Heart Rate Extraction from Photoplethysmography Signal: A Multi Model Machine Learning Approach
The purpose of this research is to estimate the heart rate (HR) from wearable gadgets, for example, fingertip gadgets. As the skin of finger-tip is slight, it is not difficult to separate pulse from that point. An optimistic component in this day, HR checking is Photoplethysmography (PPG). Moreover,...
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| Vydáno v: | 2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) s. 1 - 6 |
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IEEE
26.09.2020
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| Abstract | The purpose of this research is to estimate the heart rate (HR) from wearable gadgets, for example, fingertip gadgets. As the skin of finger-tip is slight, it is not difficult to separate pulse from that point. An optimistic component in this day, HR checking is Photoplethysmography (PPG). Moreover, during physical workout HR extraction precision is truly influenced by clamor and movement artifact (MA). To extract HR variability there are numerous ordinary techniques. In this research, a novel way is utilized to extract HR which is known as a multi-model machine learning technique. In this study, initially training and testing of our developed algorithm is done for various features and various dataset. In addition, separation of noisy and non noisy information is done by K means clustering. Then, the machine gain information from noisy and non noisy dataset. The Linear Regression model is utilized to estimate HR by using dataset. In this study, the feature engineering is also done, as it were, we choose an alternate set of features and know their conduct with our recommended technique and we discover error percentage for each set of features. There were 12 subject from where trial dataset were recorded. The root mean square (RMS) and the mean absolute error of HR was extracted. The lowest absolute mean error we find in this research is 3.06 beats per minute (BPM). |
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| AbstractList | The purpose of this research is to estimate the heart rate (HR) from wearable gadgets, for example, fingertip gadgets. As the skin of finger-tip is slight, it is not difficult to separate pulse from that point. An optimistic component in this day, HR checking is Photoplethysmography (PPG). Moreover, during physical workout HR extraction precision is truly influenced by clamor and movement artifact (MA). To extract HR variability there are numerous ordinary techniques. In this research, a novel way is utilized to extract HR which is known as a multi-model machine learning technique. In this study, initially training and testing of our developed algorithm is done for various features and various dataset. In addition, separation of noisy and non noisy information is done by K means clustering. Then, the machine gain information from noisy and non noisy dataset. The Linear Regression model is utilized to estimate HR by using dataset. In this study, the feature engineering is also done, as it were, we choose an alternate set of features and know their conduct with our recommended technique and we discover error percentage for each set of features. There were 12 subject from where trial dataset were recorded. The root mean square (RMS) and the mean absolute error of HR was extracted. The lowest absolute mean error we find in this research is 3.06 beats per minute (BPM). |
| Author | Karim, A.H.M. Zadidul Hasan, Zahid Bashar, Shikder Shafiul Miah, Md. Sazal |
| Author_xml | – sequence: 1 givenname: Md. Sazal surname: Miah fullname: Miah, Md. Sazal email: sazalmiah94@gmail.com organization: School of Engineering and Technology, Asian Institute of Technology,Khlong Nueng,Thailand – sequence: 2 givenname: Shikder Shafiul surname: Bashar fullname: Bashar, Shikder Shafiul email: bashar96.uap@gmail.com organization: University of Asia Pacific,Department of Electrical and Electronic Engineering,Dhaka,Bangladesh – sequence: 3 givenname: A.H.M. Zadidul surname: Karim fullname: Karim, A.H.M. Zadidul email: zadid@uap-bd.edu organization: University of Asia Pacific,Department of Electrical and Electronic Engineering,Dhaka,Bangladesh – sequence: 4 givenname: Zahid surname: Hasan fullname: Hasan, Zahid email: smxahsan@gmail.com organization: University of Maryland, Baltimore County,Department of Information Systems,Maryland,USA |
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| Snippet | The purpose of this research is to estimate the heart rate (HR) from wearable gadgets, for example, fingertip gadgets. As the skin of finger-tip is slight, it... |
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| SubjectTerms | Acceleration Electrocardiography Feature engineering Feature extraction Heart rate Linear regression Linear Regression algorithm Machine learning Noise measurement Photoplethysmography Root mean square Signal processing |
| Title | Heart Rate Extraction from Photoplethysmography Signal: A Multi Model Machine Learning Approach |
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