J. Semicond. > Volume 38 > Issue 10 > Article Number: 105003

The energy-efficient implementation of an adaptive-filtering-based QRS complex detection method for wearable devices

Shudong Tian , Jun Han , , Jianwei Yang and Xiaoyang Zeng

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Abstract: Electrocardiogram (ECG) can be used as a valid way for diagnosing heart disease. To fulfill ECG processing in wearable devices by reducing computation complexity and hardware cost, two kinds of adaptive filters are designed to perform QRS complex detection and motion artifacts removal, respectively. The proposed design achieves a sensitivity of 99.49% and a positive predictivity of 99.72%, tested under the MIT-BIH ECG database. The proposed design is synthesized under the SMIC 65-nm CMOS technology and verified by post-synthesis simulation. Experimental results show that the power consumption and area cost of this design are of 160 μW and 1.09 × 10 5 μm2, respectively.

Key words: ECGadaptive filterQRSmotion artifactsR wave detection

Abstract: Electrocardiogram (ECG) can be used as a valid way for diagnosing heart disease. To fulfill ECG processing in wearable devices by reducing computation complexity and hardware cost, two kinds of adaptive filters are designed to perform QRS complex detection and motion artifacts removal, respectively. The proposed design achieves a sensitivity of 99.49% and a positive predictivity of 99.72%, tested under the MIT-BIH ECG database. The proposed design is synthesized under the SMIC 65-nm CMOS technology and verified by post-synthesis simulation. Experimental results show that the power consumption and area cost of this design are of 160 μW and 1.09 × 10 5 μm2, respectively.

Key words: ECGadaptive filterQRSmotion artifactsR wave detection



References:

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[2]

Pan J, Tompkins W J. A real-time QRS detection algorithm[J]. IEEE Trans Biomed Eng, 1985, BME-32(3): 230.

[3]

Vijaya G, Kumar V, Verma H K. Artificial neural network based wave complex detection in electrocardiograms[J]. Int J Syst Sci, 1997, 28(2): 125.

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Zou Y, Han J, Weng X. An ultra-low power QRS complex detection algorithm based on down-sampling wavelet transform[J]. IEEE Signal Process Lett, 2013, 20(5): 515.

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Singh O, Sunkaria R K. The utility of wavelet packet transform in QRS complex detection—a comparative study of different mother wavelets[J]. International Conference on Computing for Sustainable Global Development, 2015: 1942.

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Abdelliche F, Charef A, Ladaci S. Complex fractional and complex Morlet wavelets for QRS complex detection[J]. International Conference on Fractional Differentiation and ITS Applications, 2014: 1.

[7]

Haritha C, Ganesan M, Sumesh E P. A survey on modern trends in ECG noise removal techniques[J]. International Conference on Circuit, Power and Computing Technologies, 2016: 1.

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Priya M. MATLAB based ECG signal noise removal and its analysis[J]. International Conference on Recent Advances in Engineering & Computational Sciences, 2015: 1.

[9]

Goel S, Kaur G, Tomar P. Performance analysis of Welch and Blackman Nuttall window for noise reduction of ECG[J]. International Conference on Signal Processing, Computing and Control, 2015: 87.

[10]

Deepu C J, Lian Y. A joint QRS detection and data compression scheme for wearable sensors[J]. IEEE Trans Biomed Eng, 2015, 62(1): 165.

[11]

Shweta J, Anil K, Varun B. Technique for QRS complex detection using particle swarm optimization[J]. IET Sci Meas Technol, 2016, 10(6): 626.

[12]

Berset T, Geng D, Romero I. An optimized DSP implementation of adaptive filtering and ICA for motion artifact reduction in ambulatory ECG monitoring[J]. International Conference of the IEEE Engineering in Medicine & Biology Society IEEE Engineering in Medicine & Biology Society Conference, 2012: 6496.

[13]

Sayed A. Adaptive filters. Wiley-IEEE Press, 2008

[14]

Schafer R W. What is a Savitzky–Golay filter[J]. Signal Process Mag IEEE, 2011, 28(4): 111.

[15]

Chen S W, Chen H C, Chan H L. A real-time QRS detection method based on moving-averaging incorporating with wavelet denoising[J]. Comput Meth Prog Biomed, 2006, 82(3): 187.

[16]

Poli R, Cagnoni S, Valli G. Genetic design of optimum linear and nonlinear QRS detectors[J]. IEEE Trans Biomed Eng, 1995, 42(11): 1137.

[17]

Afonso V X, Tompkins W J, Nguyen T Q. ECG beat detection using filter banks[J]. IEEE Trans Biomed Eng, 1999, 46(2): 192.

[18]

Hamilton P S, Tompkins W J. Quantitative investigation of QRS detection rules using the MIT/BIH arrhythmia database[J]. IEEE Trans Biomed Eng, 1986, BME-33(12): 1157.

[19]

Zhang F, Lian Y. QRS detection based on multiscale mathematical morphology for wearable ECG devices in body area networks[J]. IEEE Trans Biomed Circuits Syst, 2009, 3(4): 220.

[20]

Ieong C I, Mak P I, Lam C P. A 0.83-μW QRS detection processor using quadratic spline wavelet transform for wireless ECG acquisition in 0.35-μm CMOS[J]. IEEE Trans Biomed Circuits Syst, 2012, 6(6): 586.

[21]

Martinez J P, Almeida R, Olmos S. A wavelet-based ECG delineator: evaluation on standard databases[J]. IEEE Trans Biomed Eng, 2004, 51(4): 570.

[1]

Bayasi N, Saleh H, Mohammad B. 65-nm ASIC implementation of QRS detector based on Pan and Tompkins algorithm[J]. International Conference on Innovations in Information Technology, 2014: 84.

[2]

Pan J, Tompkins W J. A real-time QRS detection algorithm[J]. IEEE Trans Biomed Eng, 1985, BME-32(3): 230.

[3]

Vijaya G, Kumar V, Verma H K. Artificial neural network based wave complex detection in electrocardiograms[J]. Int J Syst Sci, 1997, 28(2): 125.

[4]

Zou Y, Han J, Weng X. An ultra-low power QRS complex detection algorithm based on down-sampling wavelet transform[J]. IEEE Signal Process Lett, 2013, 20(5): 515.

[5]

Singh O, Sunkaria R K. The utility of wavelet packet transform in QRS complex detection—a comparative study of different mother wavelets[J]. International Conference on Computing for Sustainable Global Development, 2015: 1942.

[6]

Abdelliche F, Charef A, Ladaci S. Complex fractional and complex Morlet wavelets for QRS complex detection[J]. International Conference on Fractional Differentiation and ITS Applications, 2014: 1.

[7]

Haritha C, Ganesan M, Sumesh E P. A survey on modern trends in ECG noise removal techniques[J]. International Conference on Circuit, Power and Computing Technologies, 2016: 1.

[8]

Priya M. MATLAB based ECG signal noise removal and its analysis[J]. International Conference on Recent Advances in Engineering & Computational Sciences, 2015: 1.

[9]

Goel S, Kaur G, Tomar P. Performance analysis of Welch and Blackman Nuttall window for noise reduction of ECG[J]. International Conference on Signal Processing, Computing and Control, 2015: 87.

[10]

Deepu C J, Lian Y. A joint QRS detection and data compression scheme for wearable sensors[J]. IEEE Trans Biomed Eng, 2015, 62(1): 165.

[11]

Shweta J, Anil K, Varun B. Technique for QRS complex detection using particle swarm optimization[J]. IET Sci Meas Technol, 2016, 10(6): 626.

[12]

Berset T, Geng D, Romero I. An optimized DSP implementation of adaptive filtering and ICA for motion artifact reduction in ambulatory ECG monitoring[J]. International Conference of the IEEE Engineering in Medicine & Biology Society IEEE Engineering in Medicine & Biology Society Conference, 2012: 6496.

[13]

Sayed A. Adaptive filters. Wiley-IEEE Press, 2008

[14]

Schafer R W. What is a Savitzky–Golay filter[J]. Signal Process Mag IEEE, 2011, 28(4): 111.

[15]

Chen S W, Chen H C, Chan H L. A real-time QRS detection method based on moving-averaging incorporating with wavelet denoising[J]. Comput Meth Prog Biomed, 2006, 82(3): 187.

[16]

Poli R, Cagnoni S, Valli G. Genetic design of optimum linear and nonlinear QRS detectors[J]. IEEE Trans Biomed Eng, 1995, 42(11): 1137.

[17]

Afonso V X, Tompkins W J, Nguyen T Q. ECG beat detection using filter banks[J]. IEEE Trans Biomed Eng, 1999, 46(2): 192.

[18]

Hamilton P S, Tompkins W J. Quantitative investigation of QRS detection rules using the MIT/BIH arrhythmia database[J]. IEEE Trans Biomed Eng, 1986, BME-33(12): 1157.

[19]

Zhang F, Lian Y. QRS detection based on multiscale mathematical morphology for wearable ECG devices in body area networks[J]. IEEE Trans Biomed Circuits Syst, 2009, 3(4): 220.

[20]

Ieong C I, Mak P I, Lam C P. A 0.83-μW QRS detection processor using quadratic spline wavelet transform for wireless ECG acquisition in 0.35-μm CMOS[J]. IEEE Trans Biomed Circuits Syst, 2012, 6(6): 586.

[21]

Martinez J P, Almeida R, Olmos S. A wavelet-based ECG delineator: evaluation on standard databases[J]. IEEE Trans Biomed Eng, 2004, 51(4): 570.

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S D Tian, J Han, J W Yang, X Y Zeng. The energy-efficient implementation of an adaptive-filtering-based QRS complex detection method for wearable devices[J]. J. Semicond., 2017, 38(10): 105003. doi: 10.1088/1674-4926/38/10/105003.

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History

Manuscript received: 31 March 2017 Manuscript revised: 23 July 2017 Online: Accepted Manuscript: 13 November 2017 Published: 01 October 2017

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