Abstract
A myriad of data is produced in intensive care units (ICU) even for short periods of time. This data is frequently used for monitoring patient’s immediate health status, not for real-time analysis because of technical challenges in real-time processing of such massive data. Data storage is also another challenge in making ICU data useful for retrospective studies. Therefore, it is important to know the minimal sampling frequency requirement to develop real-time analysis on ICU data and to develop a data storage plan. In this study, we have applied the Probabilistic Symbolic Pattern Recognition (PSPR) method in Paroxysmal Atrial Fibrillation (PAF) screening problem by analyzing electrocardiogram signals at different sampling frequencies varying from 128 Hz to 8 Hz. Our results show that using PSPR method, we can obtain a classification accuracy of 82.67% in identifying PAF subjects even when the test data is sampled at 8 Hz frequency (73.33% for 128 Hz). This classification accuracy drastically improved to 92% when other descriptive features were used along with PSPR features. The PSPR’s PAF screening ability at low sampling frequency indicates its potential for real-time analysis and wearable embedded computing applications.
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References
Milic, L.: Multirate Filtering for Digital Signal Processing: MATLAB Applications: MATLAB Applications. IGI Global (2009)
Johnson, A.E.W., et al.: MIMIC-III, a freely accessible critical care database. Sci. Data 3, 1–9 (2016)
Karlen, W., et al.: Multiparameter respiratory rate estimation from the photoplethysmogram. IEEE Trans. Biomed. Eng. 60(7), 1946–1953 (2013)
Abdul-Rahim, A.H., Lees, K.R.: Paroxysmal atrial fibrillation after ischemic stroke: how should we hunt for it? Expert Rev. Cardiovasc. Ther. 11(4), 485–494 (2013)
Al-Khatib, S.M., et al.: Observations on the transition from intermittent to permanent atrial fibrillation. Am. Heart J. 140(1), 142–145 (2000)
Lynn, K.S., Chiang, H.D.: A two-stage solution algorithm for paroxysmal atrial fibrillation prediction. Comput. Cardiol. 28, 405–407 (2001)
Yang, A.C.C., Yin, H.W.: Prediction of paroxysmal atrial fibrillation by footprint analysis. Comput. Cardiol. 28, 401–404 (2001)
Zong, W., Mukkamala, R., Mark, R.G.: A methodology for predicting paroxysmal atrial fibrillation based on ECG arrhythmia feature analysis. Comput Cardiol. 28, 125–128 (2001)
Maier, C., Bauch, M., Dickhaus, H.: Screening and prediction of paroxysmal atrial fibrillation by analysis of heart rate variability parameters. Comput. Cardiol. 28, 129–132 (2001)
Schreier, G., Kastner, P., Marko, W.: An automatic ECG processing algorithm to identify patients prone to paroxysmal atrial fibrillation. Comput. Cardiol. 28, 133–135 (2001)
Akbilgic, O., Howe, J.A., Davis, R.L.: Categorizing atrial fibrillation via symbolic pattern recognition. J. Med. Stat. Inform. 4(8), 1–9 (2016)
Keogh, E., et al.: Dimensionality reduction for fast similarity search in large time series databases. Knowl. Inf. Syst. 3(3), 263–286 (2001)
Yi, B.-K., Faloutsos, C.: Fast time sequence indexing for arbitrary Lp norms (2000)
Lin, J., Keogh, E., Lonardi, 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, pp. 2–11 (2003)
Lkhagva, B., Suzuki, Y., Kawagoe, K.: Extended SAX: extension of symbolic aggregate approximation for financial time series data representation, DEWS2006 4A-i8, vol. 7 (2006)
Deza, M.M., Deza, E.: Encyclopedia of Distances, pp. 1–583. Springer, Heidelberg (2009)
Goldberger, S.H., et al.: PhysioBank, PhysioToolkit, and PhysioNet components of a new research resource for complex physiologic signals. Circulation 101(23), 215–220 (2000)
Physionet. The PAF Prediction challenge database (2001). https://www.physionet.org/physiobank/database/afpdb/Accessed 17 Oct 2016]
Jiapu, P., Tompkins, W.J.: A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng. BME-32(3), 230–236 (1985)
Physionet. Computers in Cardiology Challenge 2001 Top scores (Final) (2001). https://www.physionet.org/challenge/2001/top-scores.shtml. Accessed 1 Jan 2017
Goldberger, A., McClennen, S., Swiryn, S.: Predicting the onset of Paroxysmal Atrial Fibrillation: The computers in cardiology challenge 2001. In: Computers in Cardiology 2001, pp. 113–116. IEEE (2001)
Mahajan, R., Viangteeravat, T., Akbilgic, O.: Improved detection of congestive heart failure via probabilistic symbolic pattern recognition and heart rate variability. Int. J. Med. Informatics (2017, in press)
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Mahajan, R., Kamaleswaran, R., Akbilgic, O. (2017). Effects of Varying Sampling Frequency on the Analysis of Continuous ECG Data Streams. In: Begoli, E., Wang, F., Luo, G. (eds) Data Management and Analytics for Medicine and Healthcare. DMAH 2017. Lecture Notes in Computer Science(), vol 10494. Springer, Cham. https://doi.org/10.1007/978-3-319-67186-4_7
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DOI: https://doi.org/10.1007/978-3-319-67186-4_7
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