A High Precision Real-time Premature Ventricular Contraction Assessment Method based on the Complex Feature Set
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This paper presents a high precision and low computational complexity premature ventricular contraction (PVC) assessment method for the ECG human-machine interface device. The original signals are preprocessed by integrated filters. Then, R points and surrounding feature points are determined by corresponding detection algorithms. On this basis, a complex feature set and feature matrices are obtained according to the position feature points. Finally, an exponential Minkowski distance method is proposed for PVC recognition. Both public dataset and clinical experiments were utilized to verify the effectiveness and superiority of the proposed method. The results show that our R peak detection algorithm can substantially reduce the error rate, and obtained 98.97% accuracy for QRS complexes. Meanwhile, the accuracy of PVC recognition was 98.69% for the MIT-BIH database and 98.49% for clinical tests. Moreover, benefiting from the lightweight of our model, it can be easily applied to portable healthcare devices for human-computer interaction.
KeywordsElectrocardiogram (ECG); Heartbeat classification Complex feature set Precision medicine Human-computer interaction MIT database
Our research is supported by the National Key R&D Program of China (No. 2018YFB1307005).
Compliance with ethical standards
Conflict of interest
All authors declare that there is no conflict of interest in this work.
All the procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and the 1964 Helsinki declaration and its later amendments or comparable ethical standards. We have obtained the ethical approval for ECG data from Shanghai First People’s Hospital Affiliated to Shanghai Jiao Tong University, Shanghai, China.
Informed consent was obtained from all individual participants included in the study.
- 2.United Nations. Department of economic and social affairs population division. World Population Aging 2015. New York, 2015. Google Scholar
- 4.Yang, Z. et al., An IoT-cloud Based Wearable ECG Monitoring System for Smart Healthcare. J. Med. Syst. 40(12), 2016.Google Scholar
- 7.So, H. H., and Chan, K. L., Development of QRS detection method for real-time ambulatory cardiac monitor. Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings 1:289–292, 1997.Google Scholar
- 15.Christov, I. et al., Pseudo-real-time low-pass filter in ECG, self-adjustable to the frequency spectra of the waves. Med. Biol. Eng. Comput.:1–10, 2017.Google Scholar
- 21.ANSI/AAMI, Testing and reporting performance results of cardiac rhythm and ST segment measurement algorithms, Association for the Advancement of Medical Instrumentation (AAMI), 2008, American National Standards Institute, Inc. (ANSI), 2008 ANSI/AAMI/ISO EC57, 1998-(R). Google Scholar
- 24.Subbiah, S. and S. Subramanian, Biomedical arrhythmia heart diseases classification based on aritificial neural network and machine learning approach. International Journal of Engineering and Technology (UAE), 2018. 7(3.27 Special Issue 27): p. 10–14.Google Scholar
- 26.Chang, R. C.-H. et al., Design of a Low-Complexity Real-Time Arrhythmia Detection System. Journal of Signal Processing Systems:1–12, 2017.Google Scholar
- 27.Ramya, R., and Sasikala, T., An efficient Minkowski distance-based matching with Merkle hash tree authentication for biometric recognition in cloud computing. Soft. Comput., 2019.Google Scholar