quality images, the photoacoustic imaging system needs a high-element-density detector array. However, in practical applications, due to the cost limitation, manufacturing technology, and the system requirement in miniaturization and robustness, it is challenging to achieve sufficient elements and high-quality reconstructed images, which may even suffer from artifacts. Different from the latest machine learning methods based on removing distortions and artifacts to recover high-quality images, this paper proposes an adaptive machine learning method to firstly predict and complement the photoacoustic sensor channel data from sparse array sampling and then reconstruct images through conventional reconstruction algorithms. METHODS We develop an adaptive machine learning method to predict and complement the photoacoustic sensor channel data. The model consists of XGBoost and a neural network named SS-net. To handle data sets of different sizes and improve the generalization, a tunable parameter is used to control the weights of XGBoost and SS-net outputs. RESULTS The proposed method achieved superior performance as demonstrated by simulation, phantom experiments, and in vivo experiment results. Compared with linear interpolation, XGBoost, CAE, and U-net, the simulation results show that the SSIM value is increased by 12.83%, 6.78%, 21.46%, and 12.33%. Moreover, the median R2 is increased by 34.4%, 8.1%, 28.6%, and 84.1% with the in vivo data. CONCLUSIONS This model provides a framework to predict the missed photoacoustic sensor data on a sparse ring-shaped array for PACT imaging and has achieved considerable improvements in reconstructing the objects. Compared with linear interpolation and other deep learning methods qualitatively and quantitatively, our proposed methods can effectively suppress artifacts and improve image quality. The advantage of our methods is that there is no need for preparing a large number of images as the training dataset, and the data for training is directly from the sensors. It has the potential to be applied to a wide range of photoacoustic imaging detector arrays for low-cost and user-friendly clinical applications.
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Affiliation(s)
| | - Jing Zhu
- Zhejiang Lab, Hangzhou 311100, China
| | | | | | | | | | - Chiye Li
- Zhejiang Lab, Hangzhou 311100, China; Research Center for Humanoid Sensing, Zhejiang Lab, Hangzhou 311100, China.
| | - Junhui Shi
- Zhejiang Lab, Hangzhou 311100, China; Research Center for Humanoid Sensing, Zhejiang Lab, Hangzhou 311100, China.
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Cong D, Zhao Y, Zhang W, Li J, Bai Y. Applying machine learning algorithms to develop a survival prediction model for lung adenocarcinoma based on genes related to fatty acid metabolism. Front Pharmacol 2023; 14:1260742. [PMID: 37920207 PMCID: PMC10619909 DOI: 10.3389/fphar.2023.1260742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 10/02/2023] [Indexed: 11/04/2023] Open
Abstract
Background: The progression of lung adenocarcinoma (LUAD) may be related to abnormal fatty acid metabolism (FAM). The present study investigated the relationship between FAM-related genes and LUAD prognosis. Methods: LUAD samples from The Cancer Genome Atlas were collected. The scores of FAM-associated pathways from the Kyoto Encyclopedia of Genes and Genomes website were calculated using the single sample gene set enrichment analysis. ConsensusClusterPlus and cumulative distribution function were used to classify molecular subtypes for LUAD. Key genes were obtained using limma package, Cox regression analysis, and six machine learning algorithms (GBM, LASSO, XGBoost, SVM, random forest, and decision trees), and a RiskScore model was established. According to the RiskScore model and clinical features, a nomogram was developed and evaluated for its prediction performance using a calibration curve. Differences in immune abnormalities among patients with different subtypes and RiskScores were analyzed by the Estimation of STromal and Immune cells in MAlignant Tumours using Expression data, CIBERSORT, and single sample gene set enrichment analysis. Patients' drug sensitivity was predicted by the pRRophetic package in R language. Results: LUAD samples had lower scores of FAM-related pathways. Three molecular subtypes (C1, C2, and C3) were defined. Analysis on differential prognosis showed that the C1 subtype had the most favorable prognosis, followed by the C2 subtype, and the C3 subtype had the worst prognosis. The C3 subtype had lower immune infiltration. A total of 12 key genes (SLC2A1, PKP2, FAM83A, TCN1, MS4A1, CLIC6, UBE2S, RRM2, CDC45, IGF2BP1, ANGPTL4, and CD109) were screened and used to develop a RiskScore model. Survival chance of patients in the high-RiskScore group was significantly lower. The low-RiskScore group showed higher immune score and higher expression of most immune checkpoint genes. Patients with a high RiskScore were more likely to benefit from the six anticancer drugs we screened in this study. Conclusion: We developed a RiskScore model using FAM-related genes to help predict LUAD prognosis and develop new targeted drugs.
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Affiliation(s)
- Dan Cong
- Department of Oncology and Hematology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Yanan Zhao
- Department of Oncology and Hematology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Wenlong Zhang
- Department of Oncology and Hematology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Jun Li
- Department of Oncology and Hematology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Yuansong Bai
- Department of Oncology and Hematology, China-Japan Union Hospital of Jilin University, Changchun, China
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Chu M, Zhou Y, Yin Y, Jin L, Chen H, Meng T, He B, Wu J, Ye M. Construction and validation of a risk prediction model for aromatase inhibitor-associated bone loss. Front Oncol 2023; 13:1182792. [PMID: 37182163 PMCID: PMC10174287 DOI: 10.3389/fonc.2023.1182792] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 04/11/2023] [Indexed: 05/16/2023] Open
Abstract
Purpose To establish a high-risk prediction model for aromatase inhibitor-associated bone loss (AIBL) in patients with hormone receptor-positive breast cancer. Methods The study included breast cancer patients who received aromatase inhibitor (AI) treatment. Univariate analysis was performed to identify risk factors associated with AIBL. The dataset was randomly divided into a training set (70%) and a test set (30%). The identified risk factors were used to construct a prediction model using the eXtreme gradient boosting (XGBoost) machine learning method. Logistic regression and least absolute shrinkage and selection operator (LASSO) regression methods were used for comparison. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of the model in the test dataset. Results A total of 113 subjects were included in the study. Duration of breast cancer, duration of aromatase inhibitor therapy, hip fracture index, major osteoporotic fracture index, prolactin (PRL), and osteocalcin (OC) were found to be independent risk factors for AIBL (p < 0.05). The XGBoost model had a higher AUC compared to the logistic model and LASSO model (0.761 vs. 0.716, 0.691). Conclusion The XGBoost model outperformed the logistic and LASSO models in predicting the occurrence of AIBL in patients with hormone receptor-positive breast cancer receiving aromatase inhibitors.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Meina Ye
- *Correspondence: Jingjing Wu, ; Meina Ye,
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Vargas JM, Bahloul MA, Laleg-Kirati TM. A learning-based image processing approach for pulse wave velocity estimation using spectrogram from peripheral pulse wave signals: An in silico study. Front Physiol 2023; 14:1100570. [PMID: 36935738 PMCID: PMC10020726 DOI: 10.3389/fphys.2023.1100570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 01/31/2023] [Indexed: 03/06/2023] Open
Abstract
Carotid-to-femoral pulse wave velocity (cf-PWV) is considered a critical index to evaluate arterial stiffness. For this reason, estimating Carotid-to-femoral pulse wave velocity (cf-PWV) is essential for diagnosing and analyzing different cardiovascular diseases. Despite its broader adoption in the clinical routine, the measurement process of carotid-to-femoral pulse wave velocity is considered a demanding task for clinicians and patients making it prone to inaccuracies and errors in the estimation. A smart non-invasive, and peripheral measurement of carotid-to-femoral pulse wave velocity could overcome the challenges of the classical assessment process and improve the quality of patient care. This paper proposes a novel methodology for the carotid-to-femoral pulse wave velocity estimation based on the use of the spectrogram representation from single non-invasive peripheral pulse wave signals [photoplethysmography (PPG) or blood pressure (BP)]. This methodology was tested using three feature extraction methods based on the semi-classical signal analysis (SCSA) method, the Law's mask for texture energy extraction, and the central statistical moments. Finally, each feature method was fed into different machine learning models for the carotid-to-femoral pulse wave velocity estimation. The proposed methodology obtained an $R2\geq0.90$ for all the peripheral signals for the noise-free case using the MLP model, and for the different noise levels added to the original signal, the SCSA-based features with the MLP model presented an $R2\geq0.91$ for all the peripheral signals at the level of noise. These results provide evidence of the capacity of spectrogram representation for efficiently assessing the carotid-to-femoral pulse wave velocity estimation using different feature methods. Future work will be done toward testing the proposed methodology for in-vivo signals.
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Affiliation(s)
- Juan M. Vargas
- Computer, Electrical, and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology (KAUST), Makkah, Saudi Arabia
| | - Mohamed A. Bahloul
- Electrical Engineering Department, Alfaisal University, Riyadh, Saudi Arabia
| | - Taous-Meriem Laleg-Kirati
- Computer, Electrical, and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology (KAUST), Makkah, Saudi Arabia
- National Institute for Research in Digital Science and Technology INRIA, Saclay, France
- *Correspondence: Taous-Meriem Laleg-Kirati,
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