An automatic and efficient method of snore events detection from sleep audio recordings

Automatic Detection of Whole Night Snoring Events Using Non-Contact Microphone

OBJECTIVE Although awareness of sleep disorders is increasing, limited information is available on whole night detection of snoring. Our study aimed to develop and validate a robust, high performance, and sensitive whole-night snore detector based on non-contact technology. DESIGN Sounds during polysomnography (PSG) were recorded using a directional condenser microphone placed 1 m above the b...

Automatic detection of post-apnoeic snore events from home and clinical full night sleep recordings

Automatic Sleep Stages Detection Based on EEG Signals Using Combination of Classifiers

Sleep stages classification is one of the most important methods for diagnosis in psychiatry and neurology. In this paper, a combination of three kinds of classifiers are proposed which classify the EEG signal into five sleep stages including Awake, N-REM (non-rapid eye movement) stage 1, N-REM stage 2, N-REM stage 3 and 4 (also called Slow Wave Sleep), and REM. Twenty-five all night recordings...

Efficient snoring and breathing detection based on sub-band spectral statistics.

Snoring, a common symptom in the general population may indicate the presence of obstructive sleep apnea (OSA). In order to detect snoring events in sleep sound recordings, a novel method was proposed in this paper. The proposed method operates by analyzing the acoustic characteristics of the snoring sounds. Based on these acoustic properties, the feature vectors are obtained using the mean and...

An efficient method for snore/nonsnore classification of sleep sounds.

A new method to detect snoring episodes in sleep sound recordings is proposed. Sleep sound segments (i.e., 'sound episodes' or simply 'episodes') are classified as snores and nonsnores according to their subband energy distributions. The similarity of inter- and intra-individual spectral energy distributions motivated the representation of the feature vectors in a lower dimensional space. Episo...