Abstract
Anomalies in cardiac functionality can be fatal. Early detection of these anomalies, and in many cases their precursors, can save lives. The probability of the occurrence of these anomalies is extremely among people with a pre-diagnosed heart condition. In this research, we discovered that much remote Electrocardiography (ECG) monitoring systems do not convey “enough” information to the diagnosing doctor or the nominated caregiver. A few examples of this information can be the type of cardiac abnormality, the exact waveform of the ECG signal, time and frequency of the occurrence of the anomaly, machine-understandable part so that medical SCADA be alerted about the case, and immediate preventative urgent steps correlated to that emergency. It is also important to delivery surrounding context to the Health Information System so that the medical expert make his/her diagnosis with ample support data. The most important component in this communication is the security of contents from cybercrimes. We propose a cost-efficient and non-invasive health monitoring system that is secure and quickly deployable. The presented system embeds an intelligent wearable data acquisition system with unique identification algorithms requiring very little computational time and simple threshold-based classification.
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Ahmad M, Jabbar S, Ahmad A, Piccialli F, Jeon G (2018) A sustainable solution to support data security in high bandwidth health care remote locations by using TCP CUBIC mechanism. IEEE Trans Sustain Comput. https://doi.org/10.1109/TSUSC.2018.2841998
Ashraf R, Ahmed M, Ahmad U, Habib MA, Jabbar S, Naseer K (2018a) MDCBIR-MF: multimedia data for content-based image retrieval by using multiple features. Multimed Tools Appl. https://doi.org/10.1007/s11042-018-5961-1
Ashraf R, Ahmed M, Jabbbar S, Khalid S, Ahmad A, Din S, Jeon G (2018b) Content based image retrieval by using color descriptor and discrete wavelet transform. J Med Syst (SpringerLink) 42 (3):44
Buechley L, Eisenberg M (2008) The LilyPad Arduino: toward wearable engineering for everyone. IEEE Pervasive Comput (IEEE) 7(2):12–15
Cables BioMetric (2011) CardioSim Heartbeat simulator system. http://biometriccables.com/product/ecgsimulatorscardiosim11/. Accessed 18 Sep 2018
Chen M, Gonzalez S, Vasilakos A, Cao H, Leung VC (2011) Body Area Networks: A Survey. Mob Netw Appl (MONET) 16(2):171–193
Chen KR, Lin Y–L, Yang M-C (2013) Medical communication device with a compact planar antenna and heterogeneous wireless resource for ubiquitous real-time healthcare monitoring. In: ICME international conference on complex medical engineering (CME), pp 224–227
De D, Mukherjee A (2014) Femtocell based economic health monitoring scheme using mobile cloud computing. In: ieee international conference on advance computing conference (IACC), Gurgaon, India: IEEE, pp 385–390
Gradl S, Kugler P, Lohmüller C, Eskofier B (2012) Real-time ECG monitoring and arrhythmia detection using android-based mobile devices. In: Annual international conference of the IEEE engineering in medicine and biology society, San Diego, CA, USA: IEEE, pp 2452–2455
Grajales L, Nicolaescu IV (2006) “Wearable multisensor heart rate monitor.” International Workshop on Wearable and Implantable Body Sensor Networks. Cambridge, MA, USA: IEEE. 154–157
Jabbar S, Ullah F, Khalid S, Khan M, Kijun, Han (2017) Semantic Interoperability in heterogeneous IoT infrastructure for healthcare. Wirel Commun Mobile Comput. https://doi.org/10.1155/2017/9731806
Lang M (2018) A low-complexity model-free approach for real-time cardiac anomaly detection based on singular spectrum analysis and nonparametric control charts. Technologies (MDPI) 6:26
Lemkaddem A, Proença M, Delgado-Gonzalo R, Renevey P, Oei I, Montano G, Martinez-Heras JA, Donati A, Bertschi M, Lemay M (2017) An autonomous medical monitoring system: Validation on arrhythmia detection. In: 39th annual international conference of the IEEE engineering in medicine and biology society (EMBC), Jeju Island, S. Korea: IEEE, pp 4553–4556
Li S, Li Da Xu L, Wang X (2013) A continuous biomedical signal acquisition system based on compressed sensing in body sensor networks. IEEE Trans Ind Inf 9: 1764–1771
Luz EJDS, Schwartz WR, Cámara-Chávez G, Menotti D (2016) ECG-based heartbeat classification for arrhythmia detection: a survey. Comput Methods Progr Biomed (ScienceDirect, Elsevier) 127 144–164
Ma Y, Xiao D, Hang RRL, Zhao S, Zhao J, Zhang Y (2015) Android-based intelligent mobile robot for indoor healthcare. In: 17th international conference on e-health networking application & services (HealthCom), pp 472–474
Milenkovic A, Otto C, Jovanov E (2006) Wireless Sensor Networks for Personal Health Monitoring: Issues and an Implementation. Comput Commun (Elsevier) 29:(13–14)
Modarressi M, Yasoubi A, Modarressi M (2016) Low-power online ECG analysis using neural networks. In: Euromicro conference on digital system design (DSD). Limassol, Cyprus: IEEE, pp 547–552
O’Donovan T, O’Donoghue J, Sreenan C, O’Reilly P, Sammon D, O’Connor K (2009) A context-aware wireless body area network. In: Proceedings of the pervasive health conference, London, UK: IEEE
Otto C, Milenkovic A, Sanders C, Jovanov E (2005) System Architecture of a wireless body area sensor network for ubiquitous health monitoring. J Mob Multimedia (Rinton Press) 1(4):07–326
Qidwai U, Shakir M (2011) Fuzzy detection of critical cardiac abnormalities using ECG data: a ubiquitous approach. In: 11th Hybrid Intelligent Systems Conference. Melacca, Malaysia: IEEE
Qidwai U, Shakir M (2012) Embedded system design with filter bank and fuzzy classification approach to critical cardiac abnormalities detection. In: IEEE symposium on industrial electronics and applications, Bandung, Indonesia: IEEE
Ullah S, Higgins H, Braem B, Latre B, Blondia C, Moerman I, Saleem S, Rahman Z, Kwak KS (2012) A comprehensive survey of wireless body area networks: On PHY, MAC, and network layers solutions. J Med Syst (PubMed SpringerLink) 36(3):1065–1094
Ullah F, Habib MA, Farhan M, Khalid S, Durrani MY, Jabbar S (2017) Semantic interoperability for big-data in heterogeneous IoT infrastructure for healthcare. Sustain Cities Soc (Elsevier) 34:90–96
Veeravalli B, Deepu CJ, Ngo D (2017) Real-time, personalized anomaly detection in streaming data for wearable healthcare devices. In: Khan SU, Zomaya AY, Abbas A (eds) Handbook of large-scale distributed computing in smart healthcare. Scalable computing and communications. Springer, Cham, pp 403–426
Xu H, Hua K, Zhu G-C, Huang J (2015) Adaptive forward error correction for ECG signal transmission for emotional stress assessment. In: 24th International Conference on computer communication and networks (ICCCN), Las Vegas, NV, USA: IEEE, pp 1–7
Yuce MR (2010) Implementation of wireless body area networks for healthcare systems. Sens Actuators A Phys 162:116–129 (ScienceDirect Elsevier)
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Qidwai, U., Chaudhry, J., Jabbar, S. et al. Using casual reasoning for anomaly detection among ECG live data streams in ubiquitous healthcare monitoring systems. J Ambient Intell Human Comput 10, 4085–4097 (2019). https://doi.org/10.1007/s12652-018-1091-x
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DOI: https://doi.org/10.1007/s12652-018-1091-x