Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress 2023 EHA Congress 2023 ASCO Annual Meeting
Clinical Collections
Advances in Alzheimer’s

Keynote webinar | Spotlight on sleep in brain health

LIVE: Thursday 2nd May 2024, 18:00-19:30 (CEST)

Quality sleep is essential for health. But what happens to our brains when sleep patterns are disturbed? Join our experts to explore the interplay between sleep disruption and neurological diseases, and the questions that you need to be asking your patients to help you prevent the harmful effects of sleep deprivation.
ADVANCED SEARCH
Log in
Top
Journal of Medical Systems
Published in: Journal of Medical Systems 7/2016

01-07-2016 | Patient Facing Systems

Efficient Fine Arrhythmia Detection Based on DCG P-T Features

Authors: Rongfang Bie, Shuaijing Xu, Guangzhi Zhang, Meng Zhang, Xianlin Ma, Xialin Zhang

Published in: Journal of Medical Systems | Issue 7/2016

Login to get access

Abstract

Due to the high mortality associated with heart disease, there is an urgent demand for advanced detection of abnormal heart beats. The use of dynamic electrocardiogram (DCG) provides a useful indicator of heart condition from long-term monitoring techniques commonly used in the clinic. However, accurately distinguishing sparse abnormal heart beats from large DCG data sets remains difficult. Herein, we propose an efficient fine solution based on 11 geometrical features of the DCG PQRST(P-T) waves and an improved hierarchical clustering method for arrhythmia detection. Data sets selected from MIT-BIH are used to validate the effectiveness of this approach. Experimental results show that the detection procedure of arrhythmia is fast and with accurate clustering.
Literature
1.
Tu, Y.W., Chen, H., Fu, X.Q., et al., Beats clustering based algorithm for fast recognition of motion artifact sections in Holter system[J]. J. Zhejiang Univ. Eng. Sci. 46(6):1148–1156, 2012.
2.
Tu, Y.W., Yu, X.M., Chen, H., and et al., A novel method for the detection of sleep apnea syndrome based on single-lead ECG signal[C]. Appl. Mech. Mater. Trans. Technol. Publ. 239:1079–1083, 2012.CrossRef
3.
Gang, Z., Yalou, H., and Pengtao, W., Study of Classficication Rules on Weighted Coronary Heart Disease Data[C], pp. 1845–1850. Guangzhou: Proceedings of the Fourth International conference on Machine Learning and Cybernetics, 2005.
4.
Ghongade, R., and Ghatol, A.A., brief performance evaluation of ECG feature extraction techniques for artificial neural network based classification[C]. In: TENCON 2007-2007 IEEE Region 10 Conference, pp. 1–4. IEEE (2007)
5.
Jiawei Han, M.K., and Pei, J., Data mining: Concepts and Techniques, Concepts and Techniques[J] (2011)
6.
Yue, Q., Research of clustering strategies for dynamic electrocardiogram waveform Tianjin University of Technology (2008)
7.
Desai, U., Martis, R.J., NAYAK C, G., and et al., Decision support system for arrhythmia Beats using ECG signals with dct, dwt and Emd methods: a comparative study[J], p. 1640012: Journal of Mechanics in Medicine and Biology, 2016.
8.
Li, N., Yang, Z., Sun, L., and Wei, R., Extraction method based on Q wavelet transform of ECG signal characteristic. Comput. Sci. 41:61?-64, 2014.
9.
Pang, Z., The comparison of dynamic electrocardiogram and common electrocardiogram in diagnosis coronary heart disease. China Foreign Med. Treat. 28(13):1–1, 2009.
10.
Perren, F., Gschwind, M., Landis, T., and et al., Multiple sclerosis relapse mimicking a transient ischemic attack[J]. Mult. Scler. Relat. Disord. 5:27–28, 2016.CrossRefPubMed
11.
Banerjee, S., and Mitra, M., ECG feature extraction and classification of anteroseptal myocardial infarction and normal subjects using discrete wavelet transform[C]. In: 2010 International Conference on Systems in Medicine and Biology (ICSMB), pp. 55–60. IEEE (2010)
12.
Mao X, M., Zhang T, T., and LI, Q., ECG Clustering algorithm based on ARMA[j]. Chin. J. Biomed. Eng. 6:004, 2012.
13.
Chen, H.C., and Chen, S.W., A moving average based filtering system with its application to real-time QRS detection[C]. In: Computers in Cardiology, 2003, pp. 585–588. IEEE (2003)
14.
Li, N., and Yang, Z., The application of frequency slice wavelet transform in ECG signal feature Extraction[J]. J. Fiber Bioeng. Inf. 8(3):461–472, 2015.
15.
Xu, J., A-ming, W., and Zheng, X., Analysis of ECG Signals Based on Wavelet Threshold De-noising[J]. Comput. Simul. 12:068, 2011.
16.
Wang, Y., and Zeng, D., Processing of ECG signal based on compared with wavelet denoising and the EMD de-noising in quantitative. J. Numer. Methods Comput. Appl. 32:274–282 , 2011.
17.
Yuan, F., Research about noise pocessing of ECG signals. Electron. Tecnol. 5:130–131, 2014.
18.
Zhang, X., and Xu, Z., Hesitant fuzzy agglomerative hierarchical clustering algorithms[J]. Int. J. Syst. Sci. 46(3):562–576, 2015.CrossRef
19.
Jing, Y., Zhou, Q., and Chen, W., An improved template matching algorithm and its application in ECG waveform recognition. Chin. J. Biomed. Eng. 31(5):775–780, 2012.
20.
Ge, D., and Weng, J., Discrimination of myocardial infarction based on 2d-LDA and high frequency electrocardiogram. Space Med. Med. Eng. 26(2):125–130, 2013.
21.
Yuan, Z., Li, J., and Zhang, B., A weighting system clustering method and application. J. Shenyang Univ. Nat. Sci. 26(3):201–207, 2014.
22.
Ma, Y.J., and Yun, W.X., Research progress of genetic algorithm[J]. Jisuanji Yingyong Yanjiu 29(4), 2012.
23.
Bushra J., Olivier L., Eric F., et al.: Detection of QRS complex in ECG signal based on classification approach[C]. In: 2010 17th IEEE International Conference on Image Processing (ICIP), pp. 345–348. IEEE (2010)
24.
Liu, J., Analysis of ECG signal and software implement beijing jiaotong university (2010)
25.
Liu, S., Research on clustering in typical character of ECG by fuzzy C-Means Zhejiang University (2006)
Metadata
Title
Efficient Fine Arrhythmia Detection Based on DCG P-T Features
Authors
Rongfang Bie
Shuaijing Xu
Guangzhi Zhang
Meng Zhang
Xianlin Ma
Xialin Zhang
Publication date
01-07-2016
Publisher
Springer US
Published in
Journal of Medical Systems / Issue 7/2016
Print ISSN: 0148-5598
Electronic ISSN: 1573-689X
DOI
https://doi.org/10.1007/s10916-016-0519-0

Other articles of this Issue 7/2016

Journal of Medical Systems 7/2016 Go to the issue

Systems-Level Quality Improvement

HOSVD-Based 3D Active Appearance Model: Segmentation of Lung Fields in CT Images

Mobile Systems

Massive Access Control Aided by Knowledge-Extraction for Co-Existing Periodic and Random Services over Wireless Clinical Networks

Transactional Processing Systems

Data–Driven Multimodal Sleep Apnea Events Detection

Systems-Level Quality Improvement

Design and Construction of a Microcontroller-Based Ventilator Synchronized with Pulse Oximeter

Transactional Processing Systems

Analysis of the process of representing clinical statements for decision-support applications: a comparison of openEHR archetypes and HL7 virtual medical record

Systems-Level Quality Improvement

A Multilayer Perceptron Based Smart Pathological Brain Detection System by Fractional Fourier Entropy