Yohichiro Kojima

Automatic classification of oral/nasal snoring sounds based on the acoustic properties

Snoring was once regarded as an indication of good sleep. But recently it has been known to be one of the symptoms which indicate sleep disordered breathing such as sleep apnea syndrome. Moreover, heavy snoring caused by oral breathing sometimes leads benign snorers to be apneics. Thus, it is important to detect oral snoring for medical treatment in the earlier stage, but we cannot know our own snoring. This paper describes a method to detect oral snoring by extracting the acoustic properties of snoring sounds and using the k-Nearest Neighbor classifier. As a result, over 92% of snoring sounds are successfully classified under the various cross validation evaluations.

Optical imaging of arteriovenous fistula for clinical application: Clarification of transillumination image of blood vessel by image-subtraction

3. Experiment and Discussion � 試作したコイルの特性を評価するため,イン ピーダンス計測実験を行った.また,比較のた め,キャパシタを挿入しない非分割コイルも2 種類試作した.1つは分割コイルと直径,コイ ル長さ,巻き数が同じで,もう一つは分割コイ ルと自己共振周波数が同程度となる,巻き数10 の非分割コイルである. リアクタンスの計測結果をFig. 4,結果のまとめ をTable 1に示す.巻き数12の非分割コイルの自 己共振周波数が156 MHzに対し,分割コイルで は179 MHzと,約15%と高い値となり,自己共 振周波数を向上することができた.また,巻き 数10の非分割コイルは自己共振周波数が185 MHzと,分割コイルと同程度の自己共振周波数 となった. 次に各コイルがMRI用マイクロコイルとして 使用可能かどうかを判断するためMRI信号受信 回路に実装し,SWR (Standing Wave Ratio) 計測 実験を行った.分割コイルのSWRは約1.2,巻き 数12の非分割コイルは約3.1となり,巻き数12の 非分割コイルはMRI用マイクロコイルとして使 用困難であるが,分割コイルは同じ巻き数であ りながら使用可能である.これは,キャパシタ 分割により自己共振周波数が向上したためであ る.なお,巻き数10の非分割コイルもSWRが約

NEURAL CLASSIFICATION OF SNORING SOUNDS FOR THE DETECTION OF ORAL BREATHING DURING SNORING

Healthy people are generally breathing through nose during sleep, but oral breathing will be given rise to if the nasal cavity is gradually being closed. Nasal closure or even nasal congestion leads to open mouth during snoring, which causes the tongue base collapse, the origin of OSAS. Thus, if a simple home device with only a microphone can automatically monitor our snores at bedside and detect oral breathing during snoring, we can perceive an abnormality in our sleep condition easily and early detection and treatment of OSAS will be possible. In our previous work, we proposed some feature extraction methods for the stationary subsequences extracted from oral, nasal, and oronasal snoring sounds and analyzed their acoustic properties in detail. This paper addresses a snoring sound classification based on breathing route during snoring using Multilayer Perceptron, Support Vector Machines, and k-Nearest Neighbor method. According to our experiments, the SVM with Gaussian kernel acquires the best performance where 82.5% of the oral, 89.2% of the nasal, and 73.6 % of the oronasal snoring sounds are successfully classified.