Hidden Markov Model for The Heart Rate Variability Detection
Pooja Bhor1, Gurjot Singh Sodhi2, Dilbag Singh3
1Pooja Bhor, Apex Institute of Technology, Chandigarh University, Gharuan (Punjab), India.
2Gurjot Singh Sodhi, Apex Institute of Technology, Chandigarh University, Gharuan (Punjab), India.
3Dilbag Singh Apex Institute of Technology, Chandigarh University, Gharuan (Punjab), India.
Manuscript received on 18 June 2019 | Revised Manuscript received on 25 June 2019 | Manuscript published on 30 June 2019 | PP: 2494-2499 | Volume-8 Issue-5, June 2019 | Retrieval Number: E7620068519/19©BEIESP
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The signal processing is the approach which is applied to process digital signal data. The Electrocardiogram signals are applied to process the heart rate values. The heart rate variability detect has the three phases which are preprocessing, feature extraction and classification. In the previous approach, SVM classifier is applied for the heart rate variability detection. In this research work, Hidden Markov Model classifier is applied for the heart rate variability detection. The performance of proposed model is analyzed in terms of accuracy and execution time. The proposed algorithm improve result upto 8% as compared to existing approach in terms of accuracy.
Keywords: SVM, HMM, Heart Rate Variability
Scope of the Article: Healthcare Informatics