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Open Science Index

Commenced in January 2007 Frequency: Monthly Edition: International Publications Count: 31108

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Use of Hierarchical Temporal Memory Algorithm in Heart Attack Detection
In order to reduce the number of deaths due to heart problems, we propose the use of Hierarchical Temporal Memory Algorithm (HTM) which is a real time anomaly detection algorithm. HTM is a cortical learning algorithm based on neocortex used for anomaly detection. In other words, it is based on a conceptual theory of how the human brain can work. It is powerful in predicting unusual patterns, anomaly detection and classification. In this paper, HTM have been implemented and tested on ECG datasets in order to detect cardiac anomalies. Experiments showed good performance in terms of specificity, sensitivity and execution time.
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[1] Chandola, V., Banerjee, A. and Kumar, V. (2009). Anomaly detection: A survey, ACM Computing Surveys (CSUR). 41(3), 1–72.
[2] Acuna, E. and Rodriguez, C. (2004). A meta analysis study of outlier detection methods in classification, Technical paper, Department of Mathematics, University of Puerto Rico at Mayaguez.
[3] Chandore, P. R. and Chatur, D. P. N. (2013). Hybrid approach for outlier detection over wireless sensor network real time data. International Journal Of Computer Science And Applications, 6(2).
[4] Malhotra, P., Vig, L., Shroff, G. and Agarwal, P. (2015, April). Long short term memory networks for anomaly detection in time series. In Proceedings (p. 89). Presses universitaires de Louvain.
[5] Jankov, D., Sikdar, S., Mukherjee, R., Teymourian, K. and Jermaine, C. (2017, June). Real-time High Performance Anomaly Detection over Data Streams: Grand Challenge. In Proceedings of the 11th ACM International Conference on Distributed and Event-based Systems (pp. 292-297). ACM.
[6] Loganathan, G., Samarabandu, J. and Wang, X. (2018, May). Sequence to Sequence Pattern Learning Algorithm for Real-Time Anomaly Detection in Network Traffic. In 2018 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) (pp. 1-4). IEEE.
[7] Edgeworth, F. Y. (1887). On discordant observations, Philosophical Magazine. To Appear in ACM Computing Surveys, 09 2009. 23(5), 364–375.
[8] Ahmad, S., Lavin, A., Purdy, S. and Agha, Z. (2017). Unsupervised real-time anomaly detection for streaming data. Neurocomputing.
[9] Lavin, A. and Ahmad, S. (2015). Evaluating Real-Time Anomaly Detection Algorithms–The Numenta Anomaly Benchmark. In Machine Learning and Applications (ICMLA), 2015 IEEE 14th International Conference on (pp. 38-44). IEEE.
[10] Ahmad, S. and Purdy, S. (2016). Real-Time Anomaly Detection for Streaming Analytics. arXiv preprint arXiv:1607.02480.
[11] Kerner, M. and Tammeme K. (2017). Hierarchical temporal memory implementation on FPGA using LFSR based spatial pooler address space generator. In Design and Diagnostics of Electronic Circuits and Systems (DDECS), 2017 IEEE 20th International Symposium on pp. 92–95. IEEE.
[12] M. Putic,A. J. Varshneya and M. R. Stan (2017). Hierarchical Temporal Memory on the Automata Processor. IEEE Micro, 37(1), 52-59.
[13] Fan, D., Sharad, M., Sengupta, A., and Roy, K. (2016). Hierarchical temporal memory based on spin-neurons and resistive memory for energy-efficient brain-inspired computing. IEEE transactions on neural networks and learning systems, 27(9), 1907-1919.
[14] Hawkins, J., and Ahmad, S. (2016). Why neurons have thousands of synapses, a theory of sequence memory in neocortex. Frontiers in neural circuits, 10.
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