Abstract
Our human heart is classified into four sections called the left side and right side of the atrium and ventricle accordingly. Monitoring and taking care of the heart of every human is the very essential part. Therefore, the early prediction is essential to save and give awareness to humans about diet plan, lifestyle schedule. Also, this is used to improve the clinical diagnosis and treatment of any patients. To predict or identifying any cardiovascular problems, Electro Cardio Gram (ECG) is used to record the electrical signal of the heart from the body surface of humans. The algorithm learns the dataset from before cluster is called supervised; The algorithm learns to train the data from the set of a dataset is called unsupervised. Then the classification of more amount of heartbeat for different category of normal, abnormal, irregular heartbeats to detect cardiovascular diseases. In this research article, a comparison of various methods to classify the dataset with a fusion-based feature extraction method. Besides, our research work consists of a de-noising filter to reconstruct the raw data from the original input. Our proposed framework performing preprocessing that consists of a filtering approach to remove noises from the raw data set. The signal is affected by thermal noise and instrumentation noise, calibration noise due to power line fluctuation. This interference is high in many handheld devices which can be eliminated by de-noising filters. The output of the de-noising filter is input for fusion-based feature extraction and prediction model construction. This workflow progress has given good results of classifier effectiveness and imbalance arrangement conditions. We achieved good accuracy 96.5% and minimum computation time for classification of ECG signal.
References
Emelia, J.; Benjamin, S.S.V.; Clifton, W.; Callaway, A.M.C.; Alexander, R.; Chang, S.C.; Stephanie, E.; Chiuve, M.C.; Francesca, N.; Delling, R.D.; et al. Heart Disease and Stroke Statistics—2018 Update: A Report from the American Heart Association. Circulation 2018, 137, e67–e492.
Hannun, A.Y.; Rajpurkar, P.; Haghpanahi, M.; Tison, G.H.; Bourn, C.; Turakhia, M.P.; Ng, A.Y. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nat. Med. 2019, 25, 65–69.[CrossRef]
. Yang, C.; Veiga, C.; Rodriguez-Andina, J.J.; Farina, J.; Iniguez, A.; Yin, S. Using PPG Signals and Wearable Devices for Atrial Fibrillation Screening. IEEE Trans. Ind. Electron. 2019, 66, 8832–8842.[CrossRef]
. Lyon, A.; Mincholé, A.; Martínez, J.P.; Laguna, P.; Rodriguez, B. Computational techniques for ECG analysis and interpretation in light of their contribution to medical advances. J. R. Soc. Interface 2018, 15, 20170821.[CrossRef]
. Singh, B.N.; Tiwari, A.K. Optimal selection of wavelet basis function applied to ECG signal denoising. Digit. Signal Process. 2006, 16, 275–287.[CrossRef]
. Friesen, G.M.; Jannett, T.C.; Jadallah, M.A.; Yates, S.L.; Quint, S.R.; Nagle, H.T. A comparison of the noise sensitivity of nine QRS detection algorithms. IEEE Trans. Biomed. Eng. 1990, 37, 85–98.[CrossRef]
. Van Alste and, T.S.; Schilder, J.A. Removal of base-line wander and power-line interference from the ECG by an efficient FIR filter with a reduced number of taps. IEEE Trans. Biomed. Eng. 1985, BME-32, 1052–1060.[CrossRef]
. Oster, J.; Behar, J.; Sayadi, O.; Nemati, S.; Johnson, A.E.W.; Clifford, G.D. Semisupervised ECG Ventricular Beat Classification with Novelty Detection Based on Switching Kalman Filters. IEEE Trans. Biomed. Eng. 2015, 62, 2125–2134.[CrossRef]
. Dalin Tang, Z.T.; Canton, G.; Hatsukami, T.S.; Dong, L.; Yuan, X.H.A.C. Local critical stress correlates better than global maximum stress with plaque morphological features linked to atherosclerotic plaque vulnerability: An in vivo multi-patient study. Biomed. Eng. Online 2009, 8, 15.[CrossRef]
. Frolich, L.; Dowding, I. Removal of muscular artifacts in EEG signals: A comparison of linear decomposition methods. Brain Inf. 2018, 5, 13–22.[CrossRef][PubMed]
. Blanco-Velasco, M.; Weng, B.; Barner, K.E. ECG signal denoising and baseline wander correction based on the empirical mode decomposition. Comput. Biol. Med. 2008, 38, 1–13.[CrossRef]
. Alfaouri, M.; Daqrouq, K. ECG Signal Denoising By Wavelet Transform Thresholding. Am. J. Appl. Sci. 2008, 5, 276–281.[CrossRef]
. Xu, Y.; Luo, M.; Li, T.; Song, G. ECG Signal De-noising and Baseline Wander Correction Based on CEEMDAN and Wavelet Threshold. Sensors 2017, 17, 2754.[CrossRef][PubMed]
. Han, G.; Xu, Z. Electrocardiogram signal denoising based on a new improved wavelet thresholding. Rev. Sci. Instrum. 2016, 87, 084303.[CrossRef]
. Pinho, A.; Pombo, N.; Silva, B.M.C.; Bousson, K.; Garcia, N. Towards an accurate sleep apnea detection based on ECG signal: The quintessential of a wise feature selection. Appl. Soft Comput. 2019, 83, 105568.[CrossRef]
Moavenian, M.; Khorrami, H. A qualitative comparison of Artificial Neural Networks and Support Vector Machines in ECG arrhythmias classification. Expert Syst. Appl. 2010, 37, 3088–3093.[CrossRef]
. Appathurai, A.; Jerusalin Carol, J.; Raja, C.; Kumar, S.N.; Daniel, A.V.; Jasmine Gnana Malar, A.; Fred, A.L.; Krishnamoorthy, S. A study on ECG signal characterization and practical implementation of some ECG characterization techniques. Measurement 2019, 147, 106384.[CrossRef]
. Chauhan S, Vig L. 2015 Anomaly detection in ECG time signals via deep long short-term memory networks. In 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 1 – 7. 19– 21 October, Paris, France. Piscataway, NJ: IEEE. (doi:10.1109/DSAA.2015.7344872).
. Oresko JJ, Jin Z, Cheng J, Huang S, Sun Y, Duschl H, Cheng AC. 2010 A wearable smartphone-based platform for real-time cardiovascular disease detection via electrocardiogram processing. IEEE Trans. Inf. Technol. Biomed. 14, 734– 740. (doi:10.1109/TITB.2010.2047865)
J. S. Wang, W. C. Chiang, Y. T. Yang, and Y. L. Hsu, "An effective ECG arrhythmia classification algorithm," Bio-Inspired Computing and Applicat, Springer Berlin Heidelberg, pp. 545-550, 2012.
A. Dallali, A. Kachouri, and M. Samet, "Classification of Cardiac Arrhythmia Using WT, HRV, and Fuzzy C-Means Clustering," Signal Processing: An Int. J. (SPJI), vol. 5, no. 3, pp. 101-109, 2011.
M. Vijayavanan, V. Rathikarani, and P. Dhanalakshmi, “Automatic Classification of ECG Signal for Heart Disease Diagnosis using morphological features,” Int. J. of Comput. Sci. and Eng. Technology (IJCSET), vol. 5, no. 4, pp. 449-455, 2014.
V. K. Srivastava and D. Prasad, "Dwt-Based Feature Extraction from ecg Signal," American J. of Eng. Research (AJER), vol. 2, no. 3, pp. 44- 50, 2013.
M. Korurek and B. Dogan, "ECG beat classification using particle swarm optimization and radial basis function neural network," Expert syst. with Applicat., vol. 37, no. 12, pp. 7563-7569, 2010.
. Üstünda ˘g, M.; Gökbulut, M.; ¸Sengür, A.; Ata, F. Denoising of weak ECG signals by using wavelet analysis and fuzzy thresholding. Netw. Modeling Anal. Health Inform. Bioinform. 2012, 1, 135–140.[CrossRef]
A. T. Sadiq and N. H. Shukr, "Classification of Cardiac Arrhythmia using ID3 Classifier Based on Wavelet Transform," Iraqi J. of Sci., vol. 54, no. 4, pp. 1167-1175, 2013.
N. P. Joshi and P. S. Topannavar, “Support vector machine based heartbeat classification,” Proc. of 4th IRF Int. Conf., pp. 140-144, 2014.
E. Zeraatkar et al., "Arrhythmia detection based on Morphological and time-frequency Features of t-wave in Electrocardiogram," J. of medical signals and sensors, vol. 1, no. 2, pp. 99-106, 2011.
S. N. Yu and K. T. Chou, "Integration of independent component analysis and neural networks for ECG beat classification," Expert Syst. with Applicat., vol. 34, no. 4, pp. 2841-2846, 2008.
D. Joshi and R. Ghongade, “Performance analysis of feature extraction schemes for ECG signal classification,” Int. J. of Elect., Electron. and Data Commun., vol. 1, pp. 45-51, 2013.
M. K. Das and S. Ari, "ECG Beats Classification Using Mixture of Features" Int. Scholarly Research Notices, 2014.
X. Tang and L. Shu, "Classification of Electrocardiogram Signals with RS and Quantum Neural Networks," Int. J. of Multimedia and Ubiquitous Eng., vol. 9, no. 2, pp. 363-372, 2014.
Wang, Zh., Yan, W., Oates, T. Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline // arXiv preprint. arXiv:1611.06455, 2016.