The Classification of Heartbeats from Two-Channel ECG Signals Using Layered Hidden Markov Model
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Abstract
Purpose: Cardiac arrhythmia is one of the most common heart diseases that can have serious consequences. Thus, heartbeat arrhythmias classification is very important to help diagnose and treat. To develop the automatic classification of heartbeats, recent advances in signal processing can be employed. The Hidden Markov Model (HMM) is a powerful statistical tool with the ability to learn different dynamics of the real time-series such as cardiac signals.
Materials and Methods: In this study, a hierarchy of HMMs named Layered HMM (LHMM) was presented to classify heartbeats from the two-channel electrocardiograms. For training in the first layer, the morphology of the heartbeats was used as observations, while observations in the second layer were the inference results of the first layer. The performance of the proposed LHMM was evaluated in classifying three types of heartbeat arrhythmias (Atrial premature beats (A), Escape beats (E), Left bundle branch block beats (L)) using fifteen records of the MIT-BIH arrhythmia database. Furthermore, the obtained results of the proposed model were compared with other HMM generalizations.
Results: The best average accuracy was achieved 97.10±1.63%. The best sensitivity of 96.8±1.24%, 98.85±0.52%, and 95.64±1.41 were obtained for A, E, and L, respectively. Furthermore, the results of the proposed method were better than other HMM generalizations.
Conclusion: Extracting information from time-series dynamics by HMM-based methods has good classification results. The proposed model shows that applying a two-layered HMM can lead to better extraction of information from the observations; therefore, the classification performance of cardiac arrhythmias has been improved using LHMM.
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15- Lawrence R Rabiner, "A tutorial on hidden Markov models and selected applications in speech recognition." Proceedings of the IEEE, Vol. 77 (No. 2), pp. 257-86, (1989).
16- Miguel Altuve, Guy Carrault, Alain Beuchée, Patrick Pladys, and Alfredo I Hernández, "Online apnea–bradycardia detection based on hidden semi-Markov models." Medical & biological engineering & computing, Vol. 53 (No. 1), pp. 1-13, (2015).
17- N Montazeri Ghahjaverestan et al., "Coupled Hidden Markov Model-Based Method for Apnea Bradycardia Detection." IEEE journal of biomedical and health informatics, Vol. 20 (No. 2), pp. 527-38, (2016).
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19- Mahsa Akhbari, Mohammad B Shamsollahi, Omid Sayadi, Antonis A Armoundas, and Christian Jutten, "ECG segmentation and fiducial point extraction using multi hidden Markov model." Computers in Biology and Medicine, Vol. 79pp. 21-29, (2016).
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21- Antti Koski, "Modelling ECG signals with hidden Markov models." Artificial intelligence in medicine, Vol. 8 (No. 5), pp. 453-71, (1996).
22- Shing-Tai Pan, Tzung-Pei Hong, and Hung-Chin Chen, "Ecg signal analysis by using hidden markov model." in International Conference on Fuzzy Theory and Its Applications (iFUZZY2012) IEEE, pp. 288-93, (2012).
23- Y. Liao, Y. Xiang, and D. Du, "Automatic Classification of Heartbeats Using ECG Signals via Higher Order Hidden Markov Model." in 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE), pp. 69-74, (2020).
24- Nuria Oliver, Ashutosh Garg, and Eric Horvitz, "Layered representations for learning and inferring office activity from multiple sensory channels." Computer Vision and Image Understanding, Vol. 96 (No. 2), pp. 163-80, (2004).
25- Daniel Aarno and Danica Kragic, "Evaluation of Layered HMM for Motion Intention Recognition." in IEEE International Conference on Advanced Robotics (2007).
26- Lei He, Chang-fu Zong, and Chang Wang, "Driving intention recognition and behaviour prediction based on a double-layer hidden Markov model." Journal of Zhejiang University SCIENCE C, journal article Vol. 13 (No. 3), pp. 208-17, (2012).
27- Yosef S Razin, Kevin Pluckter, Jun Ueda, and Karen Feigh, "Predicting Task Intent From Surface Electromyography Using Layered Hidden Markov Models." IEEE Robotics and Automation Letters, Vol. 2 (No. 2), pp. 1180-85, (2017).
28- Nicolas Thome, Serge Miguet, and Sébastien Ambellouis, "A real-time, multiview fall detection system: A LHMM-based approach." IEEE transactions on circuits and systems for video technology, Vol. 18 (No. 11), pp. 1522-32, (2008).
29- S. Solaimanpour and P. Doshi, "A layered HMM for predicting motion of a leader in multi-robot settings." in IEEE International Conference on Robotics and Automation (ICRA), pp. 788-93, (2017).
30- Ary L Goldberger et al., "Physiobank, physiotoolkit, and physionet components of a new research resource for complex physiologic signals." Circulation, Vol. 101 (No. 23), p. 220, (2000).
31- George B Moody and Roger G Mark, "The impact of the MIT-BIH arrhythmia database." IEEE Engineering in Medicine and Biology Magazine, Vol. 20 (No. 3), pp. 45-50, (2001).
32- D. Zhang, "Wavelet Approach for ECG Baseline Wander Correction and Noise Reduction." (in eng), Conf Proc IEEE Eng Med Biol Soc, Vol. 2005pp. 1212-5, (2005).
33- Jiapu Pan and Willis J Tompkins, "A real-time QRS detection algorithm." IEEE Transactions on Biomedical engineering, (No. 3), pp. 230-36, (1985).
34- C. Li, C. Zheng, and C. Tai, "Detection of ECG characteristic points using wavelet transforms." (in eng), IEEE Trans Biomed Eng, Vol. 42 (No. 1), pp. 21-8, Jan (1995).
35- D. Novak, "Electrocardiogram Signal Processing using Hidden Markov Models," Ph.D. thesis, Faculty of Electrical Engineering, Czech Technical University, Prague, (2003).
36- Iead Rezek, Peter Sykacek, and Stephen J Roberts, "Learning interaction dynamics with coupled hidden Markov models." Measurement and Technology, Vol. 147 (No. 6), pp. 345-50, (2000).
37- M. M. Casas, R. L. Avitia, M. A. Reyna, and A. Cárdenas, "Evaluation of three machine learning algorithms as classifiers of premature ventricular contractions on ECG beats." in 2016 Global Medical Engineering Physics Exchanges/Pan American Health Care Exchanges (GMEPE/PAHCE), pp. 1-6, (2016).
38- O. T. Inan, L. Giovangrandi, and G. T. A. Kovacs, "Robust Neural-Network-Based Classification of Premature Ventricular Contractions Using Wavelet Transform and Timing Interval Features." IEEE Transactions on Biomedical engineering, Vol. 53 (No. 12), pp. 2507-15, (2006).
39- R Ganesh Kumar and YS %J Int. J. Comput. Appl Kumaraswamy, "Investigating cardiac arrhythmia in ECG using random forest classification." Vol. 37 (No. 4), pp. 31-34, (2012).
40- Saibal Dutta, Amitava Chatterjee, Sugata %J Medical engineering Munshi, and physics, "Correlation technique and least square support vector machine combine for frequency domain based ECG beat classification." Vol. 32 (No. 10), pp. 1161-69, (2010).
41- Chun-Cheng Lin and Chun-Min Yang, "Heartbeat Classification Using Normalized RR Intervals and Morphological Features." Mathematical Problems in Engineering, Vol. 2014p. 712474, 2014/05/04 (2014).
42- Gari D Clifford et al., "False alarm reduction in critical care." Physiological measurement, Vol. 37 (No. 8), p. E5, (2016).
2- Philip De Chazal, Maria O'Dwyer, and Richard B Reilly, "Automatic classification of heartbeats using ECG morphology and heartbeat interval features." IEEE Transactions on Biomedical engineering, Vol. 51 (No. 7), pp. 1196-206, (2004).
3- Zahia Zidelmal, Ahmed Amirou, and Adel Belouchrani, "Heartbeat classification using support vector machines (SVMs) with an embedded reject option." International Journal of Pattern Recognition and Artificial Intelligence, Vol. 26 (No. 01), p. 1250001, (2012).
4- Chun-Cheng Lin and Chun-Min Yang, "Heartbeat classification using normalized RR intervals and morphological features." Mathematical Problems in Engineering, (2014).
5- Douglas A Coast, Richard M Stern, Gerald G Cano, and Stanley A Briller, "An approach to cardiac arrhythmia analysis using hidden Markov models." IEEE Transactions on Biomedical engineering, Vol. 37 (No. 9), pp. 826-36, (1990).
6- Muhammad Zubair, Jinsul Kim, and Changwoo Yoon, "An automated ECG beat classification system using convolutional neural networks." in 6th International Conference on IT Convergence and Security (ICITCS), IEEE, pp. 1-5, (2016).
7- C. L. Herry, M. Frasch, A. J. Seely, and H. T. Wu, "Heart beat classification from single-lead ECG using the synchrosqueezing transform." (in eng), Physiol Meas, Vol. 38 (No. 2), pp. 171-87, Feb (2017).
8- J. Bogatinovski, D. Kocev, and A. Rashkovska, "Feature Extraction for Heartbeat Classification in Single-Lead ECG." in 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 320-25, (2019).
9- P. de Chazal, "Detection of supraventricular and ventricular ectopic beats using a single lead ECG." in 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 45-48, (2013).
10- Weiyi Yang, Yujuan Si, Di Wang, and Gong Zhang, "A Novel Approach for Multi-Lead ECG Classification Using DL-CCANet and TL-CCANet." (in eng), Sensors (Basel, Switzerland), Vol. 19 (No. 14), p. 3214, (2019).
11- Ian-Christopher Tanoh and Paolo Napoletano, "A Novel 1-D CCANet for ECG Classification." Applied Sciences, Vol. 11 (No. 6), p. 2758, (2021).
12- Eduardo José da S. Luz, William Robson Schwartz, Guillermo Cámara-Chávez, and David Menotti, "ECG-based heartbeat classification for arrhythmia detection: A survey." Computer methods and programs in biomedicine, Vol. 127pp. 144-64, 2016/04/01/ (2016).
13- Shing-Hong Liu, Da-Chuan Cheng, and Chih-Ming Lin, "Arrhythmia identification with two-lead electrocardiograms using artificial neural networks and support vector machines for a portable ECG monitor system." (in eng), Sensors (Basel, Switzerland), Vol. 13 (No. 1), pp. 813-28, (2013).
14- Ataollah Ebrahim Zadeh, Ali Khazaee, and Vahid Ranaee, "Classification of the electrocardiogram signals using supervised classifiers and efficient features." Computer methods and programs in biomedicine, Vol. 99 (No. 2), pp. 179-94, 2010/08/01/ (2010).
15- Lawrence R Rabiner, "A tutorial on hidden Markov models and selected applications in speech recognition." Proceedings of the IEEE, Vol. 77 (No. 2), pp. 257-86, (1989).
16- Miguel Altuve, Guy Carrault, Alain Beuchée, Patrick Pladys, and Alfredo I Hernández, "Online apnea–bradycardia detection based on hidden semi-Markov models." Medical & biological engineering & computing, Vol. 53 (No. 1), pp. 1-13, (2015).
17- N Montazeri Ghahjaverestan et al., "Coupled Hidden Markov Model-Based Method for Apnea Bradycardia Detection." IEEE journal of biomedical and health informatics, Vol. 20 (No. 2), pp. 527-38, (2016).
18- T Stamkopoulos, N Maglaveras, PD Bamidis, and C Pappas, "Wave segmentation using nonstationary properties of ECG." in Computers in Cardiology 2000. Vol. 27 (Cat. 00CH37163) IEEE, pp. 529-32, (2000).
19- Mahsa Akhbari, Mohammad B Shamsollahi, Omid Sayadi, Antonis A Armoundas, and Christian Jutten, "ECG segmentation and fiducial point extraction using multi hidden Markov model." Computers in Biology and Medicine, Vol. 79pp. 21-29, (2016).
20- Marco AF Pimentel, Mauro D Santos, David B Springer, and Gari D Clifford, "Heart beat detection in multimodal physiological data using a hidden semi-Markov model and signal quality indices." Physiological measurement, Vol. 36 (No. 8), pp. 1717-27, (2015).
21- Antti Koski, "Modelling ECG signals with hidden Markov models." Artificial intelligence in medicine, Vol. 8 (No. 5), pp. 453-71, (1996).
22- Shing-Tai Pan, Tzung-Pei Hong, and Hung-Chin Chen, "Ecg signal analysis by using hidden markov model." in International Conference on Fuzzy Theory and Its Applications (iFUZZY2012) IEEE, pp. 288-93, (2012).
23- Y. Liao, Y. Xiang, and D. Du, "Automatic Classification of Heartbeats Using ECG Signals via Higher Order Hidden Markov Model." in 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE), pp. 69-74, (2020).
24- Nuria Oliver, Ashutosh Garg, and Eric Horvitz, "Layered representations for learning and inferring office activity from multiple sensory channels." Computer Vision and Image Understanding, Vol. 96 (No. 2), pp. 163-80, (2004).
25- Daniel Aarno and Danica Kragic, "Evaluation of Layered HMM for Motion Intention Recognition." in IEEE International Conference on Advanced Robotics (2007).
26- Lei He, Chang-fu Zong, and Chang Wang, "Driving intention recognition and behaviour prediction based on a double-layer hidden Markov model." Journal of Zhejiang University SCIENCE C, journal article Vol. 13 (No. 3), pp. 208-17, (2012).
27- Yosef S Razin, Kevin Pluckter, Jun Ueda, and Karen Feigh, "Predicting Task Intent From Surface Electromyography Using Layered Hidden Markov Models." IEEE Robotics and Automation Letters, Vol. 2 (No. 2), pp. 1180-85, (2017).
28- Nicolas Thome, Serge Miguet, and Sébastien Ambellouis, "A real-time, multiview fall detection system: A LHMM-based approach." IEEE transactions on circuits and systems for video technology, Vol. 18 (No. 11), pp. 1522-32, (2008).
29- S. Solaimanpour and P. Doshi, "A layered HMM for predicting motion of a leader in multi-robot settings." in IEEE International Conference on Robotics and Automation (ICRA), pp. 788-93, (2017).
30- Ary L Goldberger et al., "Physiobank, physiotoolkit, and physionet components of a new research resource for complex physiologic signals." Circulation, Vol. 101 (No. 23), p. 220, (2000).
31- George B Moody and Roger G Mark, "The impact of the MIT-BIH arrhythmia database." IEEE Engineering in Medicine and Biology Magazine, Vol. 20 (No. 3), pp. 45-50, (2001).
32- D. Zhang, "Wavelet Approach for ECG Baseline Wander Correction and Noise Reduction." (in eng), Conf Proc IEEE Eng Med Biol Soc, Vol. 2005pp. 1212-5, (2005).
33- Jiapu Pan and Willis J Tompkins, "A real-time QRS detection algorithm." IEEE Transactions on Biomedical engineering, (No. 3), pp. 230-36, (1985).
34- C. Li, C. Zheng, and C. Tai, "Detection of ECG characteristic points using wavelet transforms." (in eng), IEEE Trans Biomed Eng, Vol. 42 (No. 1), pp. 21-8, Jan (1995).
35- D. Novak, "Electrocardiogram Signal Processing using Hidden Markov Models," Ph.D. thesis, Faculty of Electrical Engineering, Czech Technical University, Prague, (2003).
36- Iead Rezek, Peter Sykacek, and Stephen J Roberts, "Learning interaction dynamics with coupled hidden Markov models." Measurement and Technology, Vol. 147 (No. 6), pp. 345-50, (2000).
37- M. M. Casas, R. L. Avitia, M. A. Reyna, and A. Cárdenas, "Evaluation of three machine learning algorithms as classifiers of premature ventricular contractions on ECG beats." in 2016 Global Medical Engineering Physics Exchanges/Pan American Health Care Exchanges (GMEPE/PAHCE), pp. 1-6, (2016).
38- O. T. Inan, L. Giovangrandi, and G. T. A. Kovacs, "Robust Neural-Network-Based Classification of Premature Ventricular Contractions Using Wavelet Transform and Timing Interval Features." IEEE Transactions on Biomedical engineering, Vol. 53 (No. 12), pp. 2507-15, (2006).
39- R Ganesh Kumar and YS %J Int. J. Comput. Appl Kumaraswamy, "Investigating cardiac arrhythmia in ECG using random forest classification." Vol. 37 (No. 4), pp. 31-34, (2012).
40- Saibal Dutta, Amitava Chatterjee, Sugata %J Medical engineering Munshi, and physics, "Correlation technique and least square support vector machine combine for frequency domain based ECG beat classification." Vol. 32 (No. 10), pp. 1161-69, (2010).
41- Chun-Cheng Lin and Chun-Min Yang, "Heartbeat Classification Using Normalized RR Intervals and Morphological Features." Mathematical Problems in Engineering, Vol. 2014p. 712474, 2014/05/04 (2014).
42- Gari D Clifford et al., "False alarm reduction in critical care." Physiological measurement, Vol. 37 (No. 8), p. E5, (2016).
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| Issue | Vol 9 No 1 (2022) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/fbt.v9i1.8146 | |
| Keywords | ||
| Layered Hidden Markov Model Arrhythmia Electrocardiography Machine Learning Classification | ||
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How to Cite
1.
Sadoughi A, Shamsollahi MB, Fatemizadeh E. The Classification of Heartbeats from Two-Channel ECG Signals Using Layered Hidden Markov Model. Frontiers Biomed Technol. 2021;9(1):59-67.
