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Pankaj, Kumar A, Kumar M, Komaragiri R. Optimized deep neural network models for blood pressure classification using Fourier analysis-based time-frequency spectrogram of photoplethysmography signal. Biomed Eng Lett 2023; 13:739-750. [PMID: 37872982 PMCID: PMC10590347 DOI: 10.1007/s13534-023-00296-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 05/28/2023] [Accepted: 06/09/2023] [Indexed: 10/25/2023] Open
Abstract
Appropriate blood pressure (BP) management through continuous monitoring and rapid diagnosis helps to take preventive care against cardiovascular diseases (CVD). As hypertension is one of the leading causes of CVDs, keeping hypertension under control by a timely screening of subjects becomes lifesaving. This work proposes estimating BP from motion artifact-affected photoplethysmography signals (PPG) by applying signal processing techniques in realtime. This paper proposes a deep neural network-based methodology to accurately classify PPG signals using a Fourier theory-based time-frequency (TF) spectrogram. This work uses the Fourier decomposition method (FDM) to transform a PPG signal into a TF spectrogram. In the proposed work, the last three layers of the pre-trained deep neural network, namely, GoogleNet, DenseNet, and AlexNet, are modified and then used to classify the PPG signal into normotension, pre-hypertension, and hypertension. The proposed framework is trained and tested using the MIMIC-III and PPG-BP databases using five-fold training and testing. Out of the three deep neural networks, the proposed framework with the DenseNet-201 network performs best, with a test accuracy of 96.5%. The proposed work uses FDM to compute the TF spectrogram to accurately separate the motion artifacts and noise components from a noise-corrupted PPG signal. Capturing more frequency components that contain more information from PPG signals makes the deep neural networks extract better and more meaningful features. Thus, training a deep neural network model with clean PPG signal features improves the generalized capability of a BP classification model when tested in realtime.
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Affiliation(s)
- Pankaj
- Department of Electronics and Communication Engineering, Bennett University, Greater Noida, India
| | - Ashish Kumar
- School of Electronics Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu India
| | - Manjeet Kumar
- Department of Electronics and Communication Engineering, Delhi Technological University, Delhi, India
| | - Rama Komaragiri
- Department of Electronics and Communication Engineering, Bennett University, Greater Noida, India
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Pankaj, Kumar A, Komaragiri R, Kumar M. A novel CS-NET architecture based on the unification of CNN, SVM and super-resolution spectrogram to monitor and classify blood pressure using photoplethysmography. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 240:107716. [PMID: 37542944 DOI: 10.1016/j.cmpb.2023.107716] [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: 02/06/2023] [Revised: 06/20/2023] [Accepted: 07/07/2023] [Indexed: 08/07/2023]
Abstract
CONTEXT Continuous blood pressure (BP) monitoring plays an important role while treating various cardiovascular diseases and hypertension. A high correlation between arterial blood pressure (ABP) and Photoplethysmogram (PPG) signal enables using a PPG signal to monitor and classify BP continuously. Control of BP in realtime is the basis for the prevention of hypertension. PROPOSED APPROACH This work proposes a CS-NET architecture by unifying CNN and SVM approaches to classify BP using PPG signals. The main objective of the CS-NET method is to establish an accurate and reliable algorithm for the ABP classification. METHODOLOGY ABP signals are labeled normal and abnormal using the hypertension criteria the American College of Cardiology (ACC)/American Heart Association (AHA) laid down. The proposed CS-NET model incorporates three critical steps in three successive stages. The first stage includes converting a preprocessed PPG signal into a time-frequency (TF) representation called a super-resolution spectrogram by superlet transform. The second stage uses a convolutional neural network (CNN) model with several hidden layers to extract morphological features from every PPG super-resolution spectrogram. The third stage uses a support vector machine (SVM) classifier to classify the PPG signal. RESULTS PPG signals are used to train and test the proposed model. The performance of the proposed CS-NET method is tested using MIMIC-II, MIMIC-III, and PPG-BP-figshare database in terms of accuracy and F1 score. Moreover, the CS-NET method achieves better results with high accuracy when compared with other benchmark approaches that require an electrocardiogram signal for reference. CONCLUSIONS The proposed model achieved an aggregate classification accuracy of 98.21% across a five-fold cross-validation technique, making it a reliable approach for BP classification in clinical settings and realtime monitoring.
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Affiliation(s)
- Pankaj
- Department of Electronics and Communication Engineering, Bennett University, Greater Noida, India
| | - Ashish Kumar
- School of Electronics Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India
| | - Rama Komaragiri
- Department of Electronics and Communication Engineering, Bennett University, Greater Noida, India
| | - Manjeet Kumar
- Department of Electronics and Communication Engineering, Delhi Technological University, Delhi, India.
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Chowdhury MZI, Leung AA, Walker RL, Sikdar KC, O’Beirne M, Quan H, Turin TC. A comparison of machine learning algorithms and traditional regression-based statistical modeling for predicting hypertension incidence in a Canadian population. Sci Rep 2023; 13:13. [PMID: 36593280 PMCID: PMC9807553 DOI: 10.1038/s41598-022-27264-x] [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: 04/10/2022] [Accepted: 12/29/2022] [Indexed: 01/03/2023] Open
Abstract
Risk prediction models are frequently used to identify individuals at risk of developing hypertension. This study evaluates different machine learning algorithms and compares their predictive performance with the conventional Cox proportional hazards (PH) model to predict hypertension incidence using survival data. This study analyzed 18,322 participants on 24 candidate features from the large Alberta's Tomorrow Project (ATP) to develop different prediction models. To select the top features, we applied five feature selection methods, including two filter-based: a univariate Cox p-value and C-index; two embedded-based: random survival forest and least absolute shrinkage and selection operator (Lasso); and one constraint-based: the statistically equivalent signature (SES). Five machine learning algorithms were developed to predict hypertension incidence: penalized regression Ridge, Lasso, Elastic Net (EN), random survival forest (RSF), and gradient boosting (GB), along with the conventional Cox PH model. The predictive performance of the models was assessed using C-index. The performance of machine learning algorithms was observed, similar to the conventional Cox PH model. Average C-indexes were 0.78, 0.78, 0.78, 0.76, 0.76, and 0.77 for Ridge, Lasso, EN, RSF, GB and Cox PH, respectively. Important features associated with each model were also presented. Our study findings demonstrate little predictive performance difference between machine learning algorithms and the conventional Cox PH regression model in predicting hypertension incidence. In a moderate dataset with a reasonable number of features, conventional regression-based models perform similar to machine learning algorithms with good predictive accuracy.
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Affiliation(s)
- Mohammad Ziaul Islam Chowdhury
- grid.22072.350000 0004 1936 7697Department of Community Health Sciences, University of Calgary, 3280 Hospital Drive NW, Calgary, AB T2N 4Z6 Canada ,grid.22072.350000 0004 1936 7697Department of Family Medicine, University of Calgary, 3330 Hospital Drive NW, Calgary, AB T2N 4N1 Canada ,grid.22072.350000 0004 1936 7697Present Address: Department of Psychiatry, University of Calgary, 3280 Hospital Drive NW, Calgary, AB T2N 4Z6 Canada
| | - Alexander A. Leung
- grid.22072.350000 0004 1936 7697Department of Community Health Sciences, University of Calgary, 3280 Hospital Drive NW, Calgary, AB T2N 4Z6 Canada ,grid.22072.350000 0004 1936 7697Department of Medicine, University of Calgary, 3280 Hospital Drive NW, Calgary, AB T2N 4Z6 Canada
| | - Robin L. Walker
- grid.22072.350000 0004 1936 7697Department of Community Health Sciences, University of Calgary, 3280 Hospital Drive NW, Calgary, AB T2N 4Z6 Canada ,grid.413574.00000 0001 0693 8815Primary Health Care Integration Network, Primary Health Care, Alberta Health Services, Calgary, AB Canada
| | - Khokan C. Sikdar
- grid.413574.00000 0001 0693 8815Health Status Assessment, Surveillance and Reporting, Public Health Surveillance and Infrastructure, Provincial Population and Public Health, Alberta Health Services, 10101 Southport Rd. SW, Calgary, AB T2W 3N2 Canada
| | - Maeve O’Beirne
- grid.22072.350000 0004 1936 7697Department of Family Medicine, University of Calgary, 3330 Hospital Drive NW, Calgary, AB T2N 4N1 Canada
| | - Hude Quan
- grid.22072.350000 0004 1936 7697Department of Community Health Sciences, University of Calgary, 3280 Hospital Drive NW, Calgary, AB T2N 4Z6 Canada
| | - Tanvir C. Turin
- grid.22072.350000 0004 1936 7697Department of Community Health Sciences, University of Calgary, 3280 Hospital Drive NW, Calgary, AB T2N 4Z6 Canada ,grid.22072.350000 0004 1936 7697Department of Family Medicine, University of Calgary, 3330 Hospital Drive NW, Calgary, AB T2N 4N1 Canada
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Silva GFS, Fagundes TP, Teixeira BC, Chiavegatto Filho ADP. Machine Learning for Hypertension Prediction: a Systematic Review. Curr Hypertens Rep 2022; 24:523-533. [PMID: 35731335 DOI: 10.1007/s11906-022-01212-6] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/08/2022] [Indexed: 01/31/2023]
Abstract
PURPOSE OF REVIEW To provide an overview of the literature regarding the use of machine learning algorithms to predict hypertension. A systematic review was performed to select recent articles on the subject. RECENT FINDINGS The screening of the articles was conducted using a machine learning algorithm (ASReview). A total of 21 articles published between January 2018 and May 2021 were identified and compared according to variable selection, train-test split, data balancing, outcome definition, final algorithm, and performance metrics. Overall, the articles achieved an area under the ROC curve (AUROC) between 0.766 and 1.00. The algorithms most frequently identified as having the best performance were support vector machines (SVM), extreme gradient boosting (XGBoost), and random forest. Machine learning algorithms are a promising tool to improve preventive clinical decisions and targeted public health policies for hypertension. However, technical factors such as outcome definition, availability of the final code, predictive performance, explainability, and data leakage need to be consistently and critically evaluated.
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Affiliation(s)
- Gabriel F S Silva
- Department of Epidemiology, School of Public Health, University of São Paulo, São Paulo, SP, Brazil
| | - Thales P Fagundes
- Laboratory of Big Data and Predictive Analysis in Healthcare, School of Public Health, University of São Paulo, São Paulo, SP, Brazil
| | - Bruno C Teixeira
- Laboratory of Big Data and Predictive Analysis in Healthcare, School of Public Health, University of São Paulo, São Paulo, SP, Brazil
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Chowdhury MZI, Naeem I, Quan H, Leung AA, Sikdar KC, O’Beirne M, Turin TC. Prediction of hypertension using traditional regression and machine learning models: A systematic review and meta-analysis. PLoS One 2022; 17:e0266334. [PMID: 35390039 PMCID: PMC8989291 DOI: 10.1371/journal.pone.0266334] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Accepted: 03/19/2022] [Indexed: 11/19/2022] Open
Abstract
OBJECTIVE We aimed to identify existing hypertension risk prediction models developed using traditional regression-based or machine learning approaches and compare their predictive performance. METHODS We systematically searched MEDLINE, EMBASE, Web of Science, Scopus, and the grey literature for studies predicting the risk of hypertension among the general adult population. Summary statistics from the individual studies were the C-statistic, and a random-effects meta-analysis was used to obtain pooled estimates. The predictive performance of pooled estimates was compared between traditional regression-based models and machine learning-based models. The potential sources of heterogeneity were assessed using meta-regression, and study quality was assessed using the PROBAST (Prediction model Risk Of Bias ASsessment Tool) checklist. RESULTS Of 14,778 articles, 52 articles were selected for systematic review and 32 for meta-analysis. The overall pooled C-statistics was 0.75 [0.73-0.77] for the traditional regression-based models and 0.76 [0.72-0.79] for the machine learning-based models. High heterogeneity in C-statistic was observed. The age (p = 0.011), and sex (p = 0.044) of the participants and the number of risk factors considered in the model (p = 0.001) were identified as a source of heterogeneity in traditional regression-based models. CONCLUSION We attempted to provide a comprehensive evaluation of hypertension risk prediction models. Many models with acceptable-to-good predictive performance were identified. Only a few models were externally validated, and the risk of bias and applicability was a concern in many studies. Overall discrimination was similar between models derived from traditional regression analysis and machine learning methods. More external validation and impact studies to implement the hypertension risk prediction model in clinical practice are required.
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Affiliation(s)
- Mohammad Ziaul Islam Chowdhury
- Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Department of Family Medicine, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Iffat Naeem
- Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Hude Quan
- Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Alexander A. Leung
- Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Department of Medicine, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Khokan C. Sikdar
- Health Status Assessment, Surveillance, and Reporting, Public Health Surveillance and Infrastructure, Population, Public and Indigenous Health, Alberta Health Services, Calgary, Alberta, Canada
| | - Maeve O’Beirne
- Department of Family Medicine, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Tanvir C. Turin
- Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Department of Family Medicine, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
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Drouard G, Ollikainen M, Mykkänen J, Raitakari O, Lehtimäki T, Kähönen M, Mishra PP, Wang X, Kaprio J. Multi-Omics Integration in a Twin Cohort and Predictive Modeling of Blood Pressure Values. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2022; 26:130-141. [PMID: 35259029 PMCID: PMC8978565 DOI: 10.1089/omi.2021.0201] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/21/2023]
Abstract
Abnormal blood pressure is strongly associated with risk of high-prevalence diseases, making the study of blood pressure a major public health challenge. Although biological mechanisms underlying hypertension at the single omic level have been discovered, multi-omics integrative analyses using continuous variations in blood pressure values remain limited. We used a multi-omics regression-based method, called sparse multi-block partial least square, for integrative, explanatory, and predictive interests in study of systolic and diastolic blood pressure values. Various datasets were obtained from the Finnish Twin Cohort for up to 444 twins. Blocks of omics-including transcriptomic, methylation, metabolomic-data as well as polygenic risk scores and clinical data were integrated into the modeling and supported by cross-validation. The predictive contribution of each omics block when predicting blood pressure values was investigated using external participants from the Young Finns Study. In addition to revealing interesting inter-omics associations, we found that each block of omics heterogeneously improved the predictions of blood pressure values once the multi-omics data were integrated. The modeling revealed a plurality of clinical, transcriptomic, and metabolomic factors consistent with the literature and that play a leading role in explaining unit variations in blood pressure. These findings demonstrate (1) the robustness of our integrative method to harness results obtained by single omics discriminant analyses, and (2) the added value of predictive and exploratory gains of a multi-omics approach in studies of complex phenotypes such as blood pressure.
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Affiliation(s)
- Gabin Drouard
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Address correspondence to: Gabin Drouard, MSc, Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Tukholmankatu 8, Helsinki 00014, Finland
| | - Miina Ollikainen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Juha Mykkänen
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
| | - Olli Raitakari
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories and Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Mika Kähönen
- Department of Clinical Physiology, Tampere University Hospital, and Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Pashupati P. Mishra
- Department of Clinical Chemistry, Fimlab Laboratories and Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Xiaoling Wang
- Georgia Prevention Institute (GPI), Medical College of Georgia, Augusta University, Augusta, Georgia, USA
| | - Jaakko Kaprio
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
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Xiang Y, Li S, Zhang P. An exploration in remote blood pressure management: Application of daily routine pattern based on mobile data in health management. FUNDAMENTAL RESEARCH 2022; 2:154-165. [PMID: 38933904 PMCID: PMC11197610 DOI: 10.1016/j.fmre.2021.11.006] [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: 04/22/2021] [Revised: 09/02/2021] [Accepted: 11/12/2021] [Indexed: 11/24/2022] Open
Abstract
Blood pressure (BP) is an important indicator of an individual's health status and is closely related to daily behaviors. Thus, a continuous daily measurement of BP is critical for hypertension control. To assist continuous measurement, BP prediction based on non-physiological data (ubiquitous mobile phone data) was studied in the research. An algorithm was proposed that predicts BP based on patients' daily routine, which includes activities such as sleep, work, and commuting. The aim of the research is to provide insight into the application of mobile data in telemonitoring and the continuous unobtrusive daily measurement of BP. A half-year data set from October 2017 of 320 individuals, including telecom data and BP measurement data, was analyzed. Two hierarchical Bayesian topic models were used to extract individuals' location-driven daily routine patterns (topics) and calculate probabilities among these topics from their day-level mobile trajectories. Based on the topic probability distribution and patients' contextual data, their BP were predicted using different models. The prediction model comparison shows that the long short-term memory (LSTM) method exceeds others when the data has a high dependency. Otherwise, the Random Forest regression model outperforms the LSTM method. Also, the experimental results validate the effectiveness of the topics in BP prediction.
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Affiliation(s)
- Yidan Xiang
- Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Shaochun Li
- Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Pengzhu Zhang
- Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai 200240, China
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8
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A Multilayer Perceptron Neural Network Model to Classify Hypertension in Adolescents Using Anthropometric Measurements: A Cross-Sectional Study in Sarawak, Malaysia. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:2794888. [PMID: 34917164 PMCID: PMC8670914 DOI: 10.1155/2021/2794888] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 09/28/2021] [Accepted: 11/13/2021] [Indexed: 11/21/2022]
Abstract
This study outlines and developed a multilayer perceptron (MLP) neural network model for adolescent hypertension classification focusing on the use of simple anthropometric and sociodemographic data collected from a cross-sectional research study in Sarawak, Malaysia. Among the 2,461 data collected, 741 were hypertensive (30.1%) and 1720 were normal (69.9%). During the data gathering process, eleven anthropometric measurements and sociodemographic data were collected. The variable selection procedure in the methodology proposed selected five parameters: weight, weight-to-height ratio (WHtR), age, sex, and ethnicity, as the input of the network model. The developed MLP model with a single hidden layer of 50 hidden neurons managed to achieve a sensitivity of 0.41, specificity of 0.91, precision of 0.65, F-score of 0.50, accuracy of 0.76, and Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) of 0.75 using the imbalanced data set. Analyzing the performance metrics obtained from the training, validation and testing data sets show that the developed network model is well-generalized. Using Bayes' Theorem, an adolescent classified as hypertensive using this created model has a 66.2% likelihood of having hypertension in the Sarawak adolescent population, which has a hypertension prevalence of 30.1%. When the prevalence of hypertension in the Sarawak population was increased to 50%, the developed model could predict an adolescent having hypertension with an 82.0% chance, whereas when the prevalence of hypertension was reduced to 10%, the developed model could only predict true positive hypertension with a 33.6% chance. With the sensitivity of the model increasing to 65% and 90% while retaining a specificity of 91%, the true positivity of an adolescent being hypertension would be 75.7% and 81.2%, respectively, according to Bayes' Theorem. The findings show that simple anthropometric measurements paired with sociodemographic data are feasible to be used to classify hypertension in adolescents using the developed MLP model in Sarawak adolescent population with modest hypertension prevalence. However, a model with higher sensitivity and specificity is required for better positive hypertension predictive value when the prevalence is low. We conclude that the developed classification model could serve as a quick and easy preliminary warning tool for screening high-risk adolescents of developing hypertension.
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Martinez-Ríos E, Montesinos L, Alfaro-Ponce M, Pecchia L. A review of machine learning in hypertension detection and blood pressure estimation based on clinical and physiological data. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102813] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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10
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Marcos-Pardo PJ, González-Gálvez N, Espeso-García A, Abelleira-Lamela T, López-Vivancos A, Vaquero-Cristóbal R. Association among Adherence to the Mediterranean Diet, Cardiorespiratory Fitness, Cardiovascular, Obesity, and Anthropometric Variables of Overweight and Obese Middle-Aged and Older Adults. Nutrients 2020; 12:nu12092750. [PMID: 32927609 PMCID: PMC7551167 DOI: 10.3390/nu12092750] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 09/07/2020] [Accepted: 09/07/2020] [Indexed: 02/06/2023] Open
Abstract
The aim of this study was to evaluate the independent and combined associations between adherence to the Mediterranean diet (AMedDiet), cardiorespiratory fitness (CRF), and different parameters of overweight and obese middle-aged and older adults. Sixty-two participants were enrolled in this cross-sectional study. Fat mass was measured with Dual energy X-ray absorptiometry. AMedDiet and physical activity (PA) were assessed with the PREDIMED and Global PA Questionnaire (GPAQ). Maximal aerobic power was assessed using the 6-min walk test. Systolic (SBP) and diastolic (DBP) blood pressure (BP) were measured with Omron M6, and double product (DP) and mean BP (MBP) were calculated. Kinanthropometry proportionality variables related to obesity were also calculated. Participants with a low CRF as an independent factor or together with a low AMedDiet obtained significantly higher BP, total and trunk fat mass, and proportionality variables (all p ˂ 0.0001). According to the multiple nonlinear regression analysis, Vo2max, AMedDiet, and sex explained 53.4% of SBP, with this formula: 238.611 − (3.63*Vo2max) + (0.044*Vo2max2) − (13.051*AMedDiet) + (0.68*AMedDiet2) + (12.887*sex). SBP and p rediction SBP with the new formula showed a correlation of 0.731 (p ˂ 0.0001); showing a difference between the values of −0.278 (p = 0.883). In conclusion, CRF as an independent factor and combined with AMedDiet can be associated with BP, body composition, and proportionality in overweight and obese middle-aged and older adults.
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Affiliation(s)
- Pablo J. Marcos-Pardo
- Research Group on Health, Physical Activity, Fitness and Motor Behaviour (GISAFFCOM), Catholic University San Antonio of Murcia, Campus de los Jerónimos, Guadalupe, 30107 Murcia, Spain; (P.J.M.-P.); (A.E.-G.); (T.A.-L.); (A.L.-V.)
- Active Aging, Exercise and Health/HEALTHY-AGE Network, Consejo Superior de Deportes (CSD), Ministry of Culture and Sport of Spain, 28040 Madrid, Spain;
| | - Noelia González-Gálvez
- Research Group on Health, Physical Activity, Fitness and Motor Behaviour (GISAFFCOM), Catholic University San Antonio of Murcia, Campus de los Jerónimos, Guadalupe, 30107 Murcia, Spain; (P.J.M.-P.); (A.E.-G.); (T.A.-L.); (A.L.-V.)
- Active Aging, Exercise and Health/HEALTHY-AGE Network, Consejo Superior de Deportes (CSD), Ministry of Culture and Sport of Spain, 28040 Madrid, Spain;
- Correspondence: ; Tel.: +34-968-278-824
| | - Alejandro Espeso-García
- Research Group on Health, Physical Activity, Fitness and Motor Behaviour (GISAFFCOM), Catholic University San Antonio of Murcia, Campus de los Jerónimos, Guadalupe, 30107 Murcia, Spain; (P.J.M.-P.); (A.E.-G.); (T.A.-L.); (A.L.-V.)
| | - Tomás Abelleira-Lamela
- Research Group on Health, Physical Activity, Fitness and Motor Behaviour (GISAFFCOM), Catholic University San Antonio of Murcia, Campus de los Jerónimos, Guadalupe, 30107 Murcia, Spain; (P.J.M.-P.); (A.E.-G.); (T.A.-L.); (A.L.-V.)
| | - Abraham López-Vivancos
- Research Group on Health, Physical Activity, Fitness and Motor Behaviour (GISAFFCOM), Catholic University San Antonio of Murcia, Campus de los Jerónimos, Guadalupe, 30107 Murcia, Spain; (P.J.M.-P.); (A.E.-G.); (T.A.-L.); (A.L.-V.)
- Active Aging, Exercise and Health/HEALTHY-AGE Network, Consejo Superior de Deportes (CSD), Ministry of Culture and Sport of Spain, 28040 Madrid, Spain;
| | - Raquel Vaquero-Cristóbal
- Active Aging, Exercise and Health/HEALTHY-AGE Network, Consejo Superior de Deportes (CSD), Ministry of Culture and Sport of Spain, 28040 Madrid, Spain;
- Sports Injury Prevention Research Group, Catholic University San Antonio of Murcia, Campus de los Jerónimos, Guadalupe, 30107 Murcia, Spain
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Koshimizu H, Kojima R, Okuno Y. Future possibilities for artificial intelligence in the practical management of hypertension. Hypertens Res 2020; 43:1327-1337. [PMID: 32655135 DOI: 10.1038/s41440-020-0498-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2020] [Revised: 05/13/2020] [Accepted: 05/17/2020] [Indexed: 11/09/2022]
Abstract
The use of artificial intelligence in numerous prediction and classification tasks, including clinical research and healthcare management, is becoming increasingly more common. This review describes the current status and a future possibility for artificial intelligence in blood pressure management, that is, the possibility of accurately predicting and estimating blood pressure using large-scale data, such as personal health records and electronic medical records. Individual blood pressure continuously changes because of lifestyle habits and the environment. This review focuses on two topics regarding controlling changing blood pressure: a novel blood pressure measurement system and blood pressure analysis using artificial intelligence. Regarding the novel blood pressure measurement system, we compare the conventional cuff-less method with the analysis of pulse waves using artificial intelligence for blood pressure estimation. Then, we describe the prediction of future blood pressure values using machine learning and deep learning. In addition, we summarize factor analysis using "explainable AI" to solve a black-box problem of artificial intelligence. Overall, we show that artificial intelligence is advantageous for hypertension management and can be used to establish clinical evidence for the practical management of hypertension.
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Affiliation(s)
- Hiroshi Koshimizu
- Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto, 606-8507, Japan.,Development Center, Omron Healthcare Co., Ltd., Kyoto, 617-0002, Japan
| | - Ryosuke Kojima
- Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto, 606-8507, Japan
| | - Yasushi Okuno
- Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto, 606-8507, Japan.
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Urrea C, Venegas D. Automatized follow-up and alert system for patients with chronic hypertension. Health Informatics J 2020; 26:2625-2636. [DOI: 10.1177/1460458219900446] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
This article presents the development and implementation of a monitoring system for patients with chronic hypertension. Technological advances in wireless communication are increasingly used today to send and receive information through smartphones. This also applies to devices for measuring blood pressure, which can be efficiently integrated with smartphones. Telemedicine is used in a variety of health fields, and in the past 5 years, it has extended its reach to the online monitoring of patients. The objective of this study is to create an integrated system capable of conducting the follow-up, through mobile communication (smartphones), of patients with chronic diseases such as hypertension. An iHealth equipment certified by the Food and Drug Administration is used. The blood pressure values from users are uploaded via Internet and stored in an integral system for processing. The monitoring system developed not only informs users about their disease status but also sends them alerts generated during monitoring. This work uses the telecommunication technology existing through smartphones. The integrated system developed ensures the follow-up of the blood pressure of a large number of users. In addition, this system can be further applied to diseases such as diabetes and metabolic syndrome. The system developed was easy to use and efficient to monitor patients with chronic diseases such as high blood pressure.
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Prediction of blood pressure variability using deep neural networks. Int J Med Inform 2020; 136:104067. [DOI: 10.1016/j.ijmedinf.2019.104067] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Revised: 12/09/2019] [Accepted: 12/26/2019] [Indexed: 12/17/2022]
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Shahid N, Rappon T, Berta W. Applications of artificial neural networks in health care organizational decision-making: A scoping review. PLoS One 2019; 14:e0212356. [PMID: 30779785 PMCID: PMC6380578 DOI: 10.1371/journal.pone.0212356] [Citation(s) in RCA: 116] [Impact Index Per Article: 23.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2018] [Accepted: 01/31/2019] [Indexed: 12/12/2022] Open
Abstract
Health care organizations are leveraging machine-learning techniques, such as artificial neural networks (ANN), to improve delivery of care at a reduced cost. Applications of ANN to diagnosis are well-known; however, ANN are increasingly used to inform health care management decisions. We provide a seminal review of the applications of ANN to health care organizational decision-making. We screened 3,397 articles from six databases with coverage of Health Administration, Computer Science and Business Administration. We extracted study characteristics, aim, methodology and context (including level of analysis) from 80 articles meeting inclusion criteria. Articles were published from 1997–2018 and originated from 24 countries, with a plurality of papers (26 articles) published by authors from the United States. Types of ANN used included ANN (36 articles), feed-forward networks (25 articles), or hybrid models (23 articles); reported accuracy varied from 50% to 100%. The majority of ANN informed decision-making at the micro level (61 articles), between patients and health care providers. Fewer ANN were deployed for intra-organizational (meso- level, 29 articles) and system, policy or inter-organizational (macro- level, 10 articles) decision-making. Our review identifies key characteristics and drivers for market uptake of ANN for health care organizational decision-making to guide further adoption of this technique.
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Affiliation(s)
- Nida Shahid
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada
- Toronto Health Economics and Technology Assessment (THETA) Collaborative, University Health Network, Toronto, Canada
- * E-mail:
| | - Tim Rappon
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada
| | - Whitney Berta
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada
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