Abstract
The ability to predict patient outcomes is important for clinical triage, which is the process of assessing severity and assigning appropriate priority of treatment for large numbers of patients. In this study, we present an automatic prognosis system for patient outcome prediction with heart rate variability (HRV) and traditional vital signs. Support vector machine (SVM) and extreme learning machine (ELM) are employed as predictors, and SVM with linear kernel is reported to perform the best in general. In the experiments, the combination of HRV measures and vital signs is found to be more closely associated with patient outcome than either HRV or vital signs. Moreover, two new segment based methods are proposed to improve the predictive accuracy, where several sets of HRV measures are calculated from non-overlapped segments for each patient and final decision is made through the majority voting rule. The results reveal that the segment based methods are able to enhance the prediction performance significantly.
Similar content being viewed by others
References
Asl, B. M., Setarehdan, S. K., & Mohebbi, M. (2008). Support vector machine-based arrhythmia classification using reduced features of heart rate variability signal. Artificial Intelligence in Medicine, 44, 51–64.
Burges, C. J. C. (1998). A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2, 121–167.
Cerutti, S., Goldberger, A. L., & Yamamoto, Y. (Eds.) (2006). Special issue on recent advances in heart rate variability signal processing and interpretation. IEEE Transactions on Biomedical Engineering, 53.
Chang, C. C., & Lin, C. J. (2001). Libsvm: A library for support vector machines.
Cowan, M. J. (1995). Measurement of heart rate variability. Western Journal of Nursing Research, 17, 32–48.
Duda, R. O., Hart, P. E., & Stork, D. G. (2001). Pattern classification. New York: Wiley.
Ferrario, M., Signorini, M. G., Magenes, G., & Cerutti, S. (2006). Comparison of entropy-based regularity estimators: Application to the fetal heart rate signal for the identification of fetal distress. IEEE Transactions on Biomedical Engineering, 53, 119–125.
Goldberger, A. L., Amaral, L. A. N., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., et al. (2000). Physiobank, physiotoolkit, and physionet: Components of a new research resource for complex physiologic signals. Circulation, 101, e215–e220.
Guzzetti, S., Mezzetti, S., Magatelli, R., Porta, A., De Angelis, G., Rovelli, G., et al. (2000). Linear and non-linear 24 h heart rate variability in chronic heart failure. Autonomic Neuroscience, 86, 114–119.
Han, J. W., & Kamber, M. (2006). Data mining: Concepts and techniques. San Francisco: Morgan Kaufmann.
Huang, G. B., Zhu, Q. Y., & Siew, C. K. (2006). Extreme learning machine: Theory and applications. Neurocomputing, 70, 489–501.
Isler, Y., & Kuntalp, M. (2007). Combining classical HRV indices with wavelet entropy measures improves to performance in diagnosing congestive heart failure. Computers in Biology and Medicine, 37, 1502–1510.
Jain, A. K., Duin, R. P. W., & Mao, J. C. (2000). Statistical pattern recognition: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22, 4–37.
Kampouraki, A., Manis, G., & Nikou, C. (2009). Heartbeat time series classification with support vector machines. IEEE Transactions on Information Technology in Biomedicine, 13(4), 512–518.
Khandoker, A. H., Palaniswami, M., & Karmakar, C. K. (2009). Support vector machines for automated recognition of obstructive sleep apnea syndrome from ECG recordings. IEEE Transactions on Information Technology in Biomedicine, 13, 37–48.
Kohler, B. U., Hennig, C., & Orglmeister, R. (2002). The principles of software QRS detection. IEEE Engineering in Medicine and Biology Magazine, 21, 42–57.
Kuncheva, L. I. (2005). Combining pattern classifiers, methods and algorithms. New York: Wiley Interscience.
Kung, S. Y., Luo, Y. H., & Mak, M. W. (2010). Feature selection for genomic signal processing: Unsupervised, supervised, and self-supervised scenarios. Journal of Signal Processing Systems. doi:10.1007/s11265-008-0273-8.
Mariani, P., Saeed, M. U., Potti, A., Hebert, B., Sholes, K., Lewis, M. J., et al. (2006). Ineffectiveness of the measurement of ‘routine’ vital signs for adult inpatients with community-acquired pneumonia. International Journal of Nursing Practice, 12, 105–109.
Mietus, J. E., Peng, C. K., Ivanov, P., & Goldberger, A. L. (2000). Detection of obstructive sleep apnea from cardiac interbeat interval time series. Computers in Cardiology, 27, 753–756.
Niskanen, J.-P., Tarvainen, M. P., Ranta-aho, P. O., & Karjalainen, P. A. (2004). Software for advanced HRV analysis. Computer Methods and Programs in Biomedicine, 76, 73–81.
Norris, P. R., Morris, J. A., Ozdas, A., Grogan, E. L., & Williams, A. E. (2005). Heart rate variability predicts trauma patient outcome as early as 12 h: Implications for military and civilian triage. Journal of Surgical Research, 129, 122–128.
Task Force of the European Society of Cardiology the North American Society of Pacing Electrophysiology (1996). Heart rate variability: Standards of measurement, physiological interpretation, and clinical use. Circulation, 93, 1043–1065.
Ong, M. E. H., Padmanabhan, P., Chan, Y. H., Lin, Z., Overton, J., Ward, K. R., et al. (2008). An observational, prospective study exploring the use of heart rate variability as a predictor of clinical outcomes in pre-hospital ambulance patients. Resuscitation, 78, 289–297.
Padmanabhan, P., Lin, Z., Huang, G.-B., & Ong, M. E. H. (2008). Patient classification based on pre-hospital heart rate variability. In Proceedings of IEEE Asia Pacific conference on circuits and systems. Macao, China.
Padmanabhan, P., Lin, Z., Ong, M. E. H., Ser, W., & Huang, G.-B. (2007). Automatic extraction of HRV sequences from noisy ECG data for reliable analysis and telediagnosis. In Proceedings of international conference on telehealth. Montreal, Quebec, Canada.
Pinna, G. D., Maestri, R., & Sanarico, M. (1996). Effects of record length selection on the accuracy of spectral estimates of heart rate variability: A simulation study. IEEE Transactions on Biomedical Engineering, 43, 754–757.
Polikar, R. (2006). Ensemble based systems in decision making. IEEE Circuits and Systems Magazine, 6, 21–45.
Seely, A. J., & Macklem, P. T. (2004). Complex systems and the technology of variability analysis. Critical Care, 8, R367–R384.
Serre, D. (2002). Matrices: Theory and applications. New York: Springer.
Stys, A., & Stys, T. (1998). Current clinical applications of heart rate variability. Clinical Cardiology, 21, 719–724.
Yseboodt, L., Nil, M. D., Huisken, J., Berekovic, M., Zhao, Q., Bouwens, F., et al. (2009). Design of 100 \(\upmu\)w wireless sensor nodes for biomedical monitoring. Journal of Signal Processing Systems, 57, 107–119.
Zhu, Q. Y., Qin, A. K., Suganthan, P. N., & Huang, G. B. (2005). Evolutionary extreme learning machine. Pattern Recognition, 38, 1759–1763.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Liu, N., Lin, Z., Koh, Z. et al. Patient Outcome Prediction with Heart Rate Variability and Vital Signs. J Sign Process Syst 64, 265–278 (2011). https://doi.org/10.1007/s11265-010-0480-y
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11265-010-0480-y