Analysis of voice features related to obstructive sleep apnoea and their application in diagnosis support ?
16 voice features proposed for the detection of obstructive sleep apnoea. Voice features analyzed in isolation and combined with LDA and multiple linear regression. The set of 8-best features with LDA reaches a classification error rate of 17.1%. F1-measure of 81% with 8 speech features and a few minutes of the patient's voice. The speech features are related to apnoea not only through BMI and age.
osa obstructive sleep apnoea psg polysomnography ahi apnoeahypopnoea index bmi body mass index cer classification error rate eer equal error rate lda linear discriminant analysis mlr multiple linear regression