Abstract
In the recent past, Internet of Things (IoT) plays significant role in health care domain. The machine learning (ML) is the recent technology that is utilized by providing integration with IoT to improve accuracy and efficiency of healthcare domain. The ML technique is used to predict disease, monitor disease, self-management by patient, and intervention of clinic. ML approach is highly useful to develop diagnostic models. These models could be integrated with various healthcare service applications, and medical assessment maintains systems. In this chapter, cognitive intelligent healthcare (CIH) framework is established by integration of various wireless sensors with integration of IoT. The proposed methodology gives accurate, negligible time latency, and increased quality healthcare service at cost tolerance. The machine learning technique is used to classify the patient disease stages by monitoring patient. The logistic regression (LR) technique is one of the machine learning technique used to classify the patient disease. We develop electroencephalography (EEG) pathology analysis using LR technique to provide classification by integrating various wireless sensors to measure different parameters of patient and mainly monitor EEG signal variation during measurement. The EEG signal of patients can be transmitted through intelligent IoT device to the cloud storage. We express a real-time healthcare unit for a classification EEG and compute the performance of the proposed methodology in terms of accuracy and efficiency.
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References
Catarinucci L, de Donno D, Mainetti L, Palano L, Patrono L, Stefanizzi ML, Tarricone L (2015) An IoT-aware architecture for smart healthcare systems. IEEE Internet Things J 2:515–526. https://doi.org/10.1109/JIOT.2015.2417684
Lewy H (2015) Wearable technologies—future challenges for implementation in healthcare services. Healthcare Technol Lett 2:2–5. https://doi.org/10.1049/htl.2014.0104
Ma X, Wang Z, Zhou S, Wen H, Zhang Y (2018) Intelligent healthcare systems assisted by data analytics and mobile computing. In: 2018 14th international wireless communications & mobile computing conference (IWCMC). IEEE, Limassol (2018), pp. 1317–1322. https://doi.org/10.1109/IWCMC.2018.8450377
Alam MdGR, Abedin SF, Moon SI, Talukder A, Hong CS (2019) Healthcare IoT-based affective state mining using a deep convolutional neural network. IEEE Access 7:75189–75202. https://doi.org/10.1109/ACCESS.2019.2919995
Mohan S, Thirumalai C, Srivastava G (2019) Effective heart disease prediction using hybrid machine learning techniques. IEEE Access 7:81542–81554. https://doi.org/10.1109/ACCESS.2019.2923707
Simsek M, Obinikpo AA, Kantarci B (2020) Deep learning in smart health: methodologies, applications, challenges. In: El Saddik A, Hossain MS, Kantarci B (eds) Connected health in smart cities. Springer International Publishing, Cham, pp. 23–46. https://doi.org/10.1007/978-3-030-27844-1_3
Thabtah F, Abdelhamid N, Peebles D (2019) A machine learning autism classification based on logistic regression analysis. Health Inf Sci Syst 7:12. https://doi.org/10.1007/s13755-019-0073-5
Musa AB (2013) Comparative study on classification performance between support vector machine and logistic regression. Int J Mach Learn Cyber 4:13–24. https://doi.org/10.1007/s13042-012-0068-x
El-Kafrawy NM, Hegazy D, Tolba MF (2014) Features extraction and classification of EEG signals using empirical mode decomposition and support vector machine. In: Hassanien AE, Tolba MF, Taher Azar A (eds) Advanced machine learning technologies and applications. Springer International Publishing, Cham, pp 189–198. https://doi.org/10.1007/978-3-319-13461-1_19
Fares A, Zhong S, Jiang J (2019) EEG-based image classification via a region-level stacked bi-directional deep learning framework. BMC Med Inform Decis Mak 19:268. https://doi.org/10.1186/s12911-019-0967-9
Wang L-H, Hsiao Y-M, Xie X-Q, Lee S-Y (2016) An outdoor intelligent healthcare monitoring device for the elderly. IEEE Trans Consum Electron 62:128–135. https://doi.org/10.1109/TCE.2016.7514671
Garg L, McClean SI, Barton M, Meenan BJ, Fullerton K (2012) Intelligent patient management and resource planning for complex, heterogeneous, and stochastic healthcare systems. IEEE Trans Syst Man Cybern A 42:1332–1345. https://doi.org/10.1109/TSMCA.2012.2210211
Wang K, Shao Y, Xie L, Wu J, Guo S (2020) Adaptive and fault-tolerant data processing in healthcare IoT based on fog computing. IEEE Trans Netw Sci Eng 7:263–273. https://doi.org/10.1109/TNSE.2018.2859307
An X, Kuang D, Guo X, Zhao Y, He L (2014) A deep learning method for classification of EEG data based on motor imagery. In: Huang D-S, Han K, Gromiha M (eds) Intelligent computing in bioinformatics. Springer International Publishing, Cham, pp 203–210. https://doi.org/10.1007/978-3-319-09330-7_25
Rashid M, Sulaiman N, Mustafa M, Khatun S, Bari BS (2019) The classification of EEG signal using different machine learning techniques for BCI application. In: Kim J-H, Myung H, Lee S-M (eds) Robot intelligence technology and applications. Springer Singapore, Singapore, pp 207–221. https://doi.org/10.1007/978-981-13-7780-8_17
Ba-Karait NO, Shamsuddin SM, Sudirman R (2012) EEG signals classification using a hybrid method based on negative selection and particle swarm optimization. In: Perner P (ed) Machine learning and data mining in pattern recognition. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 427–438. https://doi.org/10.1007/978-3-642-31537-4_34
Fang C, Li H, Ma L (2013) EEG signal classification using the event-related coherence and genetic algorithm. In: Liu D, Alippi C, Zhao D, Hussain A (eds) Advances in brain inspired cognitive systems. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 92–100. https://doi.org/10.1007/978-3-642-38786-9_11
Isin A, Ozdalili S (2017) Cardiac arrhythmia detection using deep learning. Proc Comput Sci 120:268–275. https://doi.org/10.1016/j.procs.2017.11.238
Yıldırım Ö, Pławiak P, Tan R-S, Acharya UR (2018) Arrhythmia detection using deep convolutional neural network with long duration ECG signals. Comput Biol Med 102:411–420. https://doi.org/10.1016/j.compbiomed.2018.09.009
Vavrečka M, Lhotská L (2013) EEG feature selection based on time series classification. In: Perner P (ed) Machine learning and data mining in pattern recognition. Springer, Berlin Heidelberg, pp 520–527. https://doi.org/10.1007/978-3-642-39712-7_40
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Vedanarayanan, V., Arulselvi, G., Poornima, D. (2021). Cognitive Intelligent Healthcare (CIH) Framework by Integration of IoT with Machine Learning for Classification of Electroencephalography (EEG). In: Roy, S., Goyal, L.M., Mittal, M. (eds) Advanced Prognostic Predictive Modelling in Healthcare Data Analytics. Lecture Notes on Data Engineering and Communications Technologies, vol 64. Springer, Singapore. https://doi.org/10.1007/978-981-16-0538-3_6
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