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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Published in:	Sensors (Basel, Switzerland) Vol. 19; no. 4; p. 775
Main Authors:	Aspuru, Javier, Ochoa-Brust, Alberto, Félix, Ramón A., Mata-López, Walter, Mena, Luis J., Ostos, Rodolfo, Martínez-Peláez, Rafael
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI 14.02.2019 MDPI AG
Subjects:	Algorithms Digital Signal Processing ECG Sensor Electrocardiography - methods Heart - diagnostic imaging Heart - physiology Humans identification waves Linear Models Linear Regression Algorithm segmentation Signal Processing, Computer-Assisted Digital Signal Processing segmentation Linear Regression Algorithm ECG Sensor identification waves
ISSN:	1424-8220, 1424-8220
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Abstract	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.
AbstractList	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. 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.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.
Author	Aspuru, Javier Félix, Ramón A. Ochoa-Brust, Alberto Martínez-Peláez, Rafael Mata-López, Walter Mena, Luis J. Ostos, Rodolfo
AuthorAffiliation	2 Academic Unit of Computing, Master Program in Applied Sciences, Polytechnic University of Sinaloa, Mazatlan 82199, Mexico; lmena@upsin.edu.mx (L.J.M.); rostos@upsin.edu.mx (R.O.) 3 Faculty of Information Technology, University of La Salle-Bajio, Av. Universidad #602, Leon 37150, Guanajuato, Mexico; rmartinezp@delasalle.edu.mx 1 Faculty of Mechanical and Electrical Engineering, University of Colima, Av. Universidad #333, Colima 28000, Mexico; jaspuru@ucol.mx (J.A.); rfelix@ucol.mx (R.A.F.); wmata@ucol.mx (W.M.-L.)
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Title	Segmentation of the ECG Signal by Means of a Linear Regression Algorithm
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