Segmentation of the ECG Signal by Means of a Linear Regression Algorithm
The monitoring and processing of electrocardiogram (ECG) beats have been actively studied in recent years: new lines of research have even been developed to analyze ECG signals using mobile devices. Considering these trends, we proposed a simple and low computing cost algorithm to process and analyz...
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| Format: | Article |
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MDPI AG
2019-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/4/775 |
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| author | Javier Aspuru Alberto Ochoa-Brust Ramón A. Félix Walter Mata-López Luis J. Mena Rodolfo Ostos Rafael Martínez-Peláez |
| author_facet | Javier Aspuru Alberto Ochoa-Brust Ramón A. Félix Walter Mata-López Luis J. Mena Rodolfo Ostos Rafael Martínez-Peláez |
| author_sort | Javier Aspuru |
| collection | DOAJ |
| description | The monitoring and processing of electrocardiogram (ECG) beats have been actively studied in recent years: new lines of research have even been developed to analyze ECG signals using mobile devices. Considering these trends, we proposed a simple and low computing cost algorithm to process and analyze an ECG signal. Our approach is based on the use of linear regression to segment the signal, with the goal of detecting the R point of the ECG wave and later, to separate the signal in periods for detecting P, Q, S, and T peaks. After pre-processing of ECG signal to reduce the noise, the algorithm was able to efficiently detect fiducial points, information that is transcendental for diagnosis of heart conditions using machine learning classifiers. When tested on 260 ECG records, the detection approach performed with a Sensitivity of 97.5% for Q-point and 100% for the rest of ECG peaks. Finally, we validated the robustness of our algorithm by developing an ECG sensor to register and transmit the acquired signals to a mobile device in real time. |
| first_indexed | 2024-04-11T13:27:04Z |
| format | Article |
| id | doaj.art-b5cdfa16737a46b091b5d204e82ca1c9 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T13:27:04Z |
| publishDate | 2019-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-b5cdfa16737a46b091b5d204e82ca1c92022-12-22T04:22:01ZengMDPI AGSensors1424-82202019-02-0119477510.3390/s19040775s19040775Segmentation of the ECG Signal by Means of a Linear Regression AlgorithmJavier Aspuru0Alberto Ochoa-Brust1Ramón A. Félix2Walter Mata-López3Luis J. Mena4Rodolfo Ostos5Rafael Martínez-Peláez6Faculty of Mechanical and Electrical Engineering, University of Colima, Av. Universidad #333, Colima 28000, MexicoFaculty of Mechanical and Electrical Engineering, University of Colima, Av. Universidad #333, Colima 28000, MexicoFaculty of Mechanical and Electrical Engineering, University of Colima, Av. Universidad #333, Colima 28000, MexicoFaculty of Mechanical and Electrical Engineering, University of Colima, Av. Universidad #333, Colima 28000, MexicoAcademic Unit of Computing, Master Program in Applied Sciences, Polytechnic University of Sinaloa, Mazatlan 82199, MexicoAcademic Unit of Computing, Master Program in Applied Sciences, Polytechnic University of Sinaloa, Mazatlan 82199, MexicoFaculty of Information Technology, University of La Salle-Bajio, Av. Universidad #602, Leon 37150, Guanajuato, MexicoThe monitoring and processing of electrocardiogram (ECG) beats have been actively studied in recent years: new lines of research have even been developed to analyze ECG signals using mobile devices. Considering these trends, we proposed a simple and low computing cost algorithm to process and analyze an ECG signal. Our approach is based on the use of linear regression to segment the signal, with the goal of detecting the R point of the ECG wave and later, to separate the signal in periods for detecting P, Q, S, and T peaks. After pre-processing of ECG signal to reduce the noise, the algorithm was able to efficiently detect fiducial points, information that is transcendental for diagnosis of heart conditions using machine learning classifiers. When tested on 260 ECG records, the detection approach performed with a Sensitivity of 97.5% for Q-point and 100% for the rest of ECG peaks. Finally, we validated the robustness of our algorithm by developing an ECG sensor to register and transmit the acquired signals to a mobile device in real time.https://www.mdpi.com/1424-8220/19/4/775segmentationDigital Signal ProcessingECG SensorLinear Regression Algorithmidentification waves |
| spellingShingle | Javier Aspuru Alberto Ochoa-Brust Ramón A. Félix Walter Mata-López Luis J. Mena Rodolfo Ostos Rafael Martínez-Peláez Segmentation of the ECG Signal by Means of a Linear Regression Algorithm Sensors segmentation Digital Signal Processing ECG Sensor Linear Regression Algorithm identification waves |
| title | Segmentation of the ECG Signal by Means of a Linear Regression Algorithm |
| title_full | Segmentation of the ECG Signal by Means of a Linear Regression Algorithm |
| title_fullStr | Segmentation of the ECG Signal by Means of a Linear Regression Algorithm |
| title_full_unstemmed | Segmentation of the ECG Signal by Means of a Linear Regression Algorithm |
| title_short | Segmentation of the ECG Signal by Means of a Linear Regression Algorithm |
| title_sort | segmentation of the ecg signal by means of a linear regression algorithm |
| topic | segmentation Digital Signal Processing ECG Sensor Linear Regression Algorithm identification waves |
| url | https://www.mdpi.com/1424-8220/19/4/775 |
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