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https://dspace.iiti.ac.in/handle/123456789/5684
| Title: | Automated Detection of Heart Valve Disorders from the PCG Signal Using Time-Frequency Magnitude and Phase Features |
| Authors: | Pachori, Ram Bilas |
| Keywords: | Decision trees;Dimensional stability;Phonocardiography;Wavelet transforms;Aortic stenosis;Automated detection;Heart valves;Mitral regurgitation;Phonocardiograms;Random forest classifier;Sensor signals;Synchrosqueezing;Biomedical signal processing |
| Issue Date: | 2019 |
| Publisher: | Institute of Electrical and Electronics Engineers Inc. |
| Citation: | Ghosh, S. K., Tripathy, R. K., Ponnalagu, R. N., & Pachori, R. B. (2019). Automated detection of heart valve disorders from the PCG signal using time-frequency magnitude and phase features. IEEE Sensors Letters, 3(12) doi:10.1109/LSENS.2019.2949170 |
| Abstract: | In this letter, we propose a method for the automated detection of heart valve disorders namely, the aortic stenosis (AS), mitral stenosis (MS), and mitral regurgitation (MR) from the phonocardiogram (PCG) signal. The wavelet synchrosqueezing transform is used to obtain the time-frequency matrix from the segmented cycles of the PCG signal. From the time-frequency matrix, the magnitude and phase features are extracted. The random forest (RF) classifier is used for the classification. The results reveal that the proposed method has the average individual accuracy (IA) values of 98.83%, 97.66%, 91.16%, and 92.83% for normal, AS, MS, and MR classes. © 2017 IEEE. |
| URI: | https://doi.org/10.1109/LSENS.2019.2949170 https://dspace.iiti.ac.in/handle/123456789/5684 |
| ISSN: | 2475-1472 |
| Type of Material: | Journal Article |
| Appears in Collections: | Department of Electrical Engineering |
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