A Low-Complexity ECG Feature Extraction Algorithm for Mobile Healthcare Applications
This paper introduces a low-complexity algorithm for the extraction of the fiducial points from the electrocardiogram (ECG). The application area we consider is that of remote cardiovascular monitoring, where continuous sensing and processing takes place in low-power, computationally constrained dev...
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| Vydáno v: | IEEE journal of biomedical and health informatics Ročník 17; číslo 2; s. 459 - 469 |
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| Hlavní autoři: | , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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United States
IEEE
01.03.2013
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Témata: | |
| ISSN: | 2168-2194, 2168-2208, 2168-2208 |
| On-line přístup: | Získat plný text |
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| Abstract | This paper introduces a low-complexity algorithm for the extraction of the fiducial points from the electrocardiogram (ECG). The application area we consider is that of remote cardiovascular monitoring, where continuous sensing and processing takes place in low-power, computationally constrained devices, thus the power consumption and complexity of the processing algorithms should remain at a minimum level. Under this context, we choose to employ the discrete wavelet transform (DWT) with the Haar function being the mother wavelet, as our principal analysis method. From the modulus-maxima analysis on the DWT coefficients, an approximation of the ECG fiducial points is extracted. These initial findings are complimented with a refinement stage, based on the time-domain morphological properties of the ECG, which alleviates the decreased temporal resolution of the DWT. The resulting algorithm is a hybrid scheme of time- and frequency-domain signal processing. Feature extraction results from 27 ECG signals from QTDB were tested against manual annotations and used to compare our approach against the state-of-the art ECG delineators. In addition, 450 signals from the 15-lead PTBDB are used to evaluate the obtained performance against the CSE tolerance limits. Our findings indicate that all but one CSE limits are satisfied. This level of performance combined with a complexity analysis, where the upper bound of the proposed algorithm, in terms of arithmetic operations, is calculated as 2.423 N +214 additions and 1.093 N +12 multiplications for N ≤ 861 or 2.553 N +102 additions and 1.093 N +10 multiplications for N > 861 ( N being the number of input samples), reveals that the proposed method achieves an ideal tradeoff between computational complexity and performance, a key requirement in remote cardiovascular disease monitoring systems. |
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| AbstractList | This paper introduces a low-complexity algorithm for the extraction of the fiducial points from the electrocardiogram (ECG). The application area we consider is that of remote cardiovascular monitoring, where continuous sensing and processing takes place in low-power, computationally constrained devices, thus the power consumption and complexity of the processing algorithms should remain at a minimum level. Under this context, we choose to employ the discrete wavelet transform (DWT) with the Haar function being the mother wavelet, as our principal analysis method. From the modulus-maxima analysis on the DWT coefficients, an approximation of the ECG fiducial points is extracted. These initial findings are complimented with a refinement stage, based on the time-domain morphological properties of the ECG, which alleviates the decreased temporal resolution of the DWT. The resulting algorithm is a hybrid scheme of time- and frequency-domain signal processing. Feature extraction results from 27 ECG signals from QTDB were tested against manual annotations and used to compare our approach against the state-of-the art ECG delineators. In addition, 450 signals from the 15-lead PTBDB are used to evaluate the obtained performance against the CSE tolerance limits. Our findings indicate that all but one CSE limits are satisfied. This level of performance combined with a complexity analysis, where the upper bound of the proposed algorithm, in terms of arithmetic operations, is calculated as hbox 2.423 N + hbox 214 additions and hbox 1.093 N + hbox 12 multiplications for N less than or equal to hbox 861 or hbox 2.553 N + hbox 102 additions and hbox 1.093 N + hbox 10 multiplications for syntax error at token & ( N being the number of input samples), reveals that the proposed method achieves an ideal tradeoff between computational complexity and performance, a key requirement in remote cardiovascular disease monitoring systems. This paper introduces a low-complexity algorithm for the extraction of the fiducial points from the Electrocardiogram (ECG). The application area we consider is that of remote cardiovascular monitoring, where continuous sensing and processing takes place in low-power, computationally constrained devices, thus the power consumption and complexity of the processing algorithms should remain at a minimum level. Under this context, we choose to employ the Discrete Wavelet Transform (DWT) with the Haar function being the mother wavelet, as our principal analysis method. From the modulus-maxima analysis on the DWT coefficients, an approximation of the ECG fiducial points is extracted. These initial findings are complimented with a refinement stage, based on the time-domain morphological properties of the ECG, which alleviates the decreased temporal resolution of the DWT. The resulting algorithm is a hybrid scheme of time and frequency domain signal processing. Feature extraction results from 27 ECG signals from QTDB, were tested against manual annotations and used to compare our approach against the state-of-the art ECG delineators. In addition, 450 signals from the 15-lead PTBDB are used to evaluate the obtained performance against the CSE tolerance limits. Our findings indicate that all but one CSE limits are satisfied. This level of performance combined with a complexity analysis, where the upper bound of the proposed algorithm, in terms of arithmetic operations, is calculated as 2:423N + 214 additions and 1:093N + 12 multiplications for N 861 or 2:553N + 102 additions and 1:093N +10 multiplications for N > 861 (N being the number of input samples), reveals that the proposed method achieves an ideal trade-off between computational complexity and performance, a key requirement in remote CVD monitoring systems.This paper introduces a low-complexity algorithm for the extraction of the fiducial points from the Electrocardiogram (ECG). The application area we consider is that of remote cardiovascular monitoring, where continuous sensing and processing takes place in low-power, computationally constrained devices, thus the power consumption and complexity of the processing algorithms should remain at a minimum level. Under this context, we choose to employ the Discrete Wavelet Transform (DWT) with the Haar function being the mother wavelet, as our principal analysis method. From the modulus-maxima analysis on the DWT coefficients, an approximation of the ECG fiducial points is extracted. These initial findings are complimented with a refinement stage, based on the time-domain morphological properties of the ECG, which alleviates the decreased temporal resolution of the DWT. The resulting algorithm is a hybrid scheme of time and frequency domain signal processing. Feature extraction results from 27 ECG signals from QTDB, were tested against manual annotations and used to compare our approach against the state-of-the art ECG delineators. In addition, 450 signals from the 15-lead PTBDB are used to evaluate the obtained performance against the CSE tolerance limits. Our findings indicate that all but one CSE limits are satisfied. This level of performance combined with a complexity analysis, where the upper bound of the proposed algorithm, in terms of arithmetic operations, is calculated as 2:423N + 214 additions and 1:093N + 12 multiplications for N 861 or 2:553N + 102 additions and 1:093N +10 multiplications for N > 861 (N being the number of input samples), reveals that the proposed method achieves an ideal trade-off between computational complexity and performance, a key requirement in remote CVD monitoring systems. This paper introduces a low-complexity algorithm for the extraction of the fiducial points from the Electrocardiogram (ECG). The application area we consider is that of remote cardiovascular monitoring, where continuous sensing and processing takes place in low-power, computationally constrained devices, thus the power consumption and complexity of the processing algorithms should remain at a minimum level. Under this context, we choose to employ the Discrete Wavelet Transform (DWT) with the Haar function being the mother wavelet, as our principal analysis method. From the modulus-maxima analysis on the DWT coefficients, an approximation of the ECG fiducial points is extracted. These initial findings are complimented with a refinement stage, based on the time-domain morphological properties of the ECG, which alleviates the decreased temporal resolution of the DWT. The resulting algorithm is a hybrid scheme of time and frequency domain signal processing. Feature extraction results from 27 ECG signals from QTDB, were tested against manual annotations and used to compare our approach against the state-of-the art ECG delineators. In addition, 450 signals from the 15-lead PTBDB are used to evaluate the obtained performance against the CSE tolerance limits. Our findings indicate that all but one CSE limits are satisfied. This level of performance combined with a complexity analysis, where the upper bound of the proposed algorithm, in terms of arithmetic operations, is calculated as 2:423N + 214 additions and 1:093N + 12 multiplications for N 861 or 2:553N + 102 additions and 1:093N +10 multiplications for N > 861 (N being the number of input samples), reveals that the proposed method achieves an ideal trade-off between computational complexity and performance, a key requirement in remote CVD monitoring systems. This paper introduces a low-complexity algorithm for the extraction of the fiducial points from the electrocardiogram (ECG). The application area we consider is that of remote cardiovascular monitoring, where continuous sensing and processing takes place in low-power, computationally constrained devices, thus the power consumption and complexity of the processing algorithms should remain at a minimum level. Under this context, we choose to employ the discrete wavelet transform (DWT) with the Haar function being the mother wavelet, as our principal analysis method. From the modulus-maxima analysis on the DWT coefficients, an approximation of the ECG fiducial points is extracted. These initial findings are complimented with a refinement stage, based on the time-domain morphological properties of the ECG, which alleviates the decreased temporal resolution of the DWT. The resulting algorithm is a hybrid scheme of time- and frequency-domain signal processing. Feature extraction results from 27 ECG signals from QTDB were tested against manual annotations and used to compare our approach against the state-of-the art ECG delineators. In addition, 450 signals from the 15-lead PTBDB are used to evaluate the obtained performance against the CSE tolerance limits. Our findings indicate that all but one CSE limits are satisfied. This level of performance combined with a complexity analysis, where the upper bound of the proposed algorithm, in terms of arithmetic operations, is calculated as 2.423 N +214 additions and 1.093 N +12 multiplications for N ≤ 861 or 2.553 N +102 additions and 1.093 N +10 multiplications for N > 861 ( N being the number of input samples), reveals that the proposed method achieves an ideal tradeoff between computational complexity and performance, a key requirement in remote cardiovascular disease monitoring systems. This paper introduces a low-complexity algorithm for the extraction of the fiducial points from the electrocardiogram (ECG). The application area we consider is that of remote cardiovascular monitoring, where continuous sensing and processing takes place in low-power, computationally constrained devices, thus the power consumption and complexity of the processing algorithms should remain at a minimum level. Under this context, we choose to employ the discrete wavelet transform (DWT) with the Haar function being the mother wavelet, as our principal analysis method. From the modulus-maxima analysis on the DWT coefficients, an approximation of the ECG fiducial points is extracted. These initial findings are complimented with a refinement stage, based on the time-domain morphological properties of the ECG, which alleviates the decreased temporal resolution of the DWT. The resulting algorithm is a hybrid scheme of time- and frequency-domain signal processing. Feature extraction results from 27 ECG signals from QTDB were tested against manual annotations and used to compare our approach against the state-of-the art ECG delineators. In addition, 450 signals from the 15-lead PTBDB are used to evaluate the obtained performance against the CSE tolerance limits. Our findings indicate that all but one CSE limits are satisfied. This level of performance combined with a complexity analysis, where the upper bound of the proposed algorithm, in terms of arithmetic operations, is calculated as [Formula Omitted] additions and [Formula Omitted] multiplications for [Formula Omitted] or [Formula Omitted] additions and [Formula Omitted] multiplications for [Formula Omitted] ( [Formula Omitted] being the number of input samples), reveals that the proposed method achieves an ideal tradeoff between computational complexity and performance, a key requirement in remote cardiovascular disease monitoring systems. |
| Author | Maharatna, K. Rosengarten, J. Acharyya, A. Biswas, D. Taihai Chen Mazomenos, E. B. Curzen, N. Morgan, J. |
| Author_xml | – sequence: 1 givenname: E. B. surname: Mazomenos fullname: Mazomenos, E. B. email: ebm@ecs.soton.ac.uk organization: Sch. of Electron. & Comput. Sci., Univ. of Southampton, Southampton, UK – sequence: 2 givenname: D. surname: Biswas fullname: Biswas, D. email: db9g10@ecs.soton.ac.uk organization: Sch. of Electron. & Comput. Sci., Univ. of Southampton, Southampton, UK – sequence: 3 givenname: A. surname: Acharyya fullname: Acharyya, A. email: amit_acharyya@iith.ac.in organization: Dept. of Electr. Eng., Indian Inst. of Technol., Hyderabad, Hyderabad, India – sequence: 4 surname: Taihai Chen fullname: Taihai Chen email: tc10g09@ecs.soton.ac.uk organization: Sch. of Electron. & Comput. Sci., Univ. of Southampton, Southampton, UK – sequence: 5 givenname: K. surname: Maharatna fullname: Maharatna, K. email: km3@ecs.soton.ac.uk organization: Sch. of Electron. & Comput. Sci., Univ. of Southampton, Southampton, UK – sequence: 6 givenname: J. surname: Rosengarten fullname: Rosengarten, J. email: james@rosengarten.co.uk organization: Southampton Univ. Hosp. NHS Trust, Southampton, UK – sequence: 7 givenname: J. surname: Morgan fullname: Morgan, J. email: jmm@hrclinic.org organization: Southampton Univ. Hosp. NHS Trust, Southampton, UK – sequence: 8 givenname: N. surname: Curzen fullname: Curzen, N. email: nick.curzen@suht.swest.nhs.uk organization: Southampton Univ. Hosp. NHS Trust, Southampton, UK |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/23362250$$D View this record in MEDLINE/PubMed |
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| References_xml | – ident: ref20 doi: 10.1109/IEMBS.2000.897966 – ident: ref16 doi: 10.1109/TBME.2006.877103 – ident: ref8 doi: 10.1007/BF02442095 – year: 0 ident: ref22 – year: 0 ident: ref12 – ident: ref17 doi: 10.1109/CSSE.2008.1096 – ident: ref4 doi: 10.1109/TITB.2011.2163943 – ident: ref29 doi: 10.1109/IEMBS.2005.1615314 – ident: ref24 doi: 10.1109/10.362922 – ident: ref7 doi: 10.1007/BF02442378 – year: 0 ident: ref2 – ident: ref11 doi: 10.1016/0010-4809(87)90024-3 – year: 0 ident: ref3 – year: 0 ident: ref1 – volume: 6 start-page: 815 year: 1985 ident: ref5 article-title: The CSE working party, "Recommendations for measurement standards in quantitative electrocardiography publication-title: Eur Heart J – ident: ref34 doi: 10.1109/TBME.2003.821031 – ident: ref33 doi: 10.1109/CIC.2000.898460 – ident: ref35 doi: 10.1109/JSEN.2010.2045498 – year: 0 ident: ref6 – volume: 1 start-page: 289 year: 1997 ident: ref18 article-title: ECG events detection and classification using wavelet and neural networks publication-title: Proc IEEE Eng Med Biol Soc – volume: 2 start-page: 1051 year: 1995 ident: ref26 article-title: ECG fiducial points detection through wavelet transform publication-title: Proc IEEE Eng Med Biol Soc doi: 10.1109/IEMBS.1995.579466 – ident: ref21 doi: 10.1109/IEMBS.2003.1280402 – ident: ref10 doi: 10.1109/TBME.1986.325695 – volume: 22 start-page: 22 year: 2008 ident: ref23 article-title: Supervised ECG delineation using the wavelet transform and hidden Markov models publication-title: Proc Conf Int Federation Med Biol Eng doi: 10.1007/978-3-540-89208-3_7 – ident: ref9 doi: 10.1109/TBME.1985.325532 – volume: 1 start-page: 22 year: 2008 ident: ref32 article-title: Wavelet transform based ECG characteristic points detector publication-title: Proc Int Sci Conf Comp Sci – ident: ref15 doi: 10.1109/10.704877 – ident: ref19 doi: 10.1109/IEMBS.1999.802343 – start-page: 1 year: 2007 ident: ref31 article-title: Discrete wavelet transform in automatic ECG signal analysis publication-title: Proc IEEE Instrum Meas Technol Conf – ident: ref30 doi: 10.1109/CESA.2006.4281639 – volume: 3 start-page: 929 year: 1996 ident: ref14 article-title: Detection of ECG waveforms by using artificial neural networks publication-title: Proc IEEE Eng Med Biol Soc doi: 10.1109/IEMBS.1996.652646 – ident: ref28 doi: 10.1016/S0169-2607(97)01780-X – ident: ref37 doi: 10.1016/j.jelectrocard.2005.04.003 – ident: ref13 doi: 10.1016/0165-1684(88)90069-2 – ident: ref27 doi: 10.1109/51.566158 – ident: ref38 doi: 10.1006/cbmr.1994.1006 – ident: ref36 doi: 10.1109/CIC.1997.648140 – ident: ref25 doi: 10.1109/ACSSC.1992.269273 – volume: 4 start-page: 1399 year: 1996 ident: ref39 article-title: Automatic measurement of the QRS onset and offset in individual ECG leads publication-title: Proc IEEE Eng Med Biol Soc doi: 10.1109/IEMBS.1996.647474 |
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| Snippet | This paper introduces a low-complexity algorithm for the extraction of the fiducial points from the electrocardiogram (ECG). The application area we consider... This paper introduces a low-complexity algorithm for the extraction of the fiducial points from the Electrocardiogram (ECG). The application area we consider... |
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| SubjectTerms | Algorithm design and analysis Algorithms Databases, Factual Discrete wavelet transform (DWT) Discrete wavelet transforms electrocardiogram (ECG) feature extraction Electrocardiography Electrocardiography - methods Feature extraction Humans low complexity algorithm mobile healthcare Monitoring Monitoring systems Noise Signal processing Signal processing algorithms Studies Wavelet Analysis |
| Title | A Low-Complexity ECG Feature Extraction Algorithm for Mobile Healthcare Applications |
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