Calibrating different sounds for sound therapy: A general guide.

INTRODUCTION: Sound therapy is one of the complementary or alternative interventions for various populations. The intensity of the sounds for sound therapy needs to be properly calibrated to ensure their accuracy and effectiveness. This paper aims to provide a general guideline for calibrating sound files using free software, specifically Audacity®. MATERIALS AND METHODS: Six sounds (broadband noise, rain, ocean, waterfall, Quranic chapters Al-Fatihah, and Yasin recitations) were calibrated at the intensity levels of 45, 50, 55, 60, 65, 70, 75, and 80dBA. The sounds were delivered through a pair of Sennheiser HD 280 Pro headphones connected to the Sound Blaster X-Fi Surround 5.1 Pro sound card. The long-term average of the sound pressure level over the time of recording (LAseq) was recorded using the 3M SoundPro Class 1 1/3 Octave RTA sound level meter (SLM). The desired intensity levels were obtained by making adjustments to the sound files via the Audacity® software. RESULTS: All sound files were calibrated at the targeted levels as verified by the value of LAseq. CONCLUSIONS: Calibration of audio files can be done using a free/open-source software, as all six sound files were successfully calibrated at the targeted levels of 45, 50, 55, 60, 65, 70, 75, and 80dBA. The calibration steps provided in this paper can be easily applied by other researchers for similar purposes, with precautions when calibrating at low levels.

Age-related rarefaction in retinal vasculature is not linear.

The fractal dimension is a global measure of complexity and is useful for quantifying anatomical structures, including the retinal vascular network. A previous study found a linear declining trend with aging on the retinal vascular fractal dimension (DF); however, it was limited to the older population (49 years and older). This study aimed to investigate the possible models of the fractal dimension changes from young to old subjects (10-73 years). A total of 215 right-eye retinal samples, including those of 119 (55%) women and 96 (45%) men, were selected. The retinal vessels were segmented using computer-assisted software, and non-vessel fragments were deleted. The fractal dimension was measured based on the log-log plot of the number of grids versus the size. The retinal vascular DF was analyzed to determine changes with increasing age. Finally, the data were fitted to three polynomial models. All three models are statistically significant (Linear: R2 = 0.1270, 213 d.f., p < 0.001, Quadratic: R2 = 0.1536, 212 d.f., p < 0.001, Cubic: R2 = 0.1529, 211 d.f., p < 0.001). The quadratic regression is significantly better than the linear regression (p < 0.001); however, the increase in R2 from the quadratic model to the cubic model is not significant (p = 0.97). These results suggest that the decreasing trend of the fractal dimension associated with aging is better explained by the quadratic model than by the linear and cubic models in a sample with a broader age spectrum.

Age-related rarefaction in retinal vasculature is not linear.

The fractal dimension is a global measure of complexity and is useful for quantifying anatomical structures, including the retinal vascular network. A previous study found a linear declining trend with aging on the retinal vascular fractal dimension (DF); however, it was limited to the older population (49 years and older). This study aimed to investigate the possible models of the fractal dimension changes from young to old subjects (10­73 years). A total of 215 right-eye retinal samples, including those of 119 (55%) women and 96 (45%) men, were selected. The retinal vessels were segmented using computer-assisted software, and non-vessel fragments were deleted. The fractal dimension was measured based on the log­log plot of the number of grids versus the size. The retinal vascular DF was analyzed to determine changes with increasing age. Finally, the data were fitted to three polynomial models. All three models are statistically significant (Linear: R(2) = 0.1270, 213 d.f., p < 0.001, Quadratic: R(2) = 0.1536, 212 d.f., p < 0.001, Cubic: R(2) = 0.1529, 211 d.f., p < 0.001). The quadratic regression is significantly better than the linear regression (p < 0.001); however, the increase in R(2) from the quadratic model to the cubic model is not significant (p = 0.97). These results suggest that the decreasing trend of the fractal dimension associated with aging is better explained by the quadratic model than by the linear and cubic models in a sample with a broader age spectrum.

Fusion of multiscale wavelet-based fractal analysis on retina image for stroke prediction.

In this paper, we present a novel method of analyzing retinal vasculature using Fourier Fractal Dimension to extract the complexity of the retinal vasculature enhanced at different wavelet scales. Logistic regression was used as a fusion method to model the classifier for 5-year stroke prediction. The efficacy of this technique has been tested using standard pattern recognition performance evaluation, Receivers Operating Characteristics (ROC) analysis and medical prediction statistics, odds ratio. Stroke prediction model was developed using the proposed system.