Abstract
Electronic Health Records (EHRs) enabled to store and process data recorded by sensors would mean standard-based personalization of medical services and would be a step further to guaranteeing a seamless care access. However, sensor data is subject to several sources of faults and errors which may further lead to imprecise or even incorrect and misleading answers. Thus, it is pivotal to ensure the quality of data collected from e.g. wearable sensors in wireless sensor networks for it to be used in a formal EHR. This article gives comparison of different data-driven models in cleaning eHealth sensor data from wireless sensor networks in order to make sure the data collected is precise and relevant and as such, may be included into a formal EHR. Furthermore, it then suggests optimization of the selected models with the goal of improving their results.




Similar content being viewed by others
References
Modi, S. S. (2019). Electronic health records value analysis. In 2019 IEEE international conference on healthcare informatics (ICHI), Xi’an, China (pp. 1–1).
Braunstein, M. L. (2019). Health care in the age of interoperability part 5: The personal health record. IEEE Pulse, 10(3), 19–23.
Roehrs, A., da Costa, C. A., da Rosa Righi, R., Rigo, S. J., & Wichman, M. H. (2019). Toward a model for personal health record interoperability. IEEE Journal of Biomedical and Health Informatics, 23(2), 867–873.
Harerimana, G., Kim, J. W., Yoo, H., & Jang, B. (2019). Deep learning for electronic health records analytics. IEEE Access, 7, 101245–101259.
Koren, A., Jurčević, M., & Huljenić, D. (2019). Requirements and challenges in integration of aggregated personal health data for inclusion into formal electronic health records (EHR). In 2019 2nd International colloquium on smart grid metrology (SMAGRIMET), Split, Croatia (pp. 1–5).
Lucisano, J. Y., Routh, T. L., Lin, J. T., & Gough, D. A. (2017). Glucose monitoring in individuals with diabetes using a long-term implanted sensor/telemetry system and model. IEEE Transactions on Biomedical Engineering, 64(9), 1982–1993.
Sowmya Padukone, G., & Uma Devi, H. (2018). Tumor markers for cancer detection using optical sensor. In: 2018 International conference on smart systems and inventive technology (ICSSIT), Tirunelveli, India (pp. 52–56).
Divya, R., & Chinnaiyan, R. (2018). Reliable smart earplug sensors for monitoring human organs based on 5G technology. In 2018 Second international conference on inventive communication and computational technologies (ICICCT), Coimbatore (pp. 687–690).
Wang, F., & Liu, J. (2011). Networked wireless sensor data collection: Issues, challenges, and approaches. IEEE Communications Surveys & Tutorials, 11(4), 673–687.
Dong, Y., Sun, L., Liu, D., Feng, M., & Miao, T. (2018). A survey on data integrity checking in cloud. In 2018 1st International cognitive cities conference (IC3), Okinawa (pp. 109–113).
Hongyuan, W. (2019). An external data integrity tracking and verification system for universal stream computing system framework. In 2019 21st International conference on advanced communication technology (ICACT), PyeongChang Kwangwoon_Do, Korea (South) (pp. 32–37).
Bhattacharjee, S., Salimitari, M., Chatterjee, M., Kwiat, K., & Kamhoua, C. (2017). Preserving data integrity in IoT networks under opportunistic data manipulation. In 2017 IEEE 15th International conference on dependable, autonomic and secure computing, 15th international conf on pervasive intelligence and computing, 3rd international conference on big data intelligence and computing and cyber science and technology congress (DASC/PiCom/DataCom/CyberSciTech), Orlando, FL (pp. 446–453).
Huang, J. (2018). From big data to knowledge: Issues of provenance, trust, and scientific computing integrity. In 2018 IEEE international conference on big data (big data), Seattle, WA, USA (pp. 2197–2205).
Ni, K., Ramanathan, N., et al. (2009). Sensor network data fault types. ACM Transactions on Sensor Networks (TOSN), 5(3), 1–29.
Parenreng, J. M., Kitagawa, A., & Andayani, D. D. (2019). A study of limited resources and security adaptation for extreme area in wireless sensor networks. In Journal of Physics: Conference Series, Volume 1244, Conference 1.
Baljak, V., Kenji, T., & Honiden, Sh. (2013). Faults in sensory readings: Classification and model learning. Sensors and Transducers, 18, 177–187.
Parenreng, J. M., & Kitagawa, A. (2017). A model of security adaptation for limited resources in wireless sensor network. Journal of Computer and Communications, 5, 10–23.
Parenreng, J. M., & Kitagawa, A. (2018). Resource optimization “techniques and security levels for wireless sensor networks based on the ARSy framework”. Sensors (Basel, Switzerland), 18(5), 1594.
Audéoud, H., & Heusse, M. (2018). Quick and efficient link quality estimation in wireless sensors networks. In 2018 14th Annual conference on wireless on-demand network systems and services (WONS), Isola (pp. 87–90).
Karkouch, A., Mousannif, H., Al, Moatassime H., & Noel, Th. (2016). Data quality in internet of things: A state-of-the-art survey. Journal of Network and Computer Applications, 73, 57–81.
Moghaddasi, H. (2016). A systemic biologic model for healthcare data quality. Him-Interchange, 6, 28–32.
Davoudi, S., Dooling, J. A., Glondys, B., Jones, T. D., Kadlec, L., Overgaard, S. M., Ruben, K., & Wendicke, A. (2015). Data quality management model. Journal of AHIMA, 86(10), 62–67.
Li, G., Peng, S., Wang, C., Niu, J., & Yuan, Y. (2019). An energy-efficient data collection scheme using denoising autoencoder in wireless sensor networks. Tsinghua Science and Technology, 24(1), 86–96.
Fotiou, N., Siris, V. A., Mertzianis, A., & Polyzos, G. C. (2018)”Smart IoT data collection. In 2018 IEEE 19th International symposium on “A world of wireless, mobile and multimedia networks” (WoWMoM), Chania (pp. 588–599).
Schobel, J., Pryss, R., Schickler, M., & Reichert, M. (2016). Towards flexible mobile data collection in healthcare. In 2016 IEEE 29th International symposium on computer-based medical systems (CBMS), Dublin (pp. 181–182).
Islam, Z., Mamun, Q., & Rahman, G. (2014). Data cleansing during data collection from wireless sensor networks. In: Proceedings of the twelfth Australasian data mining conference (AusDM 2014), Brisbane, Australia.
Dereszynski, E. W., & Diettrich, T. G. (2007). Probabilistic models for anomaly detection in remote sensor data streams. In Proceedings of the twenty-third conference on uncertainty in artificial intelligence (UAI2007), Vancouver, BC, Canada (pp. 75–82).
Ramirez, G., Fuentes, O., & Tweedie, C. E. (2011). Assessing data quality in a sensor network for environmental monitoring. In: 2011 Annual meeting of the North American fuzzy information processing society, El Paso, TX (pp. 1–6).
Cheng, H., Feng, D., Shi, X., & Chen, Ch. (2018). Data quality analysis and cleaning strategy for wireless sensor networks. EURASIP Journal on Wireless Communications and Networking, 2018, 61.
Rahman, M. G., Islam, M. Z., Bossomaier, T., & Gao, J. (2012). CAIRAD: A co-appearance-based analysis for incorrect records and attribute-values Detection. In The 2012 international joint conference on neural networks (IJCNN), Brisbane, QLD (pp. 1–10).
Rahman, M. G., & Islam, M. Z. (2011). A Decision Tree-based missing value imputation technique for data pre-processing. In V. Estivill-Castro, & S. Simoff (Eds.), 9th Australasian Data Mining Conference: AusDM 2011. Conferences in Research and Practice in Information Technology Series (Vol. 121, pp. 41–50). Australian Computer Society Inc.
Salem, O., Guerassimov, A., Mehaoua, A., Marcus, A., & Furht, B. (2013). Sensor fault and patient anomaly detection and classification in medical wireless sensor networks. In 2013 IEEE international conference on communications (ICC), Budapest (pp. 4373–4378).
de Bruijn, B., Nguyen, T. A., Bucur, D., & Tei, K. (2016). Benchmark datasets for fault detection and classification in sensor data. In SENSORNETS 2016 Proceedings of the 5th international conference on sensor networks, Rome, Italy (pp. 185–195).
Yang, D., et al. (2018). A novel adaptive spectrum noise cancellation approach for enhancing heartbeat rate monitoring in a wearable device. IEEE Access, 6, 8364–8375.
Jauk, S., Kramer, D., & Leodolter, W. (2018). Cleansing and imputation of body mass index data and its impact on a machine learning based prediction model. Studies in Health Technology and Informatics, 116–123.
Nižetić Kosović, I., Božić, A., Mastelić, T., & Ivanković, D. (2019). Building soft sensors using artificial intelligence: Use case on daily solar radiation. In 3rd International conference on smart and sustainable technologies.
Yan, X., Xie, H., & Tong, W. (2011). A multiple linear regression data predicting method using correlation analysis for wireless sensor networks. In Proceedings of 2011 cross strait quad-regional radio science and wireless technology conference, Harbin (pp. 960–963).
Qu, X., & Kim, H. J. (2014). Enhanced discriminant linear regression classification for face recognition. In 2014 IEEE ninthinternational conference on intelligent sensors, sensor networks and information processing (ISSNIP), Singapore, (pp. 1–5).
Tsang, S., Kao, B., Yip, K. Y., Ho, W., & Lee, S. D. (2011). Decision trees for uncertain data. IEEE Transactions on Knowledge and Data Engineering, 23(1), 64–78.
Sugiarto, B., & Sustika, R. (2016). Data classification for air quality on wireless sensor network monitoring system using decision tree algorithm. In 2016 2nd International conference on science and technology-computer (ICST), Yogyakarta (pp. 172–176).
Ahmadi, A., et al. (2014). Automatic activity classification and movement assessment during a sports training session using wearable inertial sensors. In 201411th International conference on wearable and implantable body sensor networks, Zurich (pp. 98–103).
Rahman, M. J., & Morshed, B. I. (2019). Improving accuracy of inkjet printed core body WRAP temperature sensor using random forest regression implemented with an android app. In 2019 United States national committee of URSI national radio science meeting (USNC-URSI NRSM), Boulder, CO, USA (pp. 1–2).
Al-Milli, N., & Almobaideen, W. (2019). Hybrid neural network to impute missing data for IoT applications. In 2019 IEEE Jordan international joint conference on electrical engineering and information technology (JEEIT), Amman, Jordan (pp. 121–125).
Ahmadi, H., & Bouallegue, R. (2015). Comparative study of learning-based localization algorithms for wireless sensor networks: Support vector regression, neural network and naïve bayes. In 2015 International wireless communications and mobile computing conference (IWCMC), Dubrovnik (pp. 1554–1558).
Yang, D., Chhatre, N., Campi, F., & Menon, C. (2016). Feasibility of support vector machine gesture classification on a wearable embedded device. In 2016 IEEE Canadian conference on electrical and computer engineering (CCECE), Vancouver, BC (pp. 1–4).
Banos, O., Garcia, R., Holgado, J. A., Damas, M., Pomares, H., Rojas, I., Saez, A., & Villalonga, C. (2014). mHealthDroid:a novel framework for agile development of mobile health applications. In Proceedings of the 6th international work-conference on ambient assisted living an active ageing (IWAAL 2014), Belfast, Northern Ireland.
Banos, O., Villalonga, C., Garcia, R., Saez, A., Damas, M., Holgado, J. A., et al. (2015). Design, implementation and validation of a novel open framework for agile development of mobile health applications. BioMedical Engineering OnLine, 14(S2:S6), 1–20.
Rahman, G., & Islam, Z. (2013). Missing value imputation using decision trees and decision forests by splitting and merging records: Two novel techniques. Knowledge-Based Systems, 53, 51–65.
Rennie, K. L., Hemingway, H., Kumari, M., Brunner, E., Malik, M., & Marmot, M. (2003). Effects of moderate and vigorous physical activity on heart rate variability in a british study of civil servants. American Journal of Epidemiology, 158(2), 135–143.
Zhang, Z., Pi, Z., & Liu, B. (2015). TROIKA: A general framework for heart rate monitoring using wrist-type photoplethysmographic signals during intensive physical exercise. IEEE Transactions on Biomedical Engineering, 62(2), 522–531.
Long, X., Yin, B., & Aarts, R. M. (2009). Single-accelerometer-based daily physical activity classification. In 2009 Annual international conference of the IEEE engineering in medicine and biology society, Minneapolis, MN (pp. 6107–6110).
Gyllensten, I. C., & Bonomi, A. G. (2011). Identifying types of physical activity with a single accelerometer: Evaluating laboratory-trained algorithms in daily life. IEEE Transactions on Biomedical Engineering, 58(9), 2656–2663.
Al-Fatlawi, A. H., Fatlawi, H. K., & Ling, S. H. (2017). Recognition physical activities with optimal number of wearable sensors using data mining algorithms and deep belief network. In 2017 39th Annual international conference of the IEEE engineering in medicine and biology society (EMBC), Seogwipo (pp. 2871–2874).
Awais, M., Palmerini, L., & Chiari, L. (2016). Physical activity classification using body-worn inertial sensors in a multi-sensor setup. In 2016 IEEE 2nd International forum on research and technologies for society and industry leveraging a better tomorrow (RTSI), Bologna (pp. 1–4).
Fahim, M., Khattak, A. M., Aleem, S., & Katheeri, H. A. (2017). Physical activity recognizer based on multimodal sensors in smartphone for ubiquitous-lifecare services. In 2017 IEEE AFRICON, Cape Town (pp. 524–529).
Li, P., Wang, Y., Tian, Y., Zhou, T., & Li, J. (2017). An automatic user-adapted physical activity classification method using smartphones. IEEE Transactions on Biomedical Engineering, 64(3), 706–714.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Koren, A., Jurčević, M. & Prasad, R. Comparison of Data-Driven Models for Cleaning eHealth Sensor Data: Use Case on ECG Signal. Wireless Pers Commun 114, 1501–1517 (2020). https://doi.org/10.1007/s11277-020-07435-7
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11277-020-07435-7