Precise detection and localization of R-peaks from ECG signals
Heart rate variability (HRV) is derived from the R-R interval, which depends on the precise localization of R-peaks within an electrocardiogram (ECG) signal. However, current algorithm assessment methods prioritize the R-peak detection's sensitivity rather than the precision of pinpointing the...
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AIMS Press
2023-10-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2023848?viewType=HTML |
| _version_ | 1827767720313094144 |
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| author | Diguo Zhai Xinqi Bao Xi Long Taotao Ru Guofu Zhou |
| author_facet | Diguo Zhai Xinqi Bao Xi Long Taotao Ru Guofu Zhou |
| author_sort | Diguo Zhai |
| collection | DOAJ |
| description | Heart rate variability (HRV) is derived from the R-R interval, which depends on the precise localization of R-peaks within an electrocardiogram (ECG) signal. However, current algorithm assessment methods prioritize the R-peak detection's sensitivity rather than the precision of pinpointing the exact R-peak positions. As a result, it is of great value to develop an R-peak detection algorithm with high-precision R-peak localization. This paper introduces a novel R-peak localization algorithm that involves modifications to the well-established Pan-Tompkins (PT) algorithm. The algorithm was implemented as follows. First, the raw ECG signal $ X\left(i\right) $ was band-pass filtered (5–35 Hz) to obtain a preprocessed signal $ Y\left(i\right) $. Second, $ Y\left(i\right) $ was squared to enhance the QRS complex, followed by a 5 Hz low-pass filter to obtain the QRS envelope, which was transformed into a window signal $ W\left(i\right) $ by dynamic threshold with a minimum width of 200 ms to mark the QRS complex. Third, $ Y\left(i\right) $ was used to generate QRS template $ T\left(n\right) $ automatically, and then the R-peak was identified by a template matching process to find the maximum absolute value of all cross-correlation values between $ T\left(n\right) $ and $ Y\left(i\right) $. The proposed algorithm achieved a sensitivity (SE) of 99.78%, a positive prediction value (PPV) of 99.78% and data error rate (DER) of 0.44% in R-peak localization for the MIT-BIH Arrhythmia database. The annotated-detected error (ADE), which represents the error between the annotated R-peak location and the detected R-peak location, was 8.35 ms for the MIT-BIH Arrhythmia database. These results outperformed the results obtained using the classical Pan-Tompkins algorithm which yielded an SE of 98.87%, a PPV of 99.14%, a DER of 1.98% and an ADE of 21.65 ms for the MIT-BIH Arrhythmia database. It can be concluded that the algorithm can precisely detect the location of R-peaks and may have the potential to enhance clinical applications of HRV analysis. |
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| institution | Directory Open Access Journal |
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| language | English |
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| spelling | doaj.art-b02ee0d8260f48e7bc80a8167da1d7ae2023-11-08T01:34:16ZengAIMS PressMathematical Biosciences and Engineering1551-00182023-10-012011191911920810.3934/mbe.2023848Precise detection and localization of R-peaks from ECG signalsDiguo Zhai0Xinqi Bao1Xi Long2Taotao Ru 3Guofu Zhou 41. Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, Institute of Electronic Paper Displays, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China 2. National Center for International Research on Green Optoelectronics, South China Normal University, Guangzhou 510006, China3. Department of Engineering, King's College London, Strand, London, WC2R 2LS, UK4. Department of Electrical Engineering, Eindhoven University of Technology, 5612, AZ, Eindhoven, The Netherlands1. Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, Institute of Electronic Paper Displays, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China 2. National Center for International Research on Green Optoelectronics, South China Normal University, Guangzhou 510006, China1. Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, Institute of Electronic Paper Displays, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China 2. National Center for International Research on Green Optoelectronics, South China Normal University, Guangzhou 510006, ChinaHeart rate variability (HRV) is derived from the R-R interval, which depends on the precise localization of R-peaks within an electrocardiogram (ECG) signal. However, current algorithm assessment methods prioritize the R-peak detection's sensitivity rather than the precision of pinpointing the exact R-peak positions. As a result, it is of great value to develop an R-peak detection algorithm with high-precision R-peak localization. This paper introduces a novel R-peak localization algorithm that involves modifications to the well-established Pan-Tompkins (PT) algorithm. The algorithm was implemented as follows. First, the raw ECG signal $ X\left(i\right) $ was band-pass filtered (5–35 Hz) to obtain a preprocessed signal $ Y\left(i\right) $. Second, $ Y\left(i\right) $ was squared to enhance the QRS complex, followed by a 5 Hz low-pass filter to obtain the QRS envelope, which was transformed into a window signal $ W\left(i\right) $ by dynamic threshold with a minimum width of 200 ms to mark the QRS complex. Third, $ Y\left(i\right) $ was used to generate QRS template $ T\left(n\right) $ automatically, and then the R-peak was identified by a template matching process to find the maximum absolute value of all cross-correlation values between $ T\left(n\right) $ and $ Y\left(i\right) $. The proposed algorithm achieved a sensitivity (SE) of 99.78%, a positive prediction value (PPV) of 99.78% and data error rate (DER) of 0.44% in R-peak localization for the MIT-BIH Arrhythmia database. The annotated-detected error (ADE), which represents the error between the annotated R-peak location and the detected R-peak location, was 8.35 ms for the MIT-BIH Arrhythmia database. These results outperformed the results obtained using the classical Pan-Tompkins algorithm which yielded an SE of 98.87%, a PPV of 99.14%, a DER of 1.98% and an ADE of 21.65 ms for the MIT-BIH Arrhythmia database. It can be concluded that the algorithm can precisely detect the location of R-peaks and may have the potential to enhance clinical applications of HRV analysis.https://www.aimspress.com/article/doi/10.3934/mbe.2023848?viewType=HTMLheart rate variabilityr-peak detectiontemplate matchingdynamic thresholdannotated-detected error |
| spellingShingle | Diguo Zhai Xinqi Bao Xi Long Taotao Ru Guofu Zhou Precise detection and localization of R-peaks from ECG signals Mathematical Biosciences and Engineering heart rate variability r-peak detection template matching dynamic threshold annotated-detected error |
| title | Precise detection and localization of R-peaks from ECG signals |
| title_full | Precise detection and localization of R-peaks from ECG signals |
| title_fullStr | Precise detection and localization of R-peaks from ECG signals |
| title_full_unstemmed | Precise detection and localization of R-peaks from ECG signals |
| title_short | Precise detection and localization of R-peaks from ECG signals |
| title_sort | precise detection and localization of r peaks from ecg signals |
| topic | heart rate variability r-peak detection template matching dynamic threshold annotated-detected error |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2023848?viewType=HTML |
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