Abstract
Internet of Things (IoT) is going to be the next big technological revolution of mankind by connecting everything on the earth via the Internet. Mobile healthcare or remote healthcare is an important application of IoT, which provides a new platform to people for getting benefit regarding healthcare-related problems. In these applications, various sensing devices connect with the patient’s body and generate an enormous amount of heterogeneous data over time. Due to the variety of data, extraction of knowledge from these data is not straight forward just like the conventional data mining process. In this research, we have proposed a Deep Convolution Neural Network (DCNN)-based classification method for performing data mining over heterogeneous data by taking an unstructured sensor dataset from the arrhythmia database of physionet. In the proposed method, the CNN feature extraction layer converts the ECG signals into numeric form by calculating its features without any human intervention. Similarly, categorical data are converted based on their respective categories. Finally, all the converted data together were added to the CNN classification part and arrhythmia disease is predicted with an accuracy of more than 98%. The simulation result shows that our proposed CNN-based architecture outperforms other handcrafted feature extraction techniques in terms of accuracy, sensitivity, and specificity.
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References
Ahmed E, Yaqoob I, Hashem IAT, Khan I, Ahmed AIA, Imran M, Vasilakos AV (2017) The role of big data analytics in internet of things. Comput Netw 129(2):459–471
Albert C-C, Yang AS (2019) MIT-BIH Arrhythmia Database. https://physionet.org/note. Accessed 25 Nov 2019
Ayub S, Saini J (2011) Ecg classi_cation and abnormality detection using cascade forward neural network. Int J Eng Sci Technol 3(3)
Chen S, Hua W, Li Z, Li J, Gao X (2017) Heartbeat classification using projected and dynamic features of ecg signal. Biomed Signal Process Control 31:165–173
Curry SJ (2019) Screening for coronary heart disease with electrocardiography. https://www.uspreventiveservicestaskforce.org/Home/GetFileByID/1883. Accessed 14 June 2019
Dallali A, Kachouri A, Samet M (2011) Fuzzy c-means clustering neural network wt and hrv for classi_cation of cardiac arrhythmia. ARPN J Eng Appl Sci 6(10):2011
Das MK, Ari S (2014) Ecg beats classi_cation using mixture of features. International scholarly research notices 2014
Elhaj FA, Salim N, Harris AR, Swee TT, Ahmed T (2016) Arrhythmia recognition and classi_cation using combined linear and nonlinear features of ecg signals. Computer methods and programs in biomedicine 127:52–63
Ghongade R, Ghatol A (2007) Performance analysis of feature extraction schemes for arti_cial neural network based ecg classification. In: International conference on computational intelligence and multimedia applications (ICCIMA 2007). vol 2. IEEE, pp 486–490
Park K, Cho B, Lee D, Song S, Lee J, Chee Y, Kim I, Kim S (2008) Hierarchical support vector machine based heartbeat classi_cation using higher order statistics and hermite basis function. In: 2008 Computers in cardiology. IEEE, pp 229–232
Paul S (2019) Cardiac (heart) screening. https://www.radiologyinfo.org/en/info.cfm?pg=screening-cardiac. Accessed: 14 June 2019
Thomas M, Das MK, Ari S (2015) Automatic ECG arrhythmia classification using dual tree complex wavelet based features. AEU-Int J Elctron Commun 69(4):715–721
Venkatesan C, Karthigaikumar P, Paul A, Satheeskumaran S, Kumar R (2018) Ecg signal preprocessing and svm classi_er-based abnormality detection in remote healthcare applications. IEEE Access 6:9767–9773
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Medhi, K., Arifuzzaman Mondal, M., Iftekhar Hussain, M. (2021). An Approach to Handle Heterogeneous Healthcare IoT Data Using Deep Convolutional Neural Network. In: Bora, P.K., Nandi, S., Laskar, S. (eds) Emerging Technologies for Smart Cities. Lecture Notes in Electrical Engineering, vol 765. Springer, Singapore. https://doi.org/10.1007/978-981-16-1550-4_4
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DOI: https://doi.org/10.1007/978-981-16-1550-4_4
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