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Computer-aided obstructive sleep apnea screening from single-lead electrocardiogram using statistical and spectral features and bootstrap aggregating

Hassan A. R., Haque M. A.

Biocybernetics and Biomedical Engineering

2016

Vol. 36, no. 1

256--266

Automatic sleep apnea screening is important to alleviate the onus of the physicians of analyzing a large volume of data visually. Again, the push towards low-power, portable and wearable sleep quality monitoring systems necessitates the use of minimum number of recording channels to enhance battery life. So, there is a dire need of an automated apnea detection scheme based on single-lead electrocardiogram (ECG). Most of the existing works are based on multiple channels of physiological signals or yield poor performance. The effect of various classification models on algorithmic performance is also poorly explored. In the present work, we propose a statistical and spectral feature based sleep apnea identification scheme that utilizes single-lead ECG signals. Bootstrap aggregating is employed to perform classification. The efficacy of the selected features is demonstrated by intuitive, statistical and graphical analyses. Optimal choices of classifier parameters are also expounded. The performance of the proposed algorithm is evaluated for various classifiers. The performance of our method is also compared to that of the state-of-the-art ones. The proposed method yields accuracy, sensitivity and specificity of 85.97%, 84.14% and 86.83% respectively on a widely used benchmark data-set. Experimental findings backed by statistical and graphical analyses suggest that the proposed method performs better than the existing ones in terms of accuracy, sensitivity, specificity and computational cost.

Noisy image segmentation using a self-organizing map network

Gorjizadeh S, Pasban S, Alipour S

Advances in Science and Technology. Research Journal

2015

Vol. 9, nr 26

118–-123

Image segmentation is an essential step in image processing. Many image segmentation methods are available but most of these methods are not suitable for noisy images or they require priori knowledge, such as knowledge on the type of noise. In order to overcome these obstacles, a new image segmentation algorithm is proposed by using a self-organizing map (SOM) with some changes in its structure and training data. In this paper, we choose a pixel with its spatial neighbors and two statistical features, mean and median, computed based on a block of pixels as training data for each pixel. This approach helps SOM network recognize a model of noise, and consequently, segment noisy image as well by using spatial information and two statistical features. Moreover, a two cycle thresholding process is used at the end of learning phase to combine or remove extra segments. This way helps the proposed network to recognize the correct number of clusters/segments automatically. A performance evaluation of the proposed algorithm is carried out on different kinds of image, including medical data imagery and natural scene. The experimental results show that the proposed algoise in comparison with the well-known unsupervised algothms.