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Wang J, Wang Z, Zhang Z, Li P, Pan H, Ren Y, Hou T, Wang C, Kwong CF, Zhang B, Yang S, Bie J. Simultaneous Measurement of Local Pulse Wave Velocities in Radial Arteries Using a Soft Sensor Based on the Fiber Bragg Grating Technique. MICROMACHINES 2024; 15:507. [PMID: 38675318 PMCID: PMC11052460 DOI: 10.3390/mi15040507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Revised: 03/29/2024] [Accepted: 04/02/2024] [Indexed: 04/28/2024]
Abstract
Arterial stiffness has been proved to be an important parameter in the evaluation of cardiovascular diseases, and Pulse Wave Velocity (PWV) is a strong indicator of arterial stiffness. Compared to regional PWV (PWV among different arteries), local PWV (PWV within a single artery) outstands in providing higher precision in indicating arterial properties, as regional PWVs are highly affected by multiple parameters, e.g., variations in blood vessel lengths due to individual differences, and multiple reflection effects on the pulse waveform. However, local PWV is less-developed due to its high dependency on the temporal resolution in synchronized signals with usually low signal-to-noise ratios. This paper presents a method for the noninvasive simultaneous measurement of two local PWVs in both left and right radial arteries based on the Fiber Bragg Grating (FBG) technique via correlation analysis of the pulse pairs at the fossa cubitalis and at the wrist. Based on the measurements of five male volunteers at the ages of 19 to 21 years old, the average left radial PWV ranged from 9.44 m/s to 12.35 m/s and the average right radial PWV ranged from 11.50 m/s to 14.83 m/s. What is worth mentioning is that a stable difference between the left and right radial PWVs was observed for each volunteer, ranging from 2.27 m/s to 3.04 m/s. This method enables the dynamic analysis of local PWVs and analysis of their features among different arteries, which will benefit the diagnosis of early-stage arterial stiffening and may bring more insights into the diagnosis of cardiovascular diseases.
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Affiliation(s)
- Jing Wang
- Department of Electrical and Electronic Engineering, University of Nottingham Ningbo China, Ningbo 315100, China; (Z.W.); (Z.Z.); (P.L.); (H.P.); (C.W.); (C.-F.K.); (S.Y.)
- Nottingham Ningbo China Beacons of Excellence Research and Innovation Institute, University of Nottingham Ningbo China, Ningbo 315048, China
- Key Laboratory of More Electric Aircraft Technology of Zhejiang Province, University of Nottingham Ningbo China, Ningbo 315100, China
| | - Zhukun Wang
- Department of Electrical and Electronic Engineering, University of Nottingham Ningbo China, Ningbo 315100, China; (Z.W.); (Z.Z.); (P.L.); (H.P.); (C.W.); (C.-F.K.); (S.Y.)
| | - Zijun Zhang
- Department of Electrical and Electronic Engineering, University of Nottingham Ningbo China, Ningbo 315100, China; (Z.W.); (Z.Z.); (P.L.); (H.P.); (C.W.); (C.-F.K.); (S.Y.)
| | - Peiyun Li
- Department of Electrical and Electronic Engineering, University of Nottingham Ningbo China, Ningbo 315100, China; (Z.W.); (Z.Z.); (P.L.); (H.P.); (C.W.); (C.-F.K.); (S.Y.)
| | - Han Pan
- Department of Electrical and Electronic Engineering, University of Nottingham Ningbo China, Ningbo 315100, China; (Z.W.); (Z.Z.); (P.L.); (H.P.); (C.W.); (C.-F.K.); (S.Y.)
| | - Yong Ren
- Nottingham Ningbo China Beacons of Excellence Research and Innovation Institute, University of Nottingham Ningbo China, Ningbo 315048, China
- Department of Mechanics, Materials and Manufacturing Engineering, University of Nottingham Ningbo China, Ningbo 315100, China;
- Key Laboratory of Carbonaceous Wastes Processing and Process Intensification Research of Zhejiang Province, University of Nottingham Ningbo China, Ningbo 315100, China
| | - Tuo Hou
- Department of Mechanics, Materials and Manufacturing Engineering, University of Nottingham Ningbo China, Ningbo 315100, China;
| | - Chengbo Wang
- Department of Electrical and Electronic Engineering, University of Nottingham Ningbo China, Ningbo 315100, China; (Z.W.); (Z.Z.); (P.L.); (H.P.); (C.W.); (C.-F.K.); (S.Y.)
- Nottingham Ningbo China Beacons of Excellence Research and Innovation Institute, University of Nottingham Ningbo China, Ningbo 315048, China
| | - Chiew-Foong Kwong
- Department of Electrical and Electronic Engineering, University of Nottingham Ningbo China, Ningbo 315100, China; (Z.W.); (Z.Z.); (P.L.); (H.P.); (C.W.); (C.-F.K.); (S.Y.)
- Key Laboratory of More Electric Aircraft Technology of Zhejiang Province, University of Nottingham Ningbo China, Ningbo 315100, China
| | - Bei Zhang
- Department of Automation Science and Electrical Engineering, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing 100191, China;
| | - Sen Yang
- Department of Electrical and Electronic Engineering, University of Nottingham Ningbo China, Ningbo 315100, China; (Z.W.); (Z.Z.); (P.L.); (H.P.); (C.W.); (C.-F.K.); (S.Y.)
- Nottingham Ningbo China Beacons of Excellence Research and Innovation Institute, University of Nottingham Ningbo China, Ningbo 315048, China
| | - Jing Bie
- Department of Civil Engineering, University of Nottingham Ningbo China, Ningbo 315100, China;
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Regnault V, Lacolley P, Laurent S. Arterial Stiffness: From Basic Primers to Integrative Physiology. Annu Rev Physiol 2024; 86:99-121. [PMID: 38345905 DOI: 10.1146/annurev-physiol-042022-031925] [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] [Indexed: 02/15/2024]
Abstract
The elastic properties of conductance arteries are one of the most important hemodynamic functions in the body, and data continue to emerge regarding the importance of their dysfunction in vascular aging and a range of cardiovascular diseases. Here, we provide new insight into the integrative physiology of arterial stiffening and its clinical consequence. We also comprehensively review progress made on pathways/molecules that appear today as important basic determinants of arterial stiffness, particularly those mediating the vascular smooth muscle cell (VSMC) contractility, plasticity and stiffness. We focus on membrane and nuclear mechanotransduction, clearance function of the vascular wall, phenotypic switching of VSMCs, immunoinflammatory stimuli and epigenetic mechanisms. Finally, we discuss the most important advances of the latest clinical studies that revisit the classical therapeutic concepts of arterial stiffness and lead to a patient-by-patient strategy according to cardiovascular risk exposure and underlying disease.
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Xiao H, Song W, Liu C, Peng B, Zhu M, Jiang B, Liu Z. Reconstruction of central arterial pressure waveform based on CBi-SAN network from radial pressure waveform. Artif Intell Med 2023; 145:102683. [PMID: 37925212 DOI: 10.1016/j.artmed.2023.102683] [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: 06/12/2022] [Revised: 05/30/2023] [Accepted: 10/06/2023] [Indexed: 11/06/2023]
Abstract
The central arterial pressure (CAP) is an important physiological indicator of the human cardiovascular system which represents one of the greatest threats to human health. Accurate non-invasive detection and reconstruction of CAP waveforms are crucial for the reliable treatment of cardiovascular system diseases. However, the traditional methods are reconstructed with relatively low accuracy, and some deep learning neural network models also have difficulty in extracting features, as a result, these methods have potential for further advancement. In this study, we proposed a novel model (CBi-SAN) to implement an end-to-end relationship from radial artery pressure (RAP) waveform to CAP waveform, which consisted of the convolutional neural network (CNN), the bidirectional long-short-time memory network (BiLSTM), and the self-attention mechanism to improve the performance of CAP reconstruction. The data on invasive measurements of CAP and RAP waveform were used in 62 patients before and after medication to develop and validate the performance of CBi-SAN model for reconstructing CAP waveform. We compared it with traditional methods and deep learning models in mean absolute error (MAE), root mean square error (RMSE), and Spearman correlation coefficient (SCC). Study results indicated the CBi-SAN model performed great performance on CAP waveform reconstruction (MAE: 2.23 ± 0.11 mmHg, RMSE: 2.21 ± 0.07 mmHg), concurrently, the best reconstruction effect was obtained in the central artery systolic pressure (CASP) and the central artery diastolic pressure(CADP) (RMSECASP: 2.94 ± 0.48 mmHg, RMSECADP: 1.96 ± 0.06 mmHg). These results implied the performance of the CAP reconstruction based on CBi-SAN model was superior to the existing methods, hopped to be effectively applied to clinical practice in the future.
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Affiliation(s)
- Hanguang Xiao
- College of Artificial Intelligent, Chongqing University of Technology, Chongqing 401135, China.
| | - Wangwang Song
- College of Artificial Intelligent, Chongqing University of Technology, Chongqing 401135, China
| | - Chang Liu
- College of Artificial Intelligent, Chongqing University of Technology, Chongqing 401135, China
| | - Bo Peng
- College of Artificial Intelligent, Chongqing University of Technology, Chongqing 401135, China
| | - Mi Zhu
- College of Artificial Intelligent, Chongqing University of Technology, Chongqing 401135, China
| | - Bin Jiang
- College of Artificial Intelligent, Chongqing University of Technology, Chongqing 401135, China
| | - Zhi Liu
- College of Artificial Intelligent, Chongqing University of Technology, Chongqing 401135, China.
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4
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Aghilinejad A, Amlani F, Mazandarani SP, King KS, Pahlevan NM. Mechanistic insights on age-related changes in heart-aorta-brain hemodynamic coupling using a pulse wave model of the entire circulatory system. Am J Physiol Heart Circ Physiol 2023; 325:H1193-H1209. [PMID: 37712923 PMCID: PMC10908406 DOI: 10.1152/ajpheart.00314.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 08/14/2023] [Accepted: 08/31/2023] [Indexed: 09/16/2023]
Abstract
Age-related changes in aortic biomechanics can impact the brain by reducing blood flow and increasing pulsatile energy transmission. Clinical studies have shown that impaired cardiac function in patients with heart failure is associated with cognitive impairment. Although previous studies have attempted to elucidate the complex relationship between age-associated aortic stiffening and pulsatility transmission to the cerebral network, they have not adequately addressed the effect of interactions between aortic stiffness and left ventricle (LV) contractility (neither on energy transmission nor on brain perfusion). In this study, we use a well-established and validated one-dimensional blood flow and pulse wave computational model of the circulatory system to address how age-related changes in cardiac function and vasculature affect the underlying mechanisms involved in the LV-aorta-brain hemodynamic coupling. Our results reveal how LV contractility affects pulsatile energy transmission to the brain, even with preserved cardiac output. Our model demonstrates the existence of an optimal heart rate (near the normal human heart rate) that minimizes pulsatile energy transmission to the brain at different contractility levels. Our findings further suggest that the reduction in cerebral blood flow at low levels of LV contractility is more prominent in the setting of age-related aortic stiffening. Maintaining optimal blood flow to the brain requires either an increase in contractility or an increase in heart rate. The former consistently leads to higher pulsatile power transmission, and the latter can either increase or decrease subsequent pulsatile power transmission to the brain.NEW & NOTEWORTHY We investigated the impact of major aging mechanisms of the arterial system and cardiac function on brain hemodynamics. Our findings suggest that aging has a significant impact on heart-aorta-brain coupling through changes in both arterial stiffening and left ventricle (LV) contractility. Understanding the underlying physical mechanisms involved here can potentially be a key step for developing more effective therapeutic strategies that can mitigate the contributions of abnormal LV-arterial coupling toward neurodegenerative diseases and dementia.
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Affiliation(s)
- Arian Aghilinejad
- Department of Aerospace and Mechanical Engineering, University of Southern California, Los Angeles, California, United States
| | - Faisal Amlani
- Laboratoire de Mécanique Paris-Saclay, Université Paris-Saclay, Paris, France
| | - Sohrab P Mazandarani
- Department of Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, United States
| | - Kevin S King
- Barrow Neurological Institute, Phoenix, Arizona, United States
| | - Niema M Pahlevan
- Department of Aerospace and Mechanical Engineering, University of Southern California, Los Angeles, California, United States
- Division of Cardiovascular Medicine, Department of Medicine, University of Southern California, Los Angeles, California, United States
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5
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du Toit C, Tran TQB, Deo N, Aryal S, Lip S, Sykes R, Manandhar I, Sionakidis A, Stevenson L, Pattnaik H, Alsanosi S, Kassi M, Le N, Rostron M, Nichol S, Aman A, Nawaz F, Mehta D, Tummala R, McCallum L, Reddy S, Visweswaran S, Kashyap R, Joe B, Padmanabhan S. Survey and Evaluation of Hypertension Machine Learning Research. J Am Heart Assoc 2023; 12:e027896. [PMID: 37119074 PMCID: PMC10227215 DOI: 10.1161/jaha.122.027896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Accepted: 03/27/2023] [Indexed: 04/30/2023]
Abstract
Background Machine learning (ML) is pervasive in all fields of research, from automating tasks to complex decision-making. However, applications in different specialities are variable and generally limited. Like other conditions, the number of studies employing ML in hypertension research is growing rapidly. In this study, we aimed to survey hypertension research using ML, evaluate the reporting quality, and identify barriers to ML's potential to transform hypertension care. Methods and Results The Harmonious Understanding of Machine Learning Analytics Network survey questionnaire was applied to 63 hypertension-related ML research articles published between January 2019 and September 2021. The most common research topics were blood pressure prediction (38%), hypertension (22%), cardiovascular outcomes (6%), blood pressure variability (5%), treatment response (5%), and real-time blood pressure estimation (5%). The reporting quality of the articles was variable. Only 46% of articles described the study population or derivation cohort. Most articles (81%) reported at least 1 performance measure, but only 40% presented any measures of calibration. Compliance with ethics, patient privacy, and data security regulations were mentioned in 30 (48%) of the articles. Only 14% used geographically or temporally distinct validation data sets. Algorithmic bias was not addressed in any of the articles, with only 6 of them acknowledging risk of bias. Conclusions Recent ML research on hypertension is limited to exploratory research and has significant shortcomings in reporting quality, model validation, and algorithmic bias. Our analysis identifies areas for improvement that will help pave the way for the realization of the potential of ML in hypertension and facilitate its adoption.
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Affiliation(s)
- Clea du Toit
- School of Cardiovascular and Metabolic HealthUniversity of GlasgowGlasgowUnited Kingdom
| | - Tran Quoc Bao Tran
- School of Cardiovascular and Metabolic HealthUniversity of GlasgowGlasgowUnited Kingdom
| | - Neha Deo
- Mayo Clinic Alix School of MedicineRochesterMN
| | - Sachin Aryal
- Center for Hypertension and Precision Medicine, Department of Physiology and PharmacologyUniversity of Toledo College of Medicine and Life SciencesToledoOH
| | - Stefanie Lip
- School of Cardiovascular and Metabolic HealthUniversity of GlasgowGlasgowUnited Kingdom
| | - Robert Sykes
- School of Cardiovascular and Metabolic HealthUniversity of GlasgowGlasgowUnited Kingdom
| | - Ishan Manandhar
- Center for Hypertension and Precision Medicine, Department of Physiology and PharmacologyUniversity of Toledo College of Medicine and Life SciencesToledoOH
| | | | - Leah Stevenson
- Center for Hypertension and Precision Medicine, Department of Physiology and PharmacologyUniversity of Toledo College of Medicine and Life SciencesToledoOH
| | | | - Safaa Alsanosi
- School of Cardiovascular and Metabolic HealthUniversity of GlasgowGlasgowUnited Kingdom
- Department of Pharmacology and Toxicology, Faculty of MedicineUmm Al Qura UniversityMakkahSaudi Arabia
| | - Maria Kassi
- School of Cardiovascular and Metabolic HealthUniversity of GlasgowGlasgowUnited Kingdom
| | - Ngoc Le
- School of Cardiovascular and Metabolic HealthUniversity of GlasgowGlasgowUnited Kingdom
| | - Maggie Rostron
- School of Cardiovascular and Metabolic HealthUniversity of GlasgowGlasgowUnited Kingdom
| | - Sarah Nichol
- School of Cardiovascular and Metabolic HealthUniversity of GlasgowGlasgowUnited Kingdom
| | - Alisha Aman
- School of Cardiovascular and Metabolic HealthUniversity of GlasgowGlasgowUnited Kingdom
| | - Faisal Nawaz
- College of MedicineMohammed Bin Rashid University of Medicine and Health SciencesDubaiUAE
| | - Dhruven Mehta
- Department of Internal MedicineTriStar Centennial Medical Center, HCA HealthcareNashvilleTN
| | - Ramakumar Tummala
- Center for Hypertension and Precision Medicine, Department of Physiology and PharmacologyUniversity of Toledo College of Medicine and Life SciencesToledoOH
| | - Linsay McCallum
- School of Cardiovascular and Metabolic HealthUniversity of GlasgowGlasgowUnited Kingdom
| | | | - Shyam Visweswaran
- Department of Biomedical InformaticsUniversity of PittsburghPittsburghPA
| | - Rahul Kashyap
- Department of Anesthesiology and Critical Care MedicineMayo ClinicRochesterMN
| | - Bina Joe
- Center for Hypertension and Precision Medicine, Department of Physiology and PharmacologyUniversity of Toledo College of Medicine and Life SciencesToledoOH
| | - Sandosh Padmanabhan
- School of Cardiovascular and Metabolic HealthUniversity of GlasgowGlasgowUnited Kingdom
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6
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Qin K, Huang W, Zhang T, Tang S. Machine learning and deep learning for blood pressure prediction: a methodological review from multiple perspectives. Artif Intell Rev 2022. [DOI: 10.1007/s10462-022-10353-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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7
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Ouyoung T, Weng WL, Hu TY, Lee CC, Wu LW, Hsiu H. Machine-Learning Classification of Pulse Waveform Quality. SENSORS (BASEL, SWITZERLAND) 2022; 22:8607. [PMID: 36433203 PMCID: PMC9698948 DOI: 10.3390/s22228607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 11/01/2022] [Accepted: 11/07/2022] [Indexed: 06/16/2023]
Abstract
Pulse measurements made using wearable devices can aid the monitoring of human physiological condition. Accurate estimation of waveforms is often difficult for nonexperts; motion artifacts may occur during tonometry measurements when the skin-sensor contact pressure is insufficient. An alternative approach is to extract only high-quality pulses for use in index calculations. The present study aimed to determine the effectiveness of using machine-learning analysis in discriminating between high-quality and low-quality pulse waveforms induced by applying different contact pressures. Radial blood pressure waveform (BPW) signals were measured noninvasively in healthy young subjects using a strain-gauge transducer. One-minute-long trains of pulse data were measured when applying the appropriate contact pressure (67.80 ± 1.55 mmHg) and a higher contact pressure (151.80 ± 3.19 mmHg). Eight machine-learning algorithms were employed to evaluate the following 40 harmonic pulse indices: amplitude proportions and their coefficients of variation and phase angles and their standard deviations. Significant differences were noted in BPW indices between applying appropriate and higher skin-surface contact pressures. The present appropriate contact pressure could not only provide a suitable holding force for the wearable device but also helped to maintain the physiological stability of the underlying tissues. Machine-learning analysis provides an effective method for distinguishing between the high-quality and low-quality pulses with excellent discrimination performance (leave-one-subject-out test: random-forest AUC = 0.96). This approach will aid the development of an automatic screening method for waveform quality and thereby improve the noninvasive acquisition reliability. Other possible interfering factors in practical applications can also be systematically studied using a similar procedure.
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Affiliation(s)
- Te Ouyoung
- Division of Family Medicine, Department of Family and Community Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan
- School of Medicine, National Defense Medical Center, Taipei 114, Taiwan
- Health Management Center, Department of Family and Community Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan
| | - Wan-Ling Weng
- Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan
| | - Ting-Yu Hu
- Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan
| | - Chia-Chien Lee
- Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan
| | - Li-Wei Wu
- Division of Family Medicine, Department of Family and Community Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan
- School of Medicine, National Defense Medical Center, Taipei 114, Taiwan
- Health Management Center, Department of Family and Community Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan
| | - Hsin Hsiu
- Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan
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8
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Yi Z, Liu Z, Li W, Ruan T, Chen X, Liu J, Yang B, Zhang W. Piezoelectric Dynamics of Arterial Pulse for Wearable Continuous Blood Pressure Monitoring. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2110291. [PMID: 35285098 DOI: 10.1002/adma.202110291] [Citation(s) in RCA: 45] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 02/09/2022] [Indexed: 06/14/2023]
Abstract
Piezoelectric arterial pulse wave dynamics are traditionally considered to be similar to those of typical blood pressure waves. However, achieving accurate continuous blood pressure wave monitoring based on arterial pulse waves remains challenging, because the correlation between piezoelectric pulse waves and their related blood pressure waves is unclear. To address this, the correlation between piezoelectric pulse waves and blood pressure waves is first elucidated via theoretical, simulation, and experimental analysis of these dynamics. Based on this correlation, the authors develop a wireless wearable continuous blood pressure monitoring system, with better portability than conventional systems that are based on the pulse wave velocity between multiple sensors. They explore the feasibility of achieving wearable continuous blood pressure monitoring without motion artifacts, using a single piezoelectric sensor. These findings eliminate the controversy over the arterial pulse wave piezoelectric response, and can potentially be used to develop a portable wearable continuous blood pressure monitoring device for the early prevention and daily control of hypertension.
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Affiliation(s)
- Zhiran Yi
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Zhaoxu Liu
- National Key Laboratory of Science and Technology on Micro/Nano Fabrication, Department of Micro/Nano Electronics, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Wenbo Li
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Tao Ruan
- National Key Laboratory of Science and Technology on Micro/Nano Fabrication, Department of Micro/Nano Electronics, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Xiang Chen
- National Key Laboratory of Science and Technology on Micro/Nano Fabrication, Department of Micro/Nano Electronics, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Jingquan Liu
- National Key Laboratory of Science and Technology on Micro/Nano Fabrication, Department of Micro/Nano Electronics, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Bin Yang
- National Key Laboratory of Science and Technology on Micro/Nano Fabrication, Department of Micro/Nano Electronics, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Wenming Zhang
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
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9
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Xiao H, Liu C, Zhang B. Reconstruction of central arterial pressure waveform based on CNN-BILSTM. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103513] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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10
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Wang KM, Chang TI. Blood Pressure Variability: Not to Be Discounted. Am J Hypertens 2022; 35:118-120. [PMID: 34622281 DOI: 10.1093/ajh/hpab160] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 10/04/2021] [Indexed: 12/11/2022] Open
Affiliation(s)
- Katherine M Wang
- Division of Renal Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Tara I Chang
- Division of Nephrology, Stanford University, Palo Alto, California, USA
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11
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Bikia V, Fong T, Climie RE, Bruno RM, Hametner B, Mayer C, Terentes-Printzios D, Charlton PH. Leveraging the potential of machine learning for assessing vascular ageing: state-of-the-art and future research. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2021; 2:676-690. [PMID: 35316972 PMCID: PMC7612526 DOI: 10.1093/ehjdh/ztab089] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Vascular ageing biomarkers have been found to be predictive of cardiovascular risk independently of classical risk factors, yet are not widely used in clinical practice. In this review, we present two basic approaches for using machine learning (ML) to assess vascular age: parameter estimation and risk classification. We then summarize their role in developing new techniques to assess vascular ageing quickly and accurately. We discuss the methods used to validate ML-based markers, the evidence for their clinical utility, and key directions for future research. The review is complemented by case studies of the use of ML in vascular age assessment which can be replicated using freely available data and code.
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Affiliation(s)
- Vasiliki Bikia
- Laboratory of Hemodynamics and Cardiovascular Technology (LHTC), Swiss Federal Institute of Technology, CH-1015 Lausanne, Vaud, Switzerland
| | - Terence Fong
- Baker Heart and Diabetes Institute, 75 Commercial Rd, Melbourne, Victoria, 3004 Australia,Department of Cardiometabolic Health, Melbourne Medical School, University of Melbourne, Grattan Street, Parkville, Victoria, 3010 Australia
| | - Rachel E Climie
- Baker Heart and Diabetes Institute, 75 Commercial Rd, Melbourne, Victoria, 3004 Australia,Université de Paris, INSERM U970, Paris Cardiovascular Research Centre, Integrative Epidemiology of Cardiovascular Disease, Paris, France
| | - Rosa-Maria Bruno
- Université de Paris, INSERM U970, Paris Cardiovascular Research Centre, Integrative Epidemiology of Cardiovascular Disease, Paris, France
| | - Bernhard Hametner
- Center for Health & Bioresources, AIT Austrian Institute of Technology, Giefinggasse 4, 1210 Vienna, Austria
| | - Christopher Mayer
- Center for Health & Bioresources, AIT Austrian Institute of Technology, Giefinggasse 4, 1210 Vienna, Austria
| | - Dimitrios Terentes-Printzios
- First Department of Cardiology, Hippokration Hospital, Medical School, National and Kapodistrian University of Athens, 114 Vasilissis Sofias Avenue, 11527, Athens, Greece
| | - Peter H Charlton
- Department of Public Health and Primary Care, Strangeways Research Laboratory, 2 Worts' Causeway, Cambridge, CB1 8RN, UK,Research Centre for Biomedical Engineering, City, University of London, Northampton Square, London, EC1V 0HB, UK,Corresponding author.
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12
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Bikia V, Rovas G, Pagoulatou S, Stergiopulos N. Determination of Aortic Characteristic Impedance and Total Arterial Compliance From Regional Pulse Wave Velocities Using Machine Learning: An in-silico Study. Front Bioeng Biotechnol 2021; 9:649866. [PMID: 34055758 PMCID: PMC8155726 DOI: 10.3389/fbioe.2021.649866] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Accepted: 04/08/2021] [Indexed: 01/04/2023] Open
Abstract
In-vivo assessment of aortic characteristic impedance (Z ao ) and total arterial compliance (C T ) has been hampered by the need for either invasive or inconvenient and expensive methods to access simultaneous recordings of aortic pressure and flow, wall thickness, and cross-sectional area. In contrast, regional pulse wave velocity (PWV) measurements are non-invasive and clinically available. In this study, we present a non-invasive method for estimating Z ao and C T using cuff pressure, carotid-femoral PWV (cfPWV), and carotid-radial PWV (crPWV). Regression analysis is employed for both Z ao and C T . The regressors are trained and tested using a pool of virtual subjects (n = 3,818) generated from a previously validated in-silico model. Predictions achieved an accuracy of 7.40%, r = 0.90, and 6.26%, r = 0.95, for Z ao , and C T , respectively. The proposed approach constitutes a step forward to non-invasive screening of elastic vascular properties in humans by exploiting easily obtained measurements. This study could introduce a valuable tool for assessing arterial stiffness reducing the cost and the complexity of the required measuring techniques. Further clinical studies are required to validate the method in-vivo.
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Affiliation(s)
- Vasiliki Bikia
- Laboratory of Hemodynamics and Cardiovascular Technology, Institute of Bioengineering, Swiss Federal Institute of Technology, Lausanne, Switzerland
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13
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Civilla L, Sbrollini A, Burattini L, Morettini M. An integrated lumped-parameter model of the cardiovascular system for the simulation of acute ischemic stroke: description of instantaneous changes in hemodynamics. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:3993-4010. [PMID: 34198422 DOI: 10.3934/mbe.2021200] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Acute Ischemic Stroke (AIS) is defined as the acute condition of occlusion of a cerebral artery and is often caused by a Hypertensive Condition (HC). Due to its sudden occurrence, AIS is not observable the right moment it occurs, thus information about instantaneous changes in hemodynamics is limited. This study aimed to propose an integrated Lumped Parameter (LP) model of the cardiovascular system to simulate an AIS and describe instantaneous changes in hemodynamics. In the integrated LP model of the cardiovascular system, heart chambers have been modelled with elastance systems with controlled pressure inputs; heart valves have been modelled with static open/closed pressure-controlled valves; eventually, the vasculature has been modelled with resistor-inductor-capacitor (RLC) direct circuits and have been linked to the rest of the system through a series connection. After simulating physiological conditions, HC has been simulated by changing pressure inputs and constant RLC parameters. Then, AIS occurring in arteries of different sizes have been simulated by considering time-dependent RLC parameters due to the elimination from the model of the occluding artery; instantaneous changes in hemodynamics have been evaluated by Systemic Arteriolar Flow (Qa) and Systemic Arteriolar Pressure (Pa) drop with respect to those measured in HC. Occlusion of arteries of different sizes leaded to an average Qa drop of 0.38 ml/s per cardiac cycle (with minimum and maximum values of 0.04 ml/s and 1.93 ml/s) and average Pa drop of 0.39 mmHg, (with minimum and maximum values of 0.04 mmHg and 1.98 mmHg). In conclusion, hemodynamic variations due to AIS are very small with respect to HC. A direct relation between the inverse of the length of the artery in which the occlusion occurs and the hemodynamic variations has been highlighted; this may allow to link the severity of AIS to the length of the interested artery.
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Affiliation(s)
- Lorenzo Civilla
- Department of Information Engineering, UniversitȤ Politecnica delle Marche, Ancona 60131, Italy
| | - Agnese Sbrollini
- Department of Information Engineering, UniversitȤ Politecnica delle Marche, Ancona 60131, Italy
| | - Laura Burattini
- Department of Information Engineering, UniversitȤ Politecnica delle Marche, Ancona 60131, Italy
| | - Micaela Morettini
- Department of Information Engineering, UniversitȤ Politecnica delle Marche, Ancona 60131, Italy
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14
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Lin SK, Hsiu H, Chen HS, Yang CJ. Classification of patients with Alzheimer's disease using the arterial pulse spectrum and a multilayer-perceptron analysis. Sci Rep 2021; 11:8882. [PMID: 33903610 PMCID: PMC8076260 DOI: 10.1038/s41598-021-87903-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2020] [Accepted: 03/23/2021] [Indexed: 11/16/2022] Open
Abstract
Cerebrovascular atherosclerosis has been identified as a prominent pathological feature of Alzheimer's disease (AD); the link between vessel pathology and AD risk may also extend to extracranial arteries. This study aimed to determine the effectiveness of using arterial pulse-wave measurements and multilayer perceptron (MLP) analysis in distinguishing between AD and control subjects. Radial blood pressure waveform (BPW) and finger photoplethysmography signals were measured noninvasively for 3 min in 87 AD patients and 74 control subjects. The 5-layer MLP algorithm employed evaluated the following 40 harmonic pulse indices: amplitude proportion and its coefficient of variation, and phase angle and its standard deviation. The BPW indices differed significantly between the AD patients (6247 pulses) and control subjects (6626 pulses). Significant intergroup differences were found between mild, moderate, and severe AD (defined by Mini-Mental-State-Examination scores). The hold-out test results indicated an accuracy of 82.86%, a specificity of 92.31%, and a 0.83 AUC of ROC curve when using the MLP-based classification between AD and Control. The identified differences can be partly attributed to AD-induced changes in vascular elastic properties. The present findings may be meaningful in facilitating the development of a noninvasive, rapid, inexpensive, and objective method for detecting and monitoring the AD status.
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Affiliation(s)
- Shun-Ku Lin
- Institute of Public Health, National Yang-Ming University, Taipei, Taiwan
- Department of Chinese Medicine, Taipei City Hospital, Renai Branch, Taipei, Taiwan
- General Education Center, University of Taipei, Taipei, Taiwan
| | - Hsin Hsiu
- Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, No. 43, Section 4, Keelung Road, Taipei, 10607, Taiwan.
- Biomedical Engineering Research Center, National Defense Medical Center, Taipei, Taiwan.
| | - Hsi-Sheng Chen
- Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, No. 43, Section 4, Keelung Road, Taipei, 10607, Taiwan
| | - Chang-Jen Yang
- Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, No. 43, Section 4, Keelung Road, Taipei, 10607, Taiwan
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15
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Attarpour A, Ward J, Chen JJ. Vascular origins of low-frequency oscillations in the cerebrospinal fluid signal in resting-state fMRI: Interpretation using photoplethysmography. Hum Brain Mapp 2021; 42:2606-2622. [PMID: 33638224 PMCID: PMC8090775 DOI: 10.1002/hbm.25392] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 02/09/2021] [Accepted: 02/16/2021] [Indexed: 12/12/2022] Open
Abstract
In vivo mapping of cerebrovascular oscillations in the 0.05–0.15 Hz remains difficult. Oscillations in the cerebrospinal fluid (CSF) represent a possible avenue for noninvasively tracking these oscillations using resting‐state functional MRI (rs‐fMRI), and have been used to correct for vascular oscillations in rs‐fMRI functional connectivity. However, the relationship between low‐frequency CSF and vascular oscillations remains unclear. In this study, we investigate this relationship using fast simultaneous rs‐fMRI and photoplethysmogram (PPG), examining the 0.1 Hz PPG signal, heart‐rate variability (HRV), pulse‐intensity ratio (PIR), and the second derivative of the PPG (SDPPG). The main findings of this study are: (a) signals in different CSF regions are not equivalent in their associations with vascular and tissue rs‐fMRI signals; (b) the PPG signal is maximally coherent with the arterial and CSF signals at the cardiac frequency, but coherent with brain tissue at ~0.2 Hz; (c) PIR is maximally coherent with the CSF signal near 0.03 Hz; and (d) PPG‐related vascular oscillations only contribute to ~15% of the CSF (and arterial) signal in rs‐fMRI. These findings caution against averaging all CSF regions when extracting physiological nuisance regressors in rs‐fMRI applications, and indicate the drivers of the CSF signal are more than simply cardiac. Our study is an initial attempt at the refinement and standardization of how the CSF signal in rs‐fMRI can be used and interpreted. It also paves the way for using rs‐fMRI in the CSF as a potential tool for tracking cerebrovascular health through, for instance, the potential relationship between PIR and the CSF signal.
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Affiliation(s)
- Ahmadreza Attarpour
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - James Ward
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Ontario, Canada
| | - J Jean Chen
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.,Rotman Research Institute, Baycrest Health Sciences, Toronto, Ontario, Canada
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16
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Mucha W. Comparison of Machine Learning Algorithms for Structure State Prediction in Operational Load Monitoring. SENSORS (BASEL, SWITZERLAND) 2020; 20:s20247087. [PMID: 33321996 PMCID: PMC7763833 DOI: 10.3390/s20247087] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Revised: 12/08/2020] [Accepted: 12/09/2020] [Indexed: 06/12/2023]
Abstract
The aim of operational load monitoring is to make predictions about the remaining usability time of structures, which is extremely useful in aerospace industry where in-service life of aircraft structural components can be maximized, taking into account safety. In order to make such predictions, strain sensors are mounted to the structure, from which data are acquired during operational time. This allows to determine how many load cycles has the structure withstood so far. Continuous monitoring of the strain distribution of the whole structure can be complicated due to vicissitude nature of the loads. Sensors should be mounted in places where stress and strain accumulations occur, and due to experiencing variable loads, the number of required sensors may be high. In this work, different machine learning and artificial intelligence algorithms are implemented to predict the current safety factor of the structure in its most stressed point, based on relatively low number of strain measurements. Adaptive neuro-fuzzy inference systems (ANFIS), support-vector machines (SVM) and Gaussian processes for machine learning (GPML) are trained with simulation data, and their effectiveness is measured using data obtained from experiments. The proposed methods are compared to the earlier work where artificial neural networks (ANN) were proven to be efficiently used for reduction of the number of sensors in operational load monitoring processes. A numerical comparison of accuracy and computational time (taking into account possible real-time applications) between all considered methods is provided.
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Affiliation(s)
- Waldemar Mucha
- Department of Computational Mechanics and Engineering, Silesian University of Technology, 44-100 Gliwice, Poland
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17
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Noninvasive estimation of aortic hemodynamics and cardiac contractility using machine learning. Sci Rep 2020; 10:15015. [PMID: 32929108 PMCID: PMC7490416 DOI: 10.1038/s41598-020-72147-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Accepted: 08/13/2020] [Indexed: 02/07/2023] Open
Abstract
Cardiac and aortic characteristics are crucial for cardiovascular disease detection. However, noninvasive estimation of aortic hemodynamics and cardiac contractility is still challenging. This paper investigated the potential of estimating aortic systolic pressure (aSBP), cardiac output (CO), and end-systolic elastance (Ees) from cuff-pressure and pulse wave velocity (PWV) using regression analysis. The importance of incorporating ejection fraction (EF) as additional input for estimating Ees was also assessed. The models, including Random Forest, Support Vector Regressor, Ridge, Gradient Boosting, were trained/validated using synthetic data (n = 4,018) from an in-silico model. When cuff-pressure and PWV were used as inputs, the normalized-RMSEs/correlations for aSBP, CO, and Ees (best-performing models) were 3.36 ± 0.74%/0.99, 7.60 ± 0.68%/0.96, and 16.96 ± 0.64%/0.37, respectively. Using EF as additional input for estimating Ees significantly improved the predictions (7.00 ± 0.78%/0.92). Results showed that the use of noninvasive pressure measurements allows estimating aSBP and CO with acceptable accuracy. In contrast, Ees cannot be predicted from pressure signals alone. Addition of the EF information greatly improves the estimated Ees. Accuracy of the model-derived aSBP compared to in-vivo aSBP (n = 783) was very satisfactory (5.26 ± 2.30%/0.97). Future in-vivo evaluation of CO and Ees estimations remains to be conducted. This novel methodology has potential to improve the noninvasive monitoring of aortic hemodynamics and cardiac contractility.
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18
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Baiz AA, Ahmadi H, Shariatmadari F, Karimi Torshizi MA. A Gaussian process regression model to predict energy contents of corn for poultry. Poult Sci 2020; 99:5838-5843. [PMID: 33142501 PMCID: PMC7647822 DOI: 10.1016/j.psj.2020.07.044] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2020] [Revised: 06/21/2020] [Accepted: 07/17/2020] [Indexed: 11/28/2022] Open
Abstract
The present study proposes a Gaussian process regression (GPR) approach to develop a model to predict true metabolizable energy corrected for nitrogen (TMEn) content of corn samples (as model output) for poultry given levels of feed chemical compositions of crude protein, ether extract, crude fiber, and ash (as model inputs). A 30 corn samples obtained from 5 origins [Brazil (n = 9), China (n = 5), Iran (n = 7), and Ukraine (n = 9)] were assayed to determine chemical composition and TMEn content using chemical analyses and bioassay technique. In addition to GPR model, data were also analyzed by multiple linear regression (MLR) model. Results revealed that corn samples of different origins differ in their gross energy and chemical composition of crude protein, crude fiber, and ash, but no differences were observed for their ether extract and TMEn contents. Based on model evaluation criteria of R2 and root mean square error (RMSE), the GPR model showed satisfactory performance (R2 = 0.92 and RMSE = 33.68 kcal/kg DM) in predicting TMEn and produced relatively better prediction values than those produce by MLR (R2 = 0.23 and RMSE = 104.85 kcal/kg DM). The GPR model may be capable of improving our aptitude and capacity to precisely predict energy contents of feed ingredients to formulate optimal diets for poultry.
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Affiliation(s)
- Abbas Abdullah Baiz
- Department of Poultry Science, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran
| | - Hamed Ahmadi
- Department of Poultry Science, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran; Bioscience and Agriculture Modeling Research Unit, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran.
| | - Farid Shariatmadari
- Department of Poultry Science, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran
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19
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Pandit JA, Lores E, Batlle D. Cuffless Blood Pressure Monitoring: Promises and Challenges. Clin J Am Soc Nephrol 2020; 15:1531-1538. [PMID: 32680913 PMCID: PMC7536750 DOI: 10.2215/cjn.03680320] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Current BP measurements are on the basis of traditional BP cuff approaches. Ambulatory BP monitoring, at 15- to 30-minute intervals usually over 24 hours, provides sufficiently continuous readings that are superior to the office-based snapshot, but this system is not suitable for frequent repeated use. A true continuous BP measurement that could collect BP passively and frequently would require a cuffless method that could be worn by the patient, with the data stored electronically much the same way that heart rate and heart rhythm are already done routinely. Ideally, BP should be measured continuously and frequently during diverse activities during both daytime and nighttime in the same subject by means of novel devices. There is increasing excitement for newer methods to measure BP on the basis of sensors and algorithm development. As new devices are refined and their accuracy is improved, it will be possible to better assess masked hypertension, nocturnal hypertension, and the severity and variability of BP. In this review, we discuss the progression in the field, particularly in the last 5 years, ending with sensor-based approaches that incorporate machine learning algorithms to personalized medicine.
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Affiliation(s)
- Jay A Pandit
- Division of Cardiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Enrique Lores
- Division of Nephrology and Hypertension, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Daniel Batlle
- Division of Nephrology and Hypertension, Northwestern University Feinberg School of Medicine, Chicago, Illinois
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20
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Huttunen JMJ, Kärkkäinen L, Honkala M, Lindholm H. Deep learning for prediction of cardiac indices from photoplethysmographic waveform: A virtual database approach. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2020; 36:e3303. [PMID: 31886948 DOI: 10.1002/cnm.3303] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Revised: 11/28/2019] [Accepted: 12/25/2019] [Indexed: 06/10/2023]
Abstract
Deep learning methods combined with large datasets have recently shown significant progress in solving several medical tasks. However, collecting and annotating large datasets can be a very cumbersome and expensive task. We tackle these problems with a virtual database approach where training data is generated using computer simulations of related phenomena. Specifically, we concentrate on the following problem: can cardiovascular indices such as aortic elasticity, diastolic and systolic blood pressures, and blood flow from heart be predicted continuously using wearable photoplethysmographic sensors? We simulate the blood flow using a haemodynamic model consisting of the entire human circulation. Repeated evaluation of the simulator allows us to create a database of "virtual subjects" with size that is only limited by available computational resources. Using this database, we train neural networks to predict the cardiac indices from photoplethysmographic signal waveform. We consider two approaches: neural networks based on predefined input features and deep convolutional neural networks taking waveform directly as the input. The performance of the methods is demonstrated using numerical examples, thus carrying out a preliminary assessment of the approaches. The results show improvements in accuracy compared with the previous methods. The improvements are especially significant with indices related to aortic elasticity and maximum blood flow. The proposed approach would provide new means to measure cardiovascular health continuously, for example, with a simple wrist device.
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Affiliation(s)
- Janne M J Huttunen
- Algorithms, Analytics & Augmented Intelligence Research, Nokia Bell Laboratories, Espoo, Finland
| | - Leo Kärkkäinen
- Algorithms, Analytics & Augmented Intelligence Research, Nokia Bell Laboratories, Espoo, Finland
- Department of Electrical Engineering and Automation, Aalto University, Espoo, Finland
| | - Mikko Honkala
- Algorithms, Analytics & Augmented Intelligence Research, Nokia Bell Laboratories, Espoo, Finland
| | - Harri Lindholm
- Algorithms, Analytics & Augmented Intelligence Research, Nokia Bell Laboratories, Espoo, Finland
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21
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Artificial intelligence, machine learning, vascular surgery, automatic image processing. Implications for clinical practice. ANGIOLOGIA 2020. [DOI: 10.20960/angiologia.00177] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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