Abstract
This research is focused on a real world problem: the identification of a specific type of apnea disorder. Recently, a technique for evaluating and diagnosing of a certain type of apnea has been published carried out. In a brief, this technique proposes that a subject is given with two belts, to be placed on the thorax and on the abdomen, respectively; each belt includes a 3D accelerometer. In a sleep laboratory, the subject is monitored while sleeping and the apnea episodes are manually discovered and registered. Besides, during the test, the data from the sensors is gathered and segmented. The hypothesis of this study is that the diagnose of the apnea episodes can be accomplished using the data from a single 3D acceleration sensor. If successful, this technique for the diagnose of the apnea might reduce the costs of the tests as well as allow evaluating challenging cases, as those related with children or with elder people.
This study focus on the time series (TS) analysis to extract the most relevant patterns corresponding with the apneas episodes.Focusing on the analysis of the TS, this study will apply a well-known TS technique to extract the most relevant patterns. The main contributions of this study are i) to determine if a previous step for estimating the posture is needed, which is a very important decision in the design of the embedded solution, and ii) the evaluation of the hypothesis of diagnosing by means of a single 3D accelerometer.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Alonso, M.L., Canet, T., Cubel, M., Estivill, E., Fernandez, E., Gozal, D., Jurado, M.J., Lluch, A., Martínez, F., Merino-Andreu, M., Pin-Arboledas, G., Roure, N., Sanmartí, F., Sans, O., Segarra, F., Tomás, M., Terán, J.: Documento de consenso del síndrome de apneas-hipopneas durante el sueño en niños. Archivos de Bronconeumología 47, 1–18 (2011), http://www.sciencedirect.com/science/article/pii/S0300289611700254
Alonso, M.L., Terán, J., Cordero, J., González, M., Rodríguez, L., Viejo, J.L., Marañón, A.: Fiabilidad de la poligrafía respiratoria domiciliaria para el diagnóstico del síndrome de apneas/hipopneas durante el sueño. Análisis de costes Arch. Bronconeumol. 44(1), 22–28 (2008)
Berry, R., Brook, S.R., Gamaldo, C.E., Harding, S.M., Marcus, C.L., Vaughn, V.B., Tangredi, M.M.: The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specification. American Academy of Sleep Medicine (2012), http://www.aasmnet.org/Store/ProductDetails.aspx?pid=176
FITBIT co: Fitib website (2014), http://www.tbit.com
Hills, J., Lines, J., Baranauskas, E., Mapp, J., Bagnall, A.: Classification of time series by shapelet transformation. Data Mining and Knowledge Discovery 28(4), 851–881 (2014)
Keogh, E., Lin, J., Fu, A.: Hot sax: Efficiently finding the most unusual time series subsequence. In: Proceedings of the Fifth IEEE International Conference on Data Mining (ICDM 2005). IEEE Press (2005)
Kushida, C.A., Littner, M.R., Morgenthaler, T., Alessi, C.A., Bailey, D., Coleman, J.J., Friedman, L., Hirshkowitz, M., Kapen, S., Kramer, M., Lee-Chiong, T., Loube, D.L., Owens, J., Pancer, J.P., Wise, M.: Practice parameters for the indications for polysomnography and related procedures: an update for 2005. Sleep 28, 499–521 (2005)
Lapi, S., Lavorini, F., Borgioli, G., Calzolai, M., Masotti, L., Pistolesi, M., Fontana, G.A.: Respiratory rate assessments using a dual-accelerometer device. Respiratory Physiology and Neurobiology 191, 60–66 (2014)
Li, J., Keogh, E., Leonardi, S., Chiu, B.: A symbolic representation of time series, with implications for streaming algorithms. In: Proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery (2003)
Lloberes, P., Durán-Cantolla, J., Martínez-García, M.A., Marín, J.M., Ferrer, A., Corralf, J., Masa, J.F., Parra, O., Alonso-Álvarez, M.L., Terán, J.: Normativa sobre diagnóstico y tratamiento del síndrome de apneas-hipopneas del sueño. Arhivos de Bronconeumología 47 (2011)
Villar, J.R., Chira, C., González, S., Sedano, J., Trejo, J.M.: Stroke onset detection by means of hybrid artificial intelligent systems. Submitted to Integrated Computer Aided Engineering (2014)
Villar, J.R., González, S., Sedano, J., Chira, C., Trejo, J.M.: Human activity recognition and feature selection for stroke early diagnosis. In: Pan, J.-S., Polycarpou, M.M., Woźniak, M., de Carvalho, A.C.P.L.F., Quintián, H., Corchado, E. (eds.) HAIS 2013. LNCS (LNAI), vol. 8073, pp. 659–668. Springer, Heidelberg (2013)
Villar, J.R., González, S., Sedano, J., Chira, C., Trejo, J.M.: Hybrid systems for analyzing the movements during a temporary breath inability episode. In: Polycarpou, M., de Carvalho, A.C.P.L.F., Pan, J.-S., Woźniak, M., Quintian, H., Corchado, E. (eds.) HAIS 2014. LNCS (LNAI), vol. 8480, pp. 549–560. Springer, Heidelberg (2014)
Xie, H., Fedder, G.K., Sulouff, R.E.: 2.05 - accelerometers. In: Comprehensive Microsystems, pp. 135–180. Elsevier (2008), http://www.sciencedirect.com/science/article/pii/B9780444521903000537
Zhou, H., Hu, H.: Upper limb motion estimation from inertial measurements. International Journal of Information Technology 13(1) (2007)
Zhou, H., Hu, H.: Reducing drifts in the inertial measurements of wrist and elbow positions. IEEE Transactions on Instrumentation and Measurement 59(3), 575–585 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Alonso, M.L. et al. (2014). Data Analysis for Detecting a Temporary Breath Inability Episode. In: Corchado, E., Lozano, J.A., Quintián, H., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2014. IDEAL 2014. Lecture Notes in Computer Science, vol 8669. Springer, Cham. https://doi.org/10.1007/978-3-319-10840-7_16
Download citation
DOI: https://doi.org/10.1007/978-3-319-10840-7_16
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10839-1
Online ISBN: 978-3-319-10840-7
eBook Packages: Computer ScienceComputer Science (R0)