Abstract
In the medicine, the implication of individual differences has frequently been emphasized. Gender- and age-related differences can be mentioned as the most important individual parameters. On the other hand, electrocardiogram (ECG) signals are the subject of these differences. However, limited information is available regarding these individual dissimilarities in ECG dynamics. This study was aimed to evaluate gender and age differences by means of novel Poincare section indices. Our focus was to detect and classify dynamical behaviors of the ECG trajectories using three binary classification strategies: (1) gender-, (2) age-, (3) gender- and age-based classification. After constructing the 2D phase space of ECG, linear Poincare sections in distinct angles were developed and some geometric indices were extracted. The effect of delayed phase space on ECG measures was also inspected. We tested our algorithm on 79 healthy subjects. Using support vector machine, the maximum correct rate of 93.33% was achieved for the gender- and age-based classification strategies. Considering the information of both age and gender, the highest rate was 94.66%. The best results were achieved with delays of 5 and 6. In conclusion, our results showed that basin geometry of the ECG phase states is affected by individual differences.
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Arafat, M.A., Chowdhury, A.W., Hasan, K.: A simple time domain algorithm for the detection of ventricular fibrillation in electrocardiogram. SIVP 5(1), 1–10 (2011)
Bassiouni, M.M., El-Dahshan, E.S.A., Khalefa, W., Salem, A.M.: Intelligent hybrid approaches for human ECG signals identification. SIVP 12(5), 941–949 (2018)
Fomin, A., Da Silva, C., Ahlstrand, M., Sahlén, A., Lund, L., Stahlberg, M., Gabrielsen, A., Manouras, A.: Gender differences in myocardial function and arterio-ventricular coupling in response to maximal exercise in adolescent floor-ball players. BMC Sports Sci. Med. Rehabil. 6, 24 (2014)
Mieszczanska, H., Pietrasik, G., Piotrowicz, K., McNitt, S., Moss, A.J., Zareba, W.: Gender-related differences in electrocardiographic parameters and their association with cardiac events in patients after myocardial infarction. Am. J. Cardiol. 101(1), 20–24 (2008)
Kolb, B., Whishaw, I.Q.: An Introduction to Brain and Behavior, 2nd edn. Worth Publisher, New York (2005)
Xue, J., Farrell, R.M.: How can computerized interpretation algorithms adapt to gender/age differences in ECG measurements? J. Electrocardiol. 47(6), 849–855 (2014)
Patro, K.K., Kumar, P.R.: Effective feature extraction of ECG for biometric application. Procedia Comput Sci 115, 296–306 (2017)
Kingsbury, N.: The dual-tree complex wavelet transform: a new technique for shift invariance and directional filters. In: Proceedings of the 8th IEEE DSP Workshop, Utah, August 9–12, 1998, Paper no. 86
Goshvarpour, A., Abbasi, A., Goshvarpour, A.: Do men and women have different ECG responses to sad pictures? Biomed. Signal Process. Control 38, 67–73 (2017)
Kapidzic, A., Platisa, M.M., Bojic, T., Kalauzi, A.: Nonlinear properties of cardiac rhythm and respiratory signal under paced breathing in young and middle-aged healthy subjects. Med. Eng. Phys. 36(12), 1577–1584 (2014)
Anishchenko, T., Igosheva, N., Yakusheva, T., Glushkovskaya-Semyachkina, O., Khokhlova, O.: Normalized entropy applied to the analysis of interindividual and gender-related differences in the cardiovascular effects of stress. Eur. J. Appl. Physiol. 85, 287–298 (2001)
Ryan, S.M., Goldberger, A.L., Pincus, S.M., Mietus, J., Lipsitz, L.A.: Gender- and age-related differences in heart rate: are women more complex than men? J. Am. Coll. Cardiol. 24, 1700–1707 (1994)
Beckers, F., Verheyden, B., Aubert, A.E.: Aging and nonlinear heart rate control in a healthy population. Am. J. Physiol. Heart Circ. Physiol. 290, H2560–H2570 (2006)
Pikkujamsa, S.M., Makikallio, T.H., Sourander, L.B., Raiha, I.J., Puukka, P., Skytta, J., Peng, C.K., Goldberger, A.L., Huikuri, H.V.: Cardiac interbeat interval dynamics from childhood to senescence: comparison of conventional and new measures based on fractals and chaos theory. Circulation 100, 393–399 (1999)
Karimui, R.Y., Azadi, S.: Cardiac arrhythmia classification using the phase space sorted by Poincare sections. Biocybern Biomed Eng 37, 690–700 (2017)
Parvaneh, S., Golpayegani, M.R.H., Firoozabadi, M., Haghjoo, M.: Predicting the spontaneous termination of atrial fibrillation based on Poincare section in the electrocardiogram phase space. Proc Inst Mech Eng H 226(1), 3–20 (2011)
Fang, S.C., Chan, H.L.: Human identification by quantifying similarity and dissimilarity in electrocardiogram phase space. Pattern Recogn. 42, 1824–1831 (2009)
Yang, S.: Nonlinear signal classification in the framework of high-dimensional shape analysis in reconstructed state space. IEEE Trans. Circuits Syst. II Express Briefs 52, 512–516 (2005)
Yang, S.: Nonlinear signal classification using geometric statistical features in state space. Electron. Lett. 40, 780–781 (2004)
Lugovaya, T.S.: Biometric human identification based on electrocardiogram. Master’s Thesis. Faculty of Computing Technologies and Informatics, Electrotechnical University “LETI”, Saint-Petersburg (June 2005)
Najarian, K., Splinter, R.: Biomedical signal and image processing, 2nd edn, pp. 1–405. Taylor & Francis Group, LLC, CRC Press, New York (2012)
Wang, X.W., Nie, D., Lu B.L.: EEG-based emotion recognition using frequency domain features and support vector machines. In: Lu, B.L., Zhang, L., Kwok, J. (eds) Neural Information Processing, ICONIP 2011, Lecture Notes in Computer Science, Vol. 7062, pp. 734-743. Springer, Berlin (2011)
Rijnbeek, P.R., van Herpen, G., Bots, M.L., Man, S., Verweij, N., Hofman, A., Hillege, H., Numans, M.E., Swenne, C.A., Witteman, J.C., Kors, J.A.: Normal values of the electrocardiogram for ages 16–90 years. J. Electrocardiol. 47(6), 914–921 (2014)
Goshvarpour, A., Abbasi, A., Goshvarpour, A.: Sleep loss effects on affective responses of women and men using ECG characteristics. Biomed. Eng. Appl. Basis Commun. 29(5), 1750032 (2017)
Tripathy, R.K., Acharya, A., Choudhary, S.K.: Gender classification from ECG signal analysis using least square support vector machine. Am. J. Signal Process. 5(2), 145–149 (2012)
Lin, F., Wu, Y., Zhuang, Y., Long, X., Xu, W.: Human gender classification: a review. Int. J. Biom. 8(3/4), 275–300 (2016)
Wigging, M., Saad, A., Litt, B., Vachtsevanos, G.: Evolving a Bayesian classifier for ECG-based age classification in medical applications. Appl. Soft Comput. 8(1), 599–608 (2008)
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Goshvarpour, A., Goshvarpour, A. Gender and age classification using a new Poincare section-based feature set of ECG. SIViP 13, 531–539 (2019). https://doi.org/10.1007/s11760-018-1379-5
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DOI: https://doi.org/10.1007/s11760-018-1379-5