Ensemble Heterogeneous Feature Selection (EHFS) and Heterogeneous Ensemble Classifier with VOTE (HECV) for Automatic Detection of Snore Sounds
E. Jeslin Renjith1, A. Christy2

1E. Jeslin Renjith, Research Scholar, Bharathiar University, Coimbatore (Tamil Nadu), India.
2Dr. A. Christy, Research Supervisor, Bharathiar University, Coimbatore (Tamil Nadu), India.
Manuscript received on 10 October 2019 | Revised Manuscript received on 19 October 2019 | Manuscript Published on 02 November 2019 | PP: 255-264 | Volume-8 Issue-2S11 September 2019 | Retrieval Number: B10420982S1119/2019©BEIESP | DOI: 10.35940/ijrte.B1042.0982S1119
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© 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: Obstructive Sleep Apnea (OSA) is generally considered as a sleep co-related breathing difficulty with some important well known disabling indication. This research work tends to differentiate the condition of OSA victims. The significant work is to propose an innovative Heterogeneous Ensemble Classifier with Velum Oropharyngeal Tongue Epiglottis (HECV) method with a high level of effectiveness. In the formulated study, data recording, features extraction, multi-feature selection, classification and performance evaluation are the five stages of processing. At the initial stage, the noises represented in the audio signals which were eliminated through Adaptive Fuzzy Median Filter (AFMF) algorithm. After thatCrest Factor, original Frequency, Spectral Frequency Features, Subband Energy Ratio, Mel-Scale Frequency Cepstral Coefficients (MFCC), Empirical Mode Decomposition (EMD) Features, and Wavelet Energy Features are collected from the noise suppressed audio signals and inputs are fed into Ensemble Heterogeneous Feature Selection (EHFS) technique. EHFS algorithm fuses the outputs of filter and wrapper oriented feature selection methods. These identified features are classified using HECV technique which renders a good classification outputs by validation without the regard of subjects. The outputs show that the formulated HECV approach gives better performance in snore detection when co-related with other classifiers.
Keywords: Obstructive Sleep Apnea (OSA), Heterogeneous Ensemble Classifier with Velum Oropharyngeal Tongue Epiglottis (HECV), Velum Oropharyngeal Tongue Epiglottis (VOTE), Multi-feature selection algorithm, and Ensemble Heterogeneous Feature Selection (EHFS).
Scope of the Article: Heterogeneous and Streaming Data