Physical activities recognition from ambulatory ECG signals using neuro-fuzzy classifiers and support vector machines

References

• Shojaei-Baghini, M., Lal, R.K., and Sharma, D.K., 2005, a low-power and compact analog CMOS processing chip for portable ECG recorders. Asian Solid State Circuit Conference, A-SSCC’05, Hshinchu, Taiwan
   Google Scholar
• Okada, Y., Yoto, Y.-T., Suzuki, T.-A., Sugiura, T., and Sakuragawa, S., 2010, Development of a wearable ECG recorder for measuring daily stress. International Conference on Information Science and Applications (ICISA), 21–23 April 2010, pp. 1–5; Seoul, Korea
   Google Scholar
• Park, C., Chou, P.H., Bai, Y., Matthews, R., and Hibbs, A., 2006, An ultra-wearable wireless low power ECG monitoring system. Biomedical Circuits and Systems Conference (BioCAS 2006), November 29–December 1. pp. 241–244; London, UK
   Google Scholar
• Chen, X., Ho, C.T., Lim, E.T., and Kyaw, T.Z., 2007, Cellular phone based online ECG processing for ambulatory and continuous detection. Computers in Cardiology, 34, 653–656
   Google Scholar
• Fensli, R., Gunnarson, E., and Gundersen, T., 2005, A wearable ECG-recording system for continuous arrhythmia monitoring in a wireless tele-home-care situation. 18th IEEE Symposium on Computer based Medical Systems, Dublin, Ireland, 23–24 June 2005, pp. 407–412
   Google Scholar
• Karlen, W., Mattiussi, C.L., and Floreano, D., 2009, Sleep and wake classification with ECG and respiratory effort signals. IEEE Transactions on Biomedical Circuits and Systems, 3, 71–78
   PubMedGoogle Scholar
• Lin, C.-T., Chang, K.-C., Lin, C.-L., Chiang, C.-C., Lu, S.-W., Chang, S.-S., Lin, B.-S., Liang, H.-Y., Chen, R.-J., Lee, Y.-T., and Ko, L.-W., 2010, An intelligent telecardiology system using a wearable and wireless ECG to detect atrial fibrillation. IEEE Transactions on Information Technology in Biomedicine, 14, 726–733
   PubMedGoogle Scholar
• Figueiredo, C.P., Becher, K., Hoffmann, K.-P., and Mendes, P.M., 2010, Low power wireless acquisition module for wearable health monitoring systems. 32nd Annual International Conference of the IEEE EMBS (EMBC 2010), August 31–September 4, 2010; Buenos Aires, Argentina
   Google Scholar
• Oresko, J.J., Jin, Z., Cheng, J., Huang, S., Sun, Y., Duschl, H., and Cheng, A.C., 2010, A wearable smartphone-based platform for real-time cardiovascular disease detection via electrocardiogram processing. IEEE Transactions on Information Technology in Biomedicine, 14, 734–740
   PubMedGoogle Scholar
• Fletcher, R.R., and Kulkarni, S., 2010, Clip-on wireless wearable microwave sensor for ambulatory cardiac monitoring. 32nd Annual International Conference of the IEEE EMBS (EMBC 2010), August 31–September 4, 2010; Buenos Aires, Argentina
   Google Scholar
• Gallego, J., Lemos, D., Meneses, G.A., and Hernandez, A.M., 2010, Development of a wearable vital signs monitor for healthcare. 32nd Annual International Conference of the IEEE EMBS (EMBC 2010), August 31–September 4, 2010. Buenos Aires, Argentina
   Google Scholar
• Jeong, D.-U., and Kim, S.-J., 2008, Development of a technique for cancelling motion artifact in ambulatory ECG monitoring system. 3rd International Conference on Convergence and Hybrid Information Technology (ICCIT), Nov. 11–13 2008, pp. 954–961. Busan, Korea
   Google Scholar
• Pawar, T., Anantakrishnan, N.S., Chaudhuri, S., and Duttagupta, S.P., 2007, Transition detection in body movement activities for wearable ECG. IEEE Transactions on Biomedical Engineering, 54, 1149–1152
   PubMedGoogle Scholar
• Thakor, N.V., and Zhu, Y.-S., 1991, Applications of adaptive filtering to ECG analysis: Noise cancellation and arryhthmia detection. IEEE Transactions on Biomedical Engineering, 38, 785–794
   PubMed Web of Science ®Google Scholar
• Nimbargi, V.S., V. M. Gadre, V.M., and Mukherji, S., 2005, Characterization of ECG motion artifacts using wavelet transform and neural networks. Indian Conference on Medical Informatics and Telemedicine, Kharagpur, West Bengal, India
   Google Scholar
• Pawar, T., Chaudhuri, S., and Duttagupta, S.P., 2007, Body movement activity recognition for ambulatory cardiac monitoring. IEEE Transactions on Biomedical Engineering, 54, 874–882
   PubMedGoogle Scholar
• Pawar, T., Anantakrishnan, N.S., Chaudhuri, S., and Duttagupta, S.P., 2008, Impact of ambulation in wearable-ECG. Springer Link Annals of Biomedical Engineering, 36, 1547–1557
   PubMedGoogle Scholar
• Chaudhuri, S., Pawar, T., and Duttagupta, S., 2009, Ambulation analysis in wearable ECG (New York: Springer)
   Google Scholar
• Parkka, J., Ermes, M., Korpipaa, P., Mantyjarvi, J., Peltola, J., Korhonen, I., Technol, V., and Tampere, F., 2006, Activity classification using realistic data from wearable sensors. IEEE Transactions on Information Technology in Biomedicine, 10, 119–128
   PubMed Web of Science ®Google Scholar
• Ermes, M., Parkka, J., Mantyjarvi, J., and Korhonen, I., 2008, Detection of daily activities and sports with wearable sensors in controlled and uncontrolled conditions. IEEE Transactions on Information Technology in Biomedicine, 12, 20–26
   PubMedGoogle Scholar
• Lin, C.-W., Yang, Y.-T.C., Wang, J.-S., and Yang, Y.-C., 2012, A wearable sensor module with a neural-network- based activity classification algorithm for daily energy expenditure estimation. IEEE Transactions on Information Technology in Biomedicine, 16, 991–998
   PubMedGoogle Scholar
• Parkka, J., Ennes, M., and van Gils, M., 2010, Automatic feature selection and classification of physical and mental load using data from wearable sensors. 10th IEEE International Conference on Information Technology and Applications in Biomedicine (ITAB), 2–5 November 2010, pp. 1–5; Greece
   Google Scholar
• Li, M., Rozgić, V., Thatte, G., Lee, S., Emken, B.A., Annavaram, M., Mitra, U., Spruijt-Metz, D., and Narayanan, S., 2010, Multimodal physical activity recognition by fusing temporal and cepstral information. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 18, 369–380
   PubMed Web of Science ®Google Scholar
• Shing, J., and Jang, R., 1993, ANFIS: Adaptive Network based Fuzzy Inference System. IEEE Transactions on Systems, Man and Cybernetics, 23, 665–685
   Web of Science ®Google Scholar
• Nauck, D., and Kruse, R., 1995, NEFCLASS - A Neuro-Fuzzy approach for the classification of data. Proceedings of the 1995 ACM Symposium on Applied Computing, Feb. 26–28, 1995, pp. 461–465; Nashville, TN
   Google Scholar
• Osowski, S., and Hoai, L.T., 2004, Analysis of features for efficient ECG signal classification using neuro-fuzzy network. IEEE International Joint Conference on Neural Networks, Volume 3, 25–29 July 2004, pp. 2443–2448; Budapest, Hungary
   Google Scholar
• Nazmy, T.M., El-Messiry, H., and Al-Bokhity, B., 2010, Adaptive Neuro-Fuzzy inference system for classification of ECG signals. 7th Int. Conference on Informatics and Systems (INFOS), 28–30 March 2010, pp. 1–6; Cairo, Egypt
   Google Scholar
• Tonekabonipour, H., Emam, A., Teshnelab, M., and Shoorehdeli, M.A., 2010, Comparison of neuro-fuzzy approaches with artificial neural networks for the detection of Ischemia in ECG signals. 2010 IEEE International Conference on Systems, Man and Cybernetics (SMC), 10-13 October, 2010, pp. 4045–4048; Istanbul, Turkey
   Google Scholar
• Tonekabonipour, H., Emam, A., Teshnelab, M., and Shoorehdeli, M.A., 2011, Ischemia prediction via ECG using MLP and RBF predictors with ANFIS classifiers. 2011 7th International Conference on Natural Computation (ICNC), 26–28 July, 2011, pp. 776–780; Shanghai, China
   Google Scholar
• Khandoker, A.H., Palaniswami, M., and 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
   PubMedGoogle Scholar
• Mehta, S.S., and Lingayat, N.S., 2007, ECG pattern classification using support vector machine. Advances in pattern recognition. 6th International Conference on Advances in Pattern Recognition (ICAPR 2007), pp. 295–298, ISI, Kolkata, India, 2–4 January, 2007
   Google Scholar
• Mehta, S.S., and Lingayat, N.S., 2007, Support vector machine for cardiac beat detection in single lead electrocardiogram. IAENG International Journal of Applied Mathematics, 36, 1630–1635
   Google Scholar
• Ubeyli, E.D., 2007, ECG beats classification using multiclass support vector machines with error correcting output codes. Digital Signal Processing, 17, 675–684
   Web of Science ®Google Scholar
• Zheng, Q., Chen, C., Li, Z., Huang, A., Jiao, B., Duan, X., and Xie, L., 2013, A novel multi-resolution SVM (MR-SVM) algorithm to detect ECG signal anomaly in WE-CARE project. 2013 Biosignals and Biorobotics Conference (BRC), 18–20 February, 2013, pp. 1–6; Rio De Janaiero, Brazil
   Google Scholar
• Kohli, N., Verma, N.K., and Roy, A., 2010, SVM based methods for arrhythmia classification in ECG. 2010 International Conference on Computer and Communication Technology (ICCCT), 17–19 September 2010, pp. 486–490; Allahabad, India
   Google Scholar
• Kallas, M., Francis, C., Kanaan, L., Merheb, D., Honeine, P., and Amoud, H., 2012, Multi-class SVM classification combined with kernel PCA feature extraction of ECG signals. 2012 19th International Conference on Telecommunications (ICT), 23–25 April, 2012, pp. 1–5; Jounieh, Labanon
   Google Scholar
• Shameer, F., and Sabiq, P.V., 2013, ECG beat classification using wavelets and SVM. 2013 International Conference on Information and Communication Technologies (ICT), 11–12 April, 2013, pp. 815–818; Thuckalay, India
   Google Scholar
• Ghosh, D., Midya, B.L., Koley, C., and Purkait, P., 2005, Wavelet aided SVM analysis of ECG signals for cardiac abnormality detection. 2005 IEEE INDICON, 11–13 December, 2005, pp. 9–13; Chennai, India
   Google Scholar
• Bsoul, M., Minn, H., Nourani, M., Gupta, G., and Tamil, L., 2010, Real-time sleep quality assessment using single-lead ECG and multi-stage SVM classifier. 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology (EMBC 2010), August 31–September 4, 2010, pp. 1178–1181; Buenos Aires, Argentina
   Google Scholar
• Almazaydeh, L., Elleithy, K., and Faezipour, M., 2012, Obstructive sleep apnea detection using SVM-based classification of ECG signal features. 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology (EMBC 2012), August 28–September 1, 2012, pp. 4938–4941; San Diego, CA, USA
   Google Scholar
• Zhang, M., and Sawchuk, A.A., 2013, human daily activity recognition with sparse representation using wearable sensors. IEEE Journal of Biomedical and Health Informatics, 17, 553–560
   Google Scholar
• Lee, Y.-D., Jung, S.-J., Seo, Y.-S., and Chung, W.-Y., 2008, Measurement of motion activity during ambulatory using pulse oximeter and triaxial accelerometer. 3rd International Conference on Convergence and Hybrid Information Technology (ICCIT), Nov. 11–13 2008, pp. 436–441. Busan, Korea
   Google Scholar