Abstract
High-speed, reliable cardiac surveillance system that can detect cardiac arrest even before 24 h is the major goal of this study. The detection is based on the Kalman filter algorithm and advanced fast Fourier transform (FFT) compressor algorithm. A number of cardiac arrest techniques have been documented in contemporary literature. Irregular ventricular fibrillation or cardiovascular disorders are the main reason for abrupt cardiac failure. The suggested enhanced heart monitoring device continuously records electrocardiogram (ECG) readings. The preprocessing block boosts and filters the ECG signal in order to avoid interference with the power line and high frequency overlaps. The digital analog converter transforms a digital sample of the analogue ECG signal and uses the Kalman filter as an advance processing method for future ECG samples 24 h a day. FFT algorithm proposed to analyze the cardiac symptoms and their diagnosis, using the ECG test signal and the ECG reference signals similarity and dissimilarity approaches (Kalman predicted ECG Samples). After the irregularity is recognized, the warning feedback is sent to the receiver.
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References
Peters, A.E., Keeley, E.C.: Trends and predictors of participation in cardiac rehabilitation following acute myocardial infarction: data from the behavioral risk factor surveillance system. J. Am. Heart Assoc. 7(1), e007664 (2017)
Palanivel Rajan, S., Sukanesh, R.: Experimental studies on intelligent, wearable and automated wireless mobile tele-alert system for continuous cardiac surveillance. J. Appl. Res. Technol. 11(1), 133–143 (2013)
Brovko, O., Wiberg, D.M., Arena, L., Bellville, J.W.: The extended Kalman filter as a pulmonary blood flow estiṁator. Automatica 17(1), 213–220 (1981)
Li, Q., Mark, R.G., Clifford, G.D.: Robust heart rate estimation from multiple asynchronous noisy sources using signal quality indices and a Kalman filter. Physiol. Measur. 29(1), 15 (2007)
Zhang, Q., et al.: Cuff-less blood pressure measurement using pulse arrival time and a Kalman filter. J. Micromech. Microeng. 27(2), 024002 (2017)
Singh, M.K., Singh, A.K., Singh, N.: Multimedia analysis for disguised voice and classification efficiency. Multimedia Tools and Applications 78(20), 29395–29411 (2018). https://doi.org/10.1007/s11042-018-6718-6
Shamsollahi, M.B.: ECG denoising and compression using a modified extended Kalman filter structure. IEEE Trans. Biomed. Eng. 55(9), 2240–2248 (2008)
Singh, M. K., Singh, N., Singh, A. K. (2019, March). speaker's voice characteristics and similarity measurement using Euclidean distances. In 2019 International Conference on Signal Processing and Communication (ICSC), pp. 317–322. IEEE, New York (2019)
Punyavathi, G., Neeladri, M., Singh, M. K.: Vehicle tracking and detection techniques using IoT. In: Materials Today: Proceedings (2021)
Veerendra, G., Swaroop, R., Dattu, D. S., Jyothi, C. A., Singh, M. K.: Detecting plant Diseases, quantifying and classifying digital image processing techniques. In: Materials Today: Proceedings (2021)
Padma, U., Jagadish, S., Singh, M. K.: Recognition of plant’s leaf infection by image processing approach. Materials Today: Proceedings (2021)
Satya, P.M., Jagadish, S., Satyanarayana, V., Singh, M.K.: Stripe noise removal from remote sensing images. In: 2021 6th International Conference on Signal Processing. Computing and Control (ISPCC), pp. 233–236. IEEE, New York (2021)
Nandini, A., Kumar, R.A., Singh, M.K.: Circuits based on the memristor for fundamental operations. In: 2021 6th International Conference on Signal Processing. Computing and Control (ISPCC), pp. 251–255. IEEE, New York (2021)
Singh, M.K., Singh, A.K., Singh, N.: Disguised voice with fast and slow speech and its acoustic analysis. Int. J. Pure Appl. Math 11(14), 241–246 (2018)
Sasilatha, T.: Investigations on cardiac monitoring system using modified Kalman filter. (2017)
Liu, Y., Wang, L., Qiu, Z., Chen, X.: A dynamic force reconstruction method based on modified Kalman filter using acceleration responses under multi-source uncertain samples. Mech. Syst. Sig. Process. 159, 107761 (2021)
Singh, M.K., Singh, A.K., Singh, N.: Multimedia utilization of non-computerized disguised voice and acoustic similarity measurement. Multimedia Tools and Applications 79(47–48), 35537–35552 (2019). https://doi.org/10.1007/s11042-019-08329-y
Kumar, A.: Design of secure image fusion technique using cloud for privacy-preserving and copyright protection. Int. J. Cloud Appl. Comput. 9(3), 22–36 (2019)
Kumar, A., Zhang, Z.J., Lyu, H.: Object detection in real time based on improved single shot multi-box detector algorithm. EURASIP J. Wirel. Commun. Netw. 2020(1), 1–18 (2020). https://doi.org/10.1186/s13638-020-01826-x
Kumar, A.: A review on implementation of digital image watermarking techniques using LSB and DWT. In: The Third International Conference on Information and Communication Technology for Sustainable Development (ICT4SD 2018), held during August 30–31, 2018 at Hotel Vivanta by Taj, Goa, India
Sostric, D., Mester, G., Dorner, S.: ECG simulation and integration of Kalman filter in cardio pediatric cases. Interdiscip. Descrip. Complex Syst. 17(3-B), 615–628 (2019)
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Urmila, S., Kumar, R.A., Singh, M.K. (2022). Cardiac Surveillance System Using by the Modified Kalman Filter. In: Kumar, A., Fister Jr., I., Gupta, P.K., Debayle, J., Zhang, Z.J., Usman, M. (eds) Artificial Intelligence and Data Science. ICAIDS 2021. Communications in Computer and Information Science, vol 1673. Springer, Cham. https://doi.org/10.1007/978-3-031-21385-4_10
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