A possible future direction of work is to adopt a model-based testing methodology for
validating the proposed approach [15–18]. Integration and investigation of event-based
processing modules [19–22] in this system is another prospect.
Acknowledgement and Ethical Approval. Authors are thankful to anonymous reviewers for
their useful feedback. This article does not contain any studies with human participants or
animals performed by any of the authors.
References
1. Pławiak, P.: Novel methodology of cardiac health recognition based on ECG signals and
evolutionary-neural system. Expert Syst. Appl. 92, 334–349 (2018)
2. Qaisar, S.M., Subasi, A.: An adaptive rate ECG acquisition and analysis for efficient
diagnosis of the cardiovascular diseases. In: 2018 IEEE 3rd International Conference on
Signal and Image Processing (ICSIP), pp. 177–181. IEEE, July 2018
3. You, I., Choo, K.K.R., Ho, C.L.: A smartphone-based wearable sensors for monitoring realtime physiological data. Comput. Electr. Eng. 65, 376–392 (2018)
4. Lian, Y.: Energy efficient system architecture for wireless wearable biomedical sensors. In:
2017 IEEE Custom Integrated Circuits Conference (CICC), pp. 1–33. IEEE, April 2017
5. Qaisar, S.M.: Efficient mobile systems based on adaptive rate signal processing. Comput.
Electr. Eng. 79, 106462 (2019)
6. Gopi, E.S.: Multirate digital signal processing. In: Gopi, E.S. (ed.) Multi-Disciplinary Digital
Signal Processing, pp. 121–146. Springer, Cham (2018). https://doi.org/10.1007/978-3-31957430-1_4
7. Jahmunah, V., et al.: Computer-aided diagnosis of congestive heart failure using ECG
signals–a review. PhysicaMedica 62, 95–104 (2019)
8. Rakshit, M., Das, S.: An efficient ECG denoising methodology using empirical mode
decomposition and adaptive switching mean filter. Biomed. Signal Process. Control 40,
140–148 (2018)
9. Kærgaard, K., Jensen, S.H., Puthusserypady, S.: A comprehensive performance analysis of
EEMD-BLMS and DWT-NN hybrid algorithms for ECG denoising. Biomed. Signal
Process. Control 25, 178–187 (2016)
10. Sharma, R.R., Pachori, R.B.: Baseline wander and power line interference removal from
ECG signals using eigenvalue decomposition. Biomed. Signal Process. Control 45, 33–49
(2018)
11. Serpen, G., Aghaei, E.: Host-based misuse intrusion detection using PCA feature extraction
and kNN classification algorithms. Intell. Data Anal. 22(5), 1101–1114 (2018)
12. Kulisch, U.W., Miranker, W.L.: Computer arithmetic in theory and practice. Academic
Press, Cambridge (2014)
13. Paluszek, M., Thomas, S.: MATLAB Machine Learning. Apress, New Jersey (2016)
14. Nowbar, A., Gitto, M., Howard, J., Francis, D., Al-Lamee, R.: 112 global and temporal
trends in mortality from ischaemic heart disease: statistics from the World Health
Organisation. Heart 105(Suppl 6), A93 (2019)
15. Krichen, M.: Model-based testing for real-time systems. Doctoral dissertation, Ph.D. thesis,
Universit Joseph Fourier (2007)
16. Krichen, M., Maâlej, A.J., Lahami, M.: A model-based approach to combine conformance
and load tests: an eHealth case study. IJCCBS 8(3/4), 282–310 (2018)
336
S. M. Qaisar et al.
validating the proposed approach [15–18]. Integration and investigation of event-based
processing modules [19–22] in this system is another prospect.
Acknowledgement and Ethical Approval. Authors are thankful to anonymous reviewers for
their useful feedback. This article does not contain any studies with human participants or
animals performed by any of the authors.
References
1. Pławiak, P.: Novel methodology of cardiac health recognition based on ECG signals and
evolutionary-neural system. Expert Syst. Appl. 92, 334–349 (2018)
2. Qaisar, S.M., Subasi, A.: An adaptive rate ECG acquisition and analysis for efficient
diagnosis of the cardiovascular diseases. In: 2018 IEEE 3rd International Conference on
Signal and Image Processing (ICSIP), pp. 177–181. IEEE, July 2018
3. You, I., Choo, K.K.R., Ho, C.L.: A smartphone-based wearable sensors for monitoring realtime physiological data. Comput. Electr. Eng. 65, 376–392 (2018)
4. Lian, Y.: Energy efficient system architecture for wireless wearable biomedical sensors. In:
2017 IEEE Custom Integrated Circuits Conference (CICC), pp. 1–33. IEEE, April 2017
5. Qaisar, S.M.: Efficient mobile systems based on adaptive rate signal processing. Comput.
Electr. Eng. 79, 106462 (2019)
6. Gopi, E.S.: Multirate digital signal processing. In: Gopi, E.S. (ed.) Multi-Disciplinary Digital
Signal Processing, pp. 121–146. Springer, Cham (2018). https://doi.org/10.1007/978-3-31957430-1_4
7. Jahmunah, V., et al.: Computer-aided diagnosis of congestive heart failure using ECG
signals–a review. PhysicaMedica 62, 95–104 (2019)
8. Rakshit, M., Das, S.: An efficient ECG denoising methodology using empirical mode
decomposition and adaptive switching mean filter. Biomed. Signal Process. Control 40,
140–148 (2018)
9. Kærgaard, K., Jensen, S.H., Puthusserypady, S.: A comprehensive performance analysis of
EEMD-BLMS and DWT-NN hybrid algorithms for ECG denoising. Biomed. Signal
Process. Control 25, 178–187 (2016)
10. Sharma, R.R., Pachori, R.B.: Baseline wander and power line interference removal from
ECG signals using eigenvalue decomposition. Biomed. Signal Process. Control 45, 33–49
(2018)
11. Serpen, G., Aghaei, E.: Host-based misuse intrusion detection using PCA feature extraction
and kNN classification algorithms. Intell. Data Anal. 22(5), 1101–1114 (2018)
12. Kulisch, U.W., Miranker, W.L.: Computer arithmetic in theory and practice. Academic
Press, Cambridge (2014)
13. Paluszek, M., Thomas, S.: MATLAB Machine Learning. Apress, New Jersey (2016)
14. Nowbar, A., Gitto, M., Howard, J., Francis, D., Al-Lamee, R.: 112 global and temporal
trends in mortality from ischaemic heart disease: statistics from the World Health
Organisation. Heart 105(Suppl 6), A93 (2019)
15. Krichen, M.: Model-based testing for real-time systems. Doctoral dissertation, Ph.D. thesis,
Universit Joseph Fourier (2007)
16. Krichen, M., Maâlej, A.J., Lahami, M.: A model-based approach to combine conformance
and load tests: an eHealth case study. IJCCBS 8(3/4), 282–310 (2018)
336
S. M. Qaisar et al.