Kun Qian1, Christoph Janott2, Vedhas Pandit3, Zixing Zhang3, Clemens Heiser4, Winfried Hohenhorst5, Michael Herzog6, Werner Hemmert2, Bjorn Schuller7. 1. Machine Intelligence and Signal Processing Group, MMK, Technische Universität München, Munich, Germany. 2. Institute for Medical EngineeringTechnische Universität München. 3. Chair of Complex and Intelligent SystemsUniversity of Passau. 4. Department of Otorhinolaryngology/Head and Neck SurgeryTechnische Universität München. 5. Clinic for ENT Medicine, Head and Neck SurgeryAlfried Krupp Krankenhaus. 6. Clinic for ENT Medicine, Head and Neck SurgeryCarl-Thiem-Klinikum Cottbus. 7. Department of ComputingImperial College London.
Abstract
OBJECTIVE: Obstructive sleep apnea (OSA) is a serious chronic disease and a risk factor for cardiovascular diseases. Snoring is a typical symptom of OSA patients. Knowledge of the origin of obstruction and vibration within the upper airways is essential for a targeted surgical approach. Aim of this paper is to systematically compare different acoustic features, and classifiers for their performance in the classification of the excitation location of snore sounds. METHODS: Snore sounds from 40 male patients have been recorded during drug-induced sleep endoscopy, and categorized by Ear, Nose & Throat (ENT) experts. Crest Factor, fundamental frequency, spectral frequency features, subband energy ratio, mel-scale frequency cepstral coefficients, empirical mode decomposition-based features, and wavelet energy features have been extracted and fed into several classifiers. Using the ReliefF algorithm, features have been ranked and the selected feature subsets have been tested with the same classifiers. RESULTS: A fusion of all features after a ReliefF feature selection step in combination with a random forests classifier showed the best classification results of 78% unweighted average recall by subject independent validation. CONCLUSION: Multifeature analysis is a promising means to help identify the anatomical mechanisms of snore sound generation in individual subjects. SIGNIFICANCE: This paper describes a novel approach for the machine-based multifeature classification of the excitation location of snore sounds in the upper airway.
OBJECTIVE: Obstructive sleep apnea (OSA) is a serious chronic disease and a risk factor for cardiovascular diseases. Snoring is a typical symptom of OSA patients. Knowledge of the origin of obstruction and vibration within the upper airways is essential for a targeted surgical approach. Aim of this paper is to systematically compare different acoustic features, and classifiers for their performance in the classification of the excitation location of snore sounds. METHODS: Snore sounds from 40 male patients have been recorded during drug-induced sleep endoscopy, and categorized by Ear, Nose & Throat (ENT) experts. Crest Factor, fundamental frequency, spectral frequency features, subband energy ratio, mel-scale frequency cepstral coefficients, empirical mode decomposition-based features, and wavelet energy features have been extracted and fed into several classifiers. Using the ReliefF algorithm, features have been ranked and the selected feature subsets have been tested with the same classifiers. RESULTS: A fusion of all features after a ReliefF feature selection step in combination with a random forests classifier showed the best classification results of 78% unweighted average recall by subject independent validation. CONCLUSION: Multifeature analysis is a promising means to help identify the anatomical mechanisms of snore sound generation in individual subjects. SIGNIFICANCE: This paper describes a novel approach for the machine-based multifeature classification of the excitation location of snore sounds in the upper airway.
Authors: C Janott; M Schmitt; C Heiser; W Hohenhorst; M Herzog; M Carrasco Llatas; W Hemmert; B Schuller Journal: HNO Date: 2019-09 Impact factor: 1.284