We present an extension of our previous work in [1] by investigating the use of Local Septenary Patterns (LSP) for breast density classification in mammograms. The LSP operator is a variant of Local Binary Patterns (LBP) inspired by Local Ternary Patterns (LTP) and Local Quinary patterns (LQP). The main extensions in our work are i) we investigate the use of a multi-resolution technique when extracting micro texture information, ii) we investigate different neighbourhood topologies as different ways of extracting texture features, and iii) we use an additional dataset called InBreast as well as the most popular dataset in the literature, which is the Mammographic Image Analysis Society (MIAS) to further evaluate the performance of the LSP operator.
{"title":"Breast Density Classification using Local Septenary Patterns: A Multi-resolution and Multi-topology Approach","authors":"Andrik Rampun, B. Scotney, P. Morrow, Haibo Wang","doi":"10.1109/CBMS.2019.00133","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00133","url":null,"abstract":"We present an extension of our previous work in [1] by investigating the use of Local Septenary Patterns (LSP) for breast density classification in mammograms. The LSP operator is a variant of Local Binary Patterns (LBP) inspired by Local Ternary Patterns (LTP) and Local Quinary patterns (LQP). The main extensions in our work are i) we investigate the use of a multi-resolution technique when extracting micro texture information, ii) we investigate different neighbourhood topologies as different ways of extracting texture features, and iii) we use an additional dataset called InBreast as well as the most popular dataset in the literature, which is the Mammographic Image Analysis Society (MIAS) to further evaluate the performance of the LSP operator.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132077462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
There is increasing interest in the potential of synthetic data to validate and benchmark machine learning algorithms as well as reveal any biases in real-world data used for algorithm development. This paper discusses the key requirements of synthetic data for such purposes and proposes an approach to generating and evaluating synthetic data that meets these requirements. We propose a framework to generate and evaluate synthetic data with the aim of simultaneously preserving the complexities of ground truth data in the synthetic data whilst also ensuring privacy. We include as a case study, a proof-of-concept synthetic dataset modelled on UK primary care data to demonstrate the application of this framework.
{"title":"Generating and Evaluating Synthetic UK Primary Care Data: Preserving Data Utility & Patient Privacy","authors":"Zhenchen Wang, P. Myles, A. Tucker","doi":"10.1109/CBMS.2019.00036","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00036","url":null,"abstract":"There is increasing interest in the potential of synthetic data to validate and benchmark machine learning algorithms as well as reveal any biases in real-world data used for algorithm development. This paper discusses the key requirements of synthetic data for such purposes and proposes an approach to generating and evaluating synthetic data that meets these requirements. We propose a framework to generate and evaluate synthetic data with the aim of simultaneously preserving the complexities of ground truth data in the synthetic data whilst also ensuring privacy. We include as a case study, a proof-of-concept synthetic dataset modelled on UK primary care data to demonstrate the application of this framework.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130830365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mobile health (m-Health) has emerged as a rapidly developing area that is transforming clinical research and health care on a global scale. In this paper, we describe a conversational app for the therapy of stroke rehabilitation. The main objective of the conversational app is to help recovering cognitive abilities of patients by means of a set of proposed exercises, which are divided into 8 categories focused on specific abilities. These categories have been defined after a detailed review of the guidelines for rehabilitation and training therapies. In addition, the application integrates a multimodal conversational interface to facilitate human-computer interaction, which has been specially designed for the elderly and patients with motor or visual or disabilities. The exercises provided by the application can be easily adapted to the specific users' requirements and preferences by means of the incorporation, deletion or modification of routines stored into a specific database isolated from the logic of the application.
{"title":"Mobile Conversational Agents for Stroke Rehabilitation Therapy","authors":"D. Griol, Zoraida Callejas Carrión","doi":"10.1109/CBMS.2019.00104","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00104","url":null,"abstract":"Mobile health (m-Health) has emerged as a rapidly developing area that is transforming clinical research and health care on a global scale. In this paper, we describe a conversational app for the therapy of stroke rehabilitation. The main objective of the conversational app is to help recovering cognitive abilities of patients by means of a set of proposed exercises, which are divided into 8 categories focused on specific abilities. These categories have been defined after a detailed review of the guidelines for rehabilitation and training therapies. In addition, the application integrates a multimodal conversational interface to facilitate human-computer interaction, which has been specially designed for the elderly and patients with motor or visual or disabilities. The exercises provided by the application can be easily adapted to the specific users' requirements and preferences by means of the incorporation, deletion or modification of routines stored into a specific database isolated from the logic of the application.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130932195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
B. Cánovas-Segura, Antonio Morales Nicolás, Antonio López Martínez-Carrasco, M. Campos, J. Juarez, L. López-Rodríguez, Francisco Palacios Ortega
One of the major problems of healthcare institutions is the treatment of infections caused by bacteria that are resistant to antimicrobials. The early prediction of such infections can improve the patient's evolution as well as minimise the spread of antimicrobial resistance. The creation of effective prediction models is particularly limited due to the high dimensionality of data, the imbalanced datasets and the concept drift problem. In this paper, we face these challenges from a machine learning perspective, considering the interpretability of the resulting models as essential. In particular, we present a study of multiple techniques focused on the mitigation of these problems, that are used in combination with interpretable models. Our results indicate that the use of oversampling along with sliding windows can improve the resulting AUC of models (up to reaching a mean AUC of 0.80 in our dataset), and FCBF can be used to drastically reduce the number of predictors, obtaining simpler models with a slight AUC reduction (from a mean number of predictors of 69.78 to 16.28, achieving a mean AUC of 0.76). According to our results, we show that the combination of multiple techniques for dealing with the aforementioned data-mining problems can clearly improve the performance of prediction models for antimicrobial resistance.
{"title":"Improving Interpretable Prediction Models for Antimicrobial Resistance","authors":"B. Cánovas-Segura, Antonio Morales Nicolás, Antonio López Martínez-Carrasco, M. Campos, J. Juarez, L. López-Rodríguez, Francisco Palacios Ortega","doi":"10.1109/CBMS.2019.00111","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00111","url":null,"abstract":"One of the major problems of healthcare institutions is the treatment of infections caused by bacteria that are resistant to antimicrobials. The early prediction of such infections can improve the patient's evolution as well as minimise the spread of antimicrobial resistance. The creation of effective prediction models is particularly limited due to the high dimensionality of data, the imbalanced datasets and the concept drift problem. In this paper, we face these challenges from a machine learning perspective, considering the interpretability of the resulting models as essential. In particular, we present a study of multiple techniques focused on the mitigation of these problems, that are used in combination with interpretable models. Our results indicate that the use of oversampling along with sliding windows can improve the resulting AUC of models (up to reaching a mean AUC of 0.80 in our dataset), and FCBF can be used to drastically reduce the number of predictors, obtaining simpler models with a slight AUC reduction (from a mean number of predictors of 69.78 to 16.28, achieving a mean AUC of 0.76). According to our results, we show that the combination of multiple techniques for dealing with the aforementioned data-mining problems can clearly improve the performance of prediction models for antimicrobial resistance.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115477722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Guilherme F. Zabot, M. Cazzolato, L. C. Scabora, Bruno S. Faiçal, A. Traina, C. Traina
The large amount of medical exams generated by hospitals has a great potential to boost the support for physicians on decision making tasks. This requires efficient and reliable computational systems to retrieve relevant information in real-time. Existing Content-Based Image Retrieval (CBIR) systems rely on Metric Access Methods (MAMs) to speed-up the retrieval task. In this context, images are represented by Feature Extraction Methods (FEMs), according to information such as color or texture. However, MAMs usually index images based on a single FEM. Whenever physicians want to search for similar images using multiple FEMs simultaneously, they need to perform separated queries. In this work, we propose UCORM, an access method capable of indexing images using multiple FEMs by overlapping different metric spaces. UCORM selects the best FEMs to generate a concise yet accurate indexing space. It relies on an interesting use of Pearson correlation, that we named PCMS, to compute the correlation between different FEMs. PCMS allows UCORM to improve the retrieval task by minimizing the overlapping between metric spaces, resulting on fewer intermediary images when performing a query. Experimental analysis shows that UCORM prunes well the data distribution regions with low correlation between FEMs. Also, two medical application scenarios support our claim that UCORM is well-fitted for clinical environments.
{"title":"UCORM: Indexing Uncorrelated Metric Spaces for Concise Content-Based Retrieval of Medical Images","authors":"Guilherme F. Zabot, M. Cazzolato, L. C. Scabora, Bruno S. Faiçal, A. Traina, C. Traina","doi":"10.1109/CBMS.2019.00070","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00070","url":null,"abstract":"The large amount of medical exams generated by hospitals has a great potential to boost the support for physicians on decision making tasks. This requires efficient and reliable computational systems to retrieve relevant information in real-time. Existing Content-Based Image Retrieval (CBIR) systems rely on Metric Access Methods (MAMs) to speed-up the retrieval task. In this context, images are represented by Feature Extraction Methods (FEMs), according to information such as color or texture. However, MAMs usually index images based on a single FEM. Whenever physicians want to search for similar images using multiple FEMs simultaneously, they need to perform separated queries. In this work, we propose UCORM, an access method capable of indexing images using multiple FEMs by overlapping different metric spaces. UCORM selects the best FEMs to generate a concise yet accurate indexing space. It relies on an interesting use of Pearson correlation, that we named PCMS, to compute the correlation between different FEMs. PCMS allows UCORM to improve the retrieval task by minimizing the overlapping between metric spaces, resulting on fewer intermediary images when performing a query. Experimental analysis shows that UCORM prunes well the data distribution regions with low correlation between FEMs. Also, two medical application scenarios support our claim that UCORM is well-fitted for clinical environments.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116330641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
F. Riaz, R. Nemati, Hina Ajmal, Ali Hassan, E. Edifor, R. Nawaz
Assessment of osteoporotic disease from the radiograph image is a significant challenge. Texture characteristics when observed from the naked eye for the bone microarchitecture of the osteoporotic and healthy cases are visually very similar making it a challenging classification problem. To extract the discriminative patterns in all the orientations and scales simultaneously in this study we have proposed an approach that is based on a combination of multi resolution Gabor filters and 1D local binary pattern (1DLBP) features. Gabor filter are used due to their advantages in yielding a scale and orientation sensitive analysis whereas LBPs are useful for quantifying microstructural changes in the images. Our experiment show that the proposed method shows good classification results with an overall accuracy of about 72.71% and outperforms the other methods that have been considered in this paper.
{"title":"Osteoporosis Classification Using Texture Features","authors":"F. Riaz, R. Nemati, Hina Ajmal, Ali Hassan, E. Edifor, R. Nawaz","doi":"10.1109/CBMS.2019.00119","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00119","url":null,"abstract":"Assessment of osteoporotic disease from the radiograph image is a significant challenge. Texture characteristics when observed from the naked eye for the bone microarchitecture of the osteoporotic and healthy cases are visually very similar making it a challenging classification problem. To extract the discriminative patterns in all the orientations and scales simultaneously in this study we have proposed an approach that is based on a combination of multi resolution Gabor filters and 1D local binary pattern (1DLBP) features. Gabor filter are used due to their advantages in yielding a scale and orientation sensitive analysis whereas LBPs are useful for quantifying microstructural changes in the images. Our experiment show that the proposed method shows good classification results with an overall accuracy of about 72.71% and outperforms the other methods that have been considered in this paper.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129521138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zoe Valero-Ramon, C. Fernández-Llatas, A. Martínez-Millana, V. Traver
Malnutrition is one of the major geriatric syndromes and frailty factor, this joint with the fact of elderly population growing, will situate malnutrition as a front end problem in the upcoming years. Therefore, it is important that health professionals can assess and follow up nutritional status in a proper way, using all available data related to patients. Process mining can be used to extract knowledge from information in order to understand health care processes. A classic approach to assess malnutrition usually comprises anthropometric measures as static variables, with no information about patients evolution and pathways. The aim of this work was to examine anthropometric measures from a dynamic perspective thanks to process mining tools, in order to obtain dynamic behaviour models. This paper proposes a method based on the use of process mining to discover and identify weight changes behaviour. Clustering is used as part of the pre-processing of data to manage variability, and then process mining is used to identify patterns of patients' behaviour. The method is applied through different experiments to data from 96 patients. Results grouped almost all individuals in different models based on common behaviours. Main finding shows different behaviour groups seem to have different results regarding malnutrition status for same interventions. By discovering patterns of dynamic weight change and their relation with malnutrition, nursing homes and health care professional can promote more successful intervention among patients based on their behaviour, moreover they can compare interventions' results analysing changes in behaviour between before and after the intervention.
{"title":"A Dynamic Behavioral Approach to Nutritional Assessment using Process Mining","authors":"Zoe Valero-Ramon, C. Fernández-Llatas, A. Martínez-Millana, V. Traver","doi":"10.1109/CBMS.2019.00085","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00085","url":null,"abstract":"Malnutrition is one of the major geriatric syndromes and frailty factor, this joint with the fact of elderly population growing, will situate malnutrition as a front end problem in the upcoming years. Therefore, it is important that health professionals can assess and follow up nutritional status in a proper way, using all available data related to patients. Process mining can be used to extract knowledge from information in order to understand health care processes. A classic approach to assess malnutrition usually comprises anthropometric measures as static variables, with no information about patients evolution and pathways. The aim of this work was to examine anthropometric measures from a dynamic perspective thanks to process mining tools, in order to obtain dynamic behaviour models. This paper proposes a method based on the use of process mining to discover and identify weight changes behaviour. Clustering is used as part of the pre-processing of data to manage variability, and then process mining is used to identify patterns of patients' behaviour. The method is applied through different experiments to data from 96 patients. Results grouped almost all individuals in different models based on common behaviours. Main finding shows different behaviour groups seem to have different results regarding malnutrition status for same interventions. By discovering patterns of dynamic weight change and their relation with malnutrition, nursing homes and health care professional can promote more successful intervention among patients based on their behaviour, moreover they can compare interventions' results analysing changes in behaviour between before and after the intervention.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129938339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
B. I. Dodo, Yongmin Li, A. Tucker, Djibril Kaba, Xiaohui Liu
Optical coherence tomography (OCT) is a noninvasive imaging modality that provides in-depth images of the retina. Properties of individual layers on OCT have become important markers for diagnosing and tracking medication of various eye diseases in current ophthalmology. Manual segmentation of OCT scans posed many challenges (errors, inconsistency), which can be addressed by automated segmentation methods. Level set method is one of the most popular methods in the literature used for this purpose. Although level set methods have a fundamental way of handling topological changes, the weak boundaries and noise in addition to inhomogeneity in OCT images make it difficult to segment the layers accurately. Inspired by the concept of region competition, we incorporate prior knowledge of the retinal structure to segment nine (9) layers of the retina. Mainly, we establish a specific region of interest, then use selected components from fuzzy C-Means for initialisation. The clustering in the initialisation stage is also used to guide the evolution through; a Mumford-Shah (MS) selective region competition force and a Hamilton-Jacobi (HJ) balloon force. The forces ensure evolution close to actual retinal boundaries. Finally, the convergence of the method is based on an improved HJ object indication function influenced by the fuzzy membership to prevent leakages at weak boundaries. Experimental results are promising based on 200 OCT images.
{"title":"Retinal OCT Segmentation Using Fuzzy Region Competition and Level Set Methods","authors":"B. I. Dodo, Yongmin Li, A. Tucker, Djibril Kaba, Xiaohui Liu","doi":"10.1109/CBMS.2019.00029","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00029","url":null,"abstract":"Optical coherence tomography (OCT) is a noninvasive imaging modality that provides in-depth images of the retina. Properties of individual layers on OCT have become important markers for diagnosing and tracking medication of various eye diseases in current ophthalmology. Manual segmentation of OCT scans posed many challenges (errors, inconsistency), which can be addressed by automated segmentation methods. Level set method is one of the most popular methods in the literature used for this purpose. Although level set methods have a fundamental way of handling topological changes, the weak boundaries and noise in addition to inhomogeneity in OCT images make it difficult to segment the layers accurately. Inspired by the concept of region competition, we incorporate prior knowledge of the retinal structure to segment nine (9) layers of the retina. Mainly, we establish a specific region of interest, then use selected components from fuzzy C-Means for initialisation. The clustering in the initialisation stage is also used to guide the evolution through; a Mumford-Shah (MS) selective region competition force and a Hamilton-Jacobi (HJ) balloon force. The forces ensure evolution close to actual retinal boundaries. Finally, the convergence of the method is based on an improved HJ object indication function influenced by the fuzzy membership to prevent leakages at weak boundaries. Experimental results are promising based on 200 OCT images.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130025016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
D. Fernández-Orth, Audald Lloret-Villas, Jordi Rambla De Argila
The European Genome-phenome Archive (EGA) is a repository that facilitates access and management for long-term archival of human biomolecular data. The EGA is co-managed by the European Bioinformatics Institute (EBI) and the Centre for Genomic Regulation (CRG). As the omics community awareness of data sharing and reproducibility increases, complex services and granular solutions are needed from the repositories such as EGA. Not only will we introduce the EGA environment but we will also present advanced features designed for a wide range of users. These new tools and technologies include the EGA Beacon (developed within the GA4GH and ELIXIR framework), infrastructures for data access and retrieval, as well as data quality control and visualisation projects.
{"title":"European Genome-Phenome Archive (EGA) - Granular Solutions for the Next 10 Years","authors":"D. Fernández-Orth, Audald Lloret-Villas, Jordi Rambla De Argila","doi":"10.1109/CBMS.2019.00011","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00011","url":null,"abstract":"The European Genome-phenome Archive (EGA) is a repository that facilitates access and management for long-term archival of human biomolecular data. The EGA is co-managed by the European Bioinformatics Institute (EBI) and the Centre for Genomic Regulation (CRG). As the omics community awareness of data sharing and reproducibility increases, complex services and granular solutions are needed from the repositories such as EGA. Not only will we introduce the EGA environment but we will also present advanced features designed for a wide range of users. These new tools and technologies include the EGA Beacon (developed within the GA4GH and ELIXIR framework), infrastructures for data access and retrieval, as well as data quality control and visualisation projects.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131017628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xiaoli Liu, Xiang Su, S. Tamminen, Topi Korhonen, J. Röning
Heart rate is a good measure for physical exercise as it accurately reflects exercise intensity and is easy to measure. If the heart rate response to a complete exercise session is predicted beforehand, information related to the exercise can be inferred, such as exercise intensity and calorie consumption. While most current heart rate prediction models are developed and tested for the scenarios of indoor running exercise or low running speed exercise, we adopt a nonlinear Ordinary Differential Equation (ODE) model for complete outdoor running exercise sessions to predict the heart rate response and identify the parameters of the model with machine learning algorithms. The proposed model enables us to predict a complete outdoor running exercise session instead of predicting the heart rate for a short duration. Model validation is carried out both on the training and testing sets. Our results show that the proposed model captures very stable prediction performance.
{"title":"Predicting the Heart Rate Response to Outdoor Running Exercise","authors":"Xiaoli Liu, Xiang Su, S. Tamminen, Topi Korhonen, J. Röning","doi":"10.1109/CBMS.2019.00052","DOIUrl":"https://doi.org/10.1109/CBMS.2019.00052","url":null,"abstract":"Heart rate is a good measure for physical exercise as it accurately reflects exercise intensity and is easy to measure. If the heart rate response to a complete exercise session is predicted beforehand, information related to the exercise can be inferred, such as exercise intensity and calorie consumption. While most current heart rate prediction models are developed and tested for the scenarios of indoor running exercise or low running speed exercise, we adopt a nonlinear Ordinary Differential Equation (ODE) model for complete outdoor running exercise sessions to predict the heart rate response and identify the parameters of the model with machine learning algorithms. The proposed model enables us to predict a complete outdoor running exercise session instead of predicting the heart rate for a short duration. Model validation is carried out both on the training and testing sets. Our results show that the proposed model captures very stable prediction performance.","PeriodicalId":311634,"journal":{"name":"2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125599535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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