A Novel Approach to Resource Allocation Mechanism in Artificial Immune Recognition System: Fuzzy Resource Allocation Mechanism and Application to Diagnosis of Atherosclerosis Disease. In: Bersini H, Carneiro J, editors. Artificial Immune Systems. Berlin: Springer Berlin Heidelberg; 2006. pp. 244-55. [DOI: 10.1007/11823940_19]

Comparison of short-time Fourier transform and Eigenvector MUSIC methods using discrete wavelet transform for diagnosis of atherosclerosis

In this paper, a more effective use of Doppler techniques is presented for the purpose of diagnosing atherosclerosis in its early stages using the carotid artery Doppler signals. The power spectral density (PSD) graphics are obtained by applying the short-time Fourier transform (STFT)-Welch and the Eigenvector MUSIC methods to the discrete wavelet transform (DWT) of Doppler signals. The PSDs for the fourth approximation component (A4) of both methods estimated that the patients with atherosclerosis in its early phase had lower maximum frequency components. On the other hand, the healthy subjects had higher maximum frequency components. The area under the curve (AUC), which belongs to the receiver operating characteristic (ROC) curve for the frequency level of the maximum PSDs of the A4 approximation obtained from the STFT modeling, is computed as 0.97. The AUC for the MUSIC modeling is computed as 0.996. The AUC belonging to the ROC curve for the higher maximum frequency component is computed as 0.87. The AUC belonging to the ROC curve for the test parameter of the frequency level of the maximum PSDs derived from the MUSIC modeling is determined to be 0.882. The results of this study clearly demonstrate that it is possible to distinguish between the healthy people and the patients with atherosclerosis by using the frequency level of the maximum PSDs for the A4 approximation. Furthermore, it is concluded that the power of Eigenvector-MUSIC method in terms of the resolution of the high frequencies is better than that of the STFT methods.

A new approach to diagnosing of importance degree of obstructive sleep apnea syndrome: Pairwise AIRS and Fuzzy-AIRS classifiers

Artificial Immune Recognition System (AIRS) classifier algorithm is robust and effective in medical dataset classification applications such as breast cancer, heart disease, diabetes diagnosis etc. In our previous work, we have proposed a new resource allocation mechanism called fuzzy resource allocation in AIRS algorithm both to improve the classification accuracy and to decrease the computation time in classification process. Here, AIRS and Fuzzy-AIRS classifier algorithms and one against all approach have been combined to increase the classification accuracy of obstructive sleep apnea syndrome (OSAS) that is an important disease that influences both the right and the left cardiac ventricle. The OSAS dataset consists of four classes including of normal (25 subjects), mild OSAS (AHI (Apnea and Hypoapnea Index) = 5-15 and 14 subjects), moderate OSAS (AHI < 15-30 and 18 subjects), and serious OSAS (AHI > 30 and 26 subjects). In the extracting of features that is characterized the OSAS disease, the clinical features obtained from Polysomnography used diagnostic tool for obstructive sleep apnea in patients clinically suspected of suffering from this disease have been used. The used clinical features are Arousals Index (ARI), Apnea and Hypoapnea Index (AHI), SaO2 minimum value in stage of REM, and Percent Sleep Time (PST) in stage of SaO2 intervals bigger than 89%. Even though AIRS and Fuzzy-AIRS classifiers have been used in the classifying multi-class problems, theirs classification performances are low in the case of multi-class classification problems. Therefore, we have used two classes in AIRS and Fuzzy-AIRS classifiers by means of one against all approach instead of four classes comprising the healthy subjects, mild OSAS, moderate OSAS, and serious OSAS. We have applied the AIRS, Fuzzy-AIRS, AIRS with one against all approach (Pairwise AIRS), and Fuzzy-AIRS with one against all approach (Pairwise Fuzzy-AIRS) to OSAS dataset. The obtained classification accuracies are 63.41%, 63.41%, 87.19%, and 84.14% using the above methods for 200 resources, respectively. These results show that the best method for diagnosis of OSAS is the combination of AIRS and one against all approach (Pairwise AIRS).