Determination gender-based hybrid artificial intelligence of body muscle percentage by photoplethysmography signal
•Artificial intelligence-based prediction model for body muscle percentage (BMP).•Gender-specific BMP prediction model.•BMP prediction model with photoplethysmography signal (PPG).•Low-cost BMP estimation model.•High accuracy hybrid artificial intelligence-based BMP prediction. Background and object...
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| Published in: | Computer methods and programs in biomedicine Vol. 224; p. 107010 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier B.V
01.09.2022
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| Subjects: | |
| ISSN: | 0169-2607, 1872-7565, 1872-7565 |
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| Abstract | •Artificial intelligence-based prediction model for body muscle percentage (BMP).•Gender-specific BMP prediction model.•BMP prediction model with photoplethysmography signal (PPG).•Low-cost BMP estimation model.•High accuracy hybrid artificial intelligence-based BMP prediction.
Background and objective: Muscle mass is one of the critical components that ensure muscle function. Loss of muscle mass at every stage of life can cause many adverse effects. Sarcopenia, which can occur in different age groups and is characterized by a decrease in muscle mass, is a critical syndrome that affects the quality of life of individuals. Aging, a universal process, can also cause loss of muscle mass. It is essential to monitor and measure muscle mass, which should be sufficient to maintain optimal health. Having various disadvantages with the ordinary methods used to estimate muscle mass increases the need for the new high technology methods. This study aims to develop a low-cost and trustworthy Body Muscle Percentage calculation model based on artificial intelligence algorithms and biomedical signals.
Methods: For the study, 327 photoplethysmography signals of the subject were used. First, the photoplethysmography signals were filtered, and sub-frequency bands were obtained. A quantity of 125 time-domain features, 25 from each signal, have been extracted. Additionally, it has reached 130 features in demographic features added to the model. To enhance the performance, the spearman feature selection algorithm was used. Decision trees, Support Vector Machines, Ensemble Decision Trees, and Hybrid machine learning algorithms (the combination of three methods) were used as machine learning algorithms.
Results: The recommended Body Muscle Percentage estimation model have the perfomance values for all individuals R=0.95, for males R=0.90 and for females R=0.90 in this study.
Conclusion: Regarding the study results, it is thought that photoplethysmography-based models can be used to predict body muscle percentage. |
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| AbstractList | •Artificial intelligence-based prediction model for body muscle percentage (BMP).•Gender-specific BMP prediction model.•BMP prediction model with photoplethysmography signal (PPG).•Low-cost BMP estimation model.•High accuracy hybrid artificial intelligence-based BMP prediction.
Background and objective: Muscle mass is one of the critical components that ensure muscle function. Loss of muscle mass at every stage of life can cause many adverse effects. Sarcopenia, which can occur in different age groups and is characterized by a decrease in muscle mass, is a critical syndrome that affects the quality of life of individuals. Aging, a universal process, can also cause loss of muscle mass. It is essential to monitor and measure muscle mass, which should be sufficient to maintain optimal health. Having various disadvantages with the ordinary methods used to estimate muscle mass increases the need for the new high technology methods. This study aims to develop a low-cost and trustworthy Body Muscle Percentage calculation model based on artificial intelligence algorithms and biomedical signals.
Methods: For the study, 327 photoplethysmography signals of the subject were used. First, the photoplethysmography signals were filtered, and sub-frequency bands were obtained. A quantity of 125 time-domain features, 25 from each signal, have been extracted. Additionally, it has reached 130 features in demographic features added to the model. To enhance the performance, the spearman feature selection algorithm was used. Decision trees, Support Vector Machines, Ensemble Decision Trees, and Hybrid machine learning algorithms (the combination of three methods) were used as machine learning algorithms.
Results: The recommended Body Muscle Percentage estimation model have the perfomance values for all individuals R=0.95, for males R=0.90 and for females R=0.90 in this study.
Conclusion: Regarding the study results, it is thought that photoplethysmography-based models can be used to predict body muscle percentage. Muscle mass is one of the critical components that ensure muscle function. Loss of muscle mass at every stage of life can cause many adverse effects. Sarcopenia, which can occur in different age groups and is characterized by a decrease in muscle mass, is a critical syndrome that affects the quality of life of individuals. Aging, a universal process, can also cause loss of muscle mass. It is essential to monitor and measure muscle mass, which should be sufficient to maintain optimal health. Having various disadvantages with the ordinary methods used to estimate muscle mass increases the need for the new high technology methods. This study aims to develop a low-cost and trustworthy Body Muscle Percentage calculation model based on artificial intelligence algorithms and biomedical signals.BACKGROUND AND OBJECTIVEMuscle mass is one of the critical components that ensure muscle function. Loss of muscle mass at every stage of life can cause many adverse effects. Sarcopenia, which can occur in different age groups and is characterized by a decrease in muscle mass, is a critical syndrome that affects the quality of life of individuals. Aging, a universal process, can also cause loss of muscle mass. It is essential to monitor and measure muscle mass, which should be sufficient to maintain optimal health. Having various disadvantages with the ordinary methods used to estimate muscle mass increases the need for the new high technology methods. This study aims to develop a low-cost and trustworthy Body Muscle Percentage calculation model based on artificial intelligence algorithms and biomedical signals.For the study, 327 photoplethysmography signals of the subject were used. First, the photoplethysmography signals were filtered, and sub-frequency bands were obtained. A quantity of 125 time-domain features, 25 from each signal, have been extracted. Additionally, it has reached 130 features in demographic features added to the model. To enhance the performance, the spearman feature selection algorithm was used. Decision trees, Support Vector Machines, Ensemble Decision Trees, and Hybrid machine learning algorithms (the combination of three methods) were used as machine learning algorithms.METHODSFor the study, 327 photoplethysmography signals of the subject were used. First, the photoplethysmography signals were filtered, and sub-frequency bands were obtained. A quantity of 125 time-domain features, 25 from each signal, have been extracted. Additionally, it has reached 130 features in demographic features added to the model. To enhance the performance, the spearman feature selection algorithm was used. Decision trees, Support Vector Machines, Ensemble Decision Trees, and Hybrid machine learning algorithms (the combination of three methods) were used as machine learning algorithms.The recommended Body Muscle Percentage estimation model have the perfomance values for all individuals R=0.95, for males R=0.90 and for females R=0.90 in this study.RESULTSThe recommended Body Muscle Percentage estimation model have the perfomance values for all individuals R=0.95, for males R=0.90 and for females R=0.90 in this study.Regarding the study results, it is thought that photoplethysmography-based models can be used to predict body muscle percentage.CONCLUSIONRegarding the study results, it is thought that photoplethysmography-based models can be used to predict body muscle percentage. |
| ArticleNumber | 107010 |
| Author | Bozkurt, Mehmet Recep Uçar, Kübra Uçar, Muhammed Kürşad Uçar, Zeliha |
| Author_xml | – sequence: 1 givenname: Muhammed Kürşad orcidid: 0000-0002-0636-8645 surname: Uçar fullname: Uçar, Muhammed Kürşad organization: Sakarya University, Faculty of Engineering, Electrical-Electronics Engineering, Serdivan, Sakarya 54187, Turkey – sequence: 2 givenname: Kübra surname: Uçar fullname: Uçar, Kübra email: kubraucar@hacettepe.edu.tr organization: Hacettepe University, Faculty of Health Sciences, Department of Nutrition and Dietetics, Sihhiye, Ankara 06100, Turkey – sequence: 3 givenname: Zeliha surname: Uçar fullname: Uçar, Zeliha email: zelihaguvenc@hotmail.com organization: Istanbul Okan University, Institute of Health Sciences, Nutrition and Dietetics, Mecidiyekoy, Istanbul 34394, Turkey – sequence: 4 givenname: Mehmet Recep surname: Bozkurt fullname: Bozkurt, Mehmet Recep email: mbozkurt@sakarya.edu.tr organization: Sakarya University, Faculty of Engineering, Electrical-Electronics Engineering, Serdivan, Sakarya 54187, Turkey |
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| Keywords | Gender-based body muscle percentage Photoplethysmography signal Artificial intelligence Body muscle percentage Body composition Machine learning |
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| Snippet | •Artificial intelligence-based prediction model for body muscle percentage (BMP).•Gender-specific BMP prediction model.•BMP prediction model with... Muscle mass is one of the critical components that ensure muscle function. Loss of muscle mass at every stage of life can cause many adverse effects.... |
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| SubjectTerms | Artificial intelligence Body composition Body muscle percentage Gender-based body muscle percentage Machine learning Photoplethysmography signal |
| Title | Determination gender-based hybrid artificial intelligence of body muscle percentage by photoplethysmography signal |
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