Self-Awareness of Sleep Apnea Symptoms Among Middle-Aged and Elderly People in Taiwan

In recent years, the proportion of middle-aged and elderly people in Taiwan has gradually increased and has already surpassed that of advanced countries such as Europe, the United States and Japan, therefore, the health of middle-aged and elderly people is a topic that needs attention. This is because physical deterioration or illness can lead to a decline in quality of life and create a medical burden on the individual and society. This study investigated the common symptoms of sleep apnea in middle-aged and elderly people (over 40 years old), and developed a self-test subjective perception model, using “daytime sleepiness” and “sleep quality” as influencing factors, and “attention” as mediating variables to verify the effect with sleep apnea symptoms. An online electronic questionnaire was conducted and distributed through social media and groups of friends in Taiwan. A total of 541 valid questionnaires were collected and analyzed in three stages: Descriptive Analysis, Measurement Model Validation, and Structural Equation Model. The research processes of the study showed that the sample fitted the normal distribution and the measurement model conformed with convergent reliability and discriminant validity. The research results were found that “sleep quality” had a significant negative effect on sleep apnea symptoms. “Daytime sleepiness” had a positive effect on sleep apnea symptoms. “Daytime sleepiness” had a negative effect on sleep apnea symptoms through the “attention” mediator. Finally, through the questionnaire, we hope to make the middle-aged people aware of themselves, so that they can seek early medical treatment if there are signs and symptoms of sleep apnea symptoms

Wheeze Detection using Modified k-Means Clustering Algorithm

本論文主要是使用彩色聲譜圖來呈現哮鳴的時頻特徵，並利用k群聚演算法來偵測哮鳴。k群聚演算法主要執行的是分群的工作，其中，群聚組數必須人為先做設定，經過測試後，k值選擇設定成三，三可以在彩色聲譜圖中，對應到紅、綠、藍三個顏色，也可以在聲譜圖上顯示哮鳴。但是k群聚演算法中群組編號的分派是隨機的，因此對應到哮鳴病徵的顏色會產生不固定的現象，本研究利用色彩索引法來修正k群聚演算法，將三個群組編號依照在聲譜圖中所佔的面積比例製作成索引，再將哮鳴索引出來，並標示成紅色。 除此之外，此方法也應用至正常的呼吸音，雜訊消除的效應也一起被探討。結果顯示，經過修正k群聚演算法，不論是正常或哮鳴，訊號-雜訊比都約可提升2dB，同時，色彩索引的製作可以使得哮鳴在彩色聲譜圖上能夠具有穩定的再現性，才能夠提供給醫生，並且能對醫生有幫助。The aim of this study is to present wheeze time-frequency characteristics in color scales spectrogram, and k-means clustering algorithm is applied to detect wheezes. k-means clustering algorithms are grouped according to its spectrogram nature. The first step is to preset the k value, representing the grouping number. After experiment testing, the k value is set to three. This number corresponds to the color scale spectrogram are red, green, and blue. Wheeze sounds can also be displayed on the spectrogram. However, k-means clustering algorithms group number is assigned randomly. Therefore, the color corresponding to wheezing symptoms has no fixed color. Through the color-indexing method, the wheezing color is set to be red in accordance with the color index production proportions. In addition, this method is also applied to the normal respiratory sounds, and the effects of noise reduction are discussed. After using modified k-means clustering algorithm, the results show that the signal-to-noise ratios are improved for about 2dB for wheeze and normal cases. The color index can mark wheezing sounds on the color spectrogram in red, and this has a stable representation and reproducibility. This helps the doctor very much in wheeze detection

Transmission Perspective on the Mechanism of Coarse and Fine Crackle Sounds

The possibility of a normal distribution indicates that few particles are in the same phase during a breath and their reflections can be observed on the chest wall, then a few explosive waves with relatively large power occurr occasionally. Therefore, the one-cycle sine wave which is simulated as a single burst of the explosive effect phenomenon penetrates through the chest wall and was analysed to explore the reason of the crackle sounds. The results explain the differences between the definitions of crackle proposed by Sovijärvi et al. (2000a). The crackles in the lungs were synthesised by a computer simulation. When the coarse crackles occur, the results indicate that higher burst frequency carriers (greater than 100 Hz) directly penetrate the bandpass filter to simulate the chest wall. The simulated coarse crackle sounds were low pitched, with a high amplitude and long duration. The total duration was greater than 10 ms. However, for a lower frequency carrier (approximately 50 Hz), the fundamental frequency component was filtered out. Therefore, the second harmonic component of the lower frequency carrier, i.e., the fine crackle, penetrated the chest wall. Consequently, it is very possible that the normal lung sounds may contain many crackle-shaped waves with very small amplitudes because of the filtering effects of the chest wall, environment noises, electric devices, stethoscopes, and human ears, the small crackles disappear in the auscultations. In addition, our study pointed out that some unknown crackles of the very low frequency under the bandwidth of the human ears cannot penetrate the airways and be detected by medical doctors. Therefore, it might be necessary to focus advanced electronic instrumentation on them in order to analyse their possible characteristics for diagnosis and treatment of the respiration system

Comparison of Moving Average and Differential Operation for Wheeze Detection in Spectrograms

A moving average (MA) is a commonly used noise reduction method in signal processing. Several studies on wheeze auscultation have used MA analysis for preprocessing. The present study compared the performance of MA analysis with that of differential operation (DO) by observing the produced spectrograms. These signal preprocessing methods are not only applicable to wheeze signals but also to signals produced by systems such as machines, cars, and flows. Accordingly, this comparison is relevant in various fields. The results revealed that DO increased the signal power intensity of episodes in the spectrograms by more than 10 dB in terms of the signal-to-noise ratio (SNR). A mathematical analysis of relevant equations demonstrated that DO could identify high-frequency episodes in an input signal. Compared with a two-dimensional Laplacian operation, the DO method is easier to implement and could be used in other studies on acoustic signal processing. DO achieved high performance not only in denoising but also in enhancing wheeze signal features. The spectrograms revealed episodes at the fourth or even fifth harmonics; thus, DO can identify high-frequency episodes. In conclusion, MA reduces noise and DO enhances episodes in the high-frequency range; combining these methods enables efficient signal preprocessing for spectrograms