J. Semicond. > 2017, Volume 38 > Issue 10 > 105003
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Special Topic on Devices and Circuits for Wearable and IoT Systems

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

+ Author Affiliations

 Corresponding author: Jun Han, Email: junhan@fudan.edu.cn

DOI: 10.1088/1674-4926/38/10/105003

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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: ECG, adaptive filter, QRS, motion artifacts, R wave detection



[1]
Bayasi N, Saleh H, Mohammad B, et al. 65-nm ASIC implementation of QRS detector based on Pan and Tompkins algorithm. International Conference on Innovations in Information Technology, 2014: 84
[2]
Pan J, Tompkins W J. A Real-Time QRS Detection Algorithm. Biomed Engineering IEEE Transactions on, 1985, BME-32(3): 230
[3]
G. VIJAYA, VINOD KUMAR, H. K. VERMA. Artificial neural network based wave complex detection in electrocardiograms. Int J Syst Sci, 1997, 28(2): 125
[4]
Zou Y, Han J, Weng X, et al. An Ultra-Low Power QRS Complex Detection Algorithm Based on Down-Sampling Wavelet Transform. 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. 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. 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. International Conference on Circuit, Power and Computing Technologies. 2016: 1
[8]
Priya, Singh M. MATLAB based ECG signal noise removal and its analysis. 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. 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. IEEE Trans Biomed Eng, 2015, 62(1): 165
[11]
Shweta J, Anil K, Varun B, Technique for QRS Complex Detection using Particle Swarm Optimization. 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. 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. 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. Comput Meth Prog Biomed, 2006, 82(3): 187
[16]
Poli R, Cagnoni S, Valli G. Genetic design of optimum linear and nonlinear QRS detectors. IEEE Trans Biomed Eng, 1995, 42(11): 1137
[17]
Afonso V X, Tompkins W J, Nguyen T Q, et al. ECG beat detection using filter banks. 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. 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. IEEE Trans Biomed Circuits Syst, 2009, 3(4): 220
[20]
Ieong C I, Mak P I, Lam C P, et al. A 0.83- QRS Detection Processor Using Quadratic Spline Wavelet Transform for Wireless ECG Acquisition in 0.35- CMOS. IEEE Trans Biomed Circuits Syst, 2012, 6(6): 586
[21]
Martinez J P, Almeida R, Olmos S, et al. A wavelet-based ECG delineator: evaluation on standard databases. IEEE Trans Bio-medical Eng, 2004, 51(4): 570
Fig. 1.  (Color online) The architecture of the design.

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Fig. 2.  (Color online) The structure of adaptive filter.

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Fig. 3.  (Color online) The filtering result of adaptive filter.

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Fig. 4.  (Color online) The feature generation and enhancement result.

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Fig. 5.  (Color online) R wave detection figure.

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Fig. 6.  The flow chart of R wave detection.

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Fig. 7.  The circuit schematic of adaptive filter based on LMS.

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Fig. 8.  The circuit schematic of adaptive filter based on SSLMS.

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Fig. 9.  (Color online) The hardware structure of R wave detection module.

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Table 1.   Comparison of detection performance with different algorithms.

Detection algorithm Se (%) +P (%)
Wavelet de-noising algorithm[15] 99.55 99.49
Genetic algorithm[16] 99.60 99.51
Filter banks algorithm[17] 99.59 99.56
BPF/search-back algorithm[18] 99.69 99.77
Multiscale morphology algorithm[19] 99.81 99.80
Spline wavelet algorithm[20] 99.31 99.70
Wavelet delineation algorithm[21] 99.66 99.56
Adaptive prediction algorithm[10] 99.64 99.81
Adaptive prediction based on PSO[11] 99.75 99.83
This design 99.49 99.72
DownLoad: CSV

Table 2.   The hardware information of the whole design.

Parameter Value
Process SMIC 65 nm
Area 1.09 × 10 5 μm2
Power 160 μW
Frequency 100 kHz
DownLoad: CSV
[1]
Bayasi N, Saleh H, Mohammad B, et al. 65-nm ASIC implementation of QRS detector based on Pan and Tompkins algorithm. International Conference on Innovations in Information Technology, 2014: 84
[2]
Pan J, Tompkins W J. A Real-Time QRS Detection Algorithm. Biomed Engineering IEEE Transactions on, 1985, BME-32(3): 230
[3]
G. VIJAYA, VINOD KUMAR, H. K. VERMA. Artificial neural network based wave complex detection in electrocardiograms. Int J Syst Sci, 1997, 28(2): 125
[4]
Zou Y, Han J, Weng X, et al. An Ultra-Low Power QRS Complex Detection Algorithm Based on Down-Sampling Wavelet Transform. 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. 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. 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. International Conference on Circuit, Power and Computing Technologies. 2016: 1
[8]
Priya, Singh M. MATLAB based ECG signal noise removal and its analysis. 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. 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. IEEE Trans Biomed Eng, 2015, 62(1): 165
[11]
Shweta J, Anil K, Varun B, Technique for QRS Complex Detection using Particle Swarm Optimization. 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. 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. 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. Comput Meth Prog Biomed, 2006, 82(3): 187
[16]
Poli R, Cagnoni S, Valli G. Genetic design of optimum linear and nonlinear QRS detectors. IEEE Trans Biomed Eng, 1995, 42(11): 1137
[17]
Afonso V X, Tompkins W J, Nguyen T Q, et al. ECG beat detection using filter banks. 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. 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. IEEE Trans Biomed Circuits Syst, 2009, 3(4): 220
[20]
Ieong C I, Mak P I, Lam C P, et al. A 0.83- QRS Detection Processor Using Quadratic Spline Wavelet Transform for Wireless ECG Acquisition in 0.35- CMOS. IEEE Trans Biomed Circuits Syst, 2012, 6(6): 586
[21]
Martinez J P, Almeida R, Olmos S, et al. A wavelet-based ECG delineator: evaluation on standard databases. IEEE Trans Bio-medical Eng, 2004, 51(4): 570

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    Received: 31 March 2017 Revised: 23 July 2017 Online: Accepted Manuscript: 13 November 2017Published: 01 October 2017

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      Article Navigation >  Journal of Semiconductors  > 2017  >  38(10): 105003
      Shudong Tian, Jun Han, Jianwei Yang, Xiaoyang Zeng. The energy-efficient implementation of an adaptive-filtering-based QRS complex detection method for wearable devices[J]. Journal of Semiconductors, 2017, 38(10): 105003. doi: 10.1088/1674-4926/38/10/105003 ****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.
      Citation:
      Shudong Tian, Jun Han, Jianwei Yang, Xiaoyang Zeng. The energy-efficient implementation of an adaptive-filtering-based QRS complex detection method for wearable devices[J]. Journal of Semiconductors, 2017, 38(10): 105003. doi: 10.1088/1674-4926/38/10/105003 ****
      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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      The energy-efficient implementation of an adaptive-filtering-based QRS complex detection method for wearable devices

      DOI: 10.1088/1674-4926/38/10/105003

      • State Key Laboratory of ASIC and System, Fudan University, Shanghai 201203, China

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      Project supported by the National Natural Science Foundation of China (Nos. 61574040, 61234002, 61525401).

      More Information
      • Corresponding author: Email: junhan@fudan.edu.cn
      • Received Date: 2017-03-31
      • Revised Date: 2017-07-23
      • Published Date: 2017-10-01

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