Time characteristics of prolonged partial obstruction periods using an Emfit mattress

Perez-Macias, Jose Maria; Viik, Jari; Värri, Alpo; Himanen, Sari-Leena; Tenhunen, Mirja

doi:10.1007/978-981-10-5122-7_194

Cited by 1 publication

(3 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The samples consist of three events: snore, apnea, and silence. Perez-Macias et al (2018) however used 30 subjects who has undergone polysomnography for one full night to captured 30 samples of snoring events [18].…”

Section: Snore Data Created Through Subjectsmentioning

confidence: 99%

“…Aside polysomnography which has high financial cost implication [12], other several approaches [3,7], [13][14][15][16][17][18][19][20][21][22] has been proposed in literatures to detect, diagnose and remedy early snoring stage before getting to an unhealthy phase. Lately, methods in the field of signal processing and AI seems to be the promising area to detect snoring and apnea event.…”

Section: Introductionmentioning

confidence: 99%

“…Lately, methods in the field of signal processing and AI seems to be the promising area to detect snoring and apnea event. These approaches majorly include: support vector machine (SVM) [10,16], recurrent neural network (RNN) [3,22], convolutional neural network (CNN) [17], electromechanical film transducer (Emfit) signal [18], pulse transition time (PTT) [19], acoustic signal processing with KNN [20], deep neural network [13], and hybrid methods [12,20,22]. Several of these methods made use of publicly available snoring data or personally aggregated snoring dataset.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Brief on Snoring Data and Classification Methods

Adesuyi¹

2020

IJATCSE

View full text Add to dashboard Cite

Recently, research on snoring sound had gained interest especially in the area of classification in Obstructive Sleep Apnea (OSA) and distinction from non-snoring sounds. Classifiers such as Support Vector Machine (SVM), Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) had been used to meet this interest. These approaches relies on several underlying techniques such as Mel Frequency Cepstral Coefficients (MFCC), Short-Time Fourier transform (STFT) and several others to extract features from snore/apnea Spectrogram images before classification process. However, achieving desirable classification accuracy depends on choice of classifier, feature extraction techniques and, available dataset. In the paper, we presented a brief survey on existing methods and snore data acquisition processes to quickly expose and ease new researches in this domain to make appropriate choice from available methods.

show abstract

Section: Snore Data Created Through Subjectsmentioning

confidence: 99%