CT Image Reconstruction Approaches Applied to Time-Frequency Representation of Signals

Pucik, Jozef; Oweis, Rami J.

doi:10.1155/s1110865703211100

Cited by 2 publications

(1 citation statement)

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“…In contrast, modern computer technology offers great advantages in terms of acquisition, storage, and analysis of biosignals and provides the possibility to automate the process. [4][5][6][7][8][9][10] Computerized lung sound analysis involves recording the patient lung sound via an electronic device, followed by computer analysis and classification of lung sounds based on specific signal characteristics. [2,11] Gurung et al conducted a review and an in-depth analysis of relevant classification studies using a computerized lung sound analysis in which the overall sensitivity and specificity of implemented algorithms were estimated.…”

Section: What This Study Adds To the Fieldmentioning

confidence: 99%

An alternative respiratory sounds classification system utilizing artificial neural networks

et al. 2015

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R espiratory sound (RS) is regarded one of the most significant biosignals used to diagnose certain respiratory abnormalities. It is established that breath sounds are created in the larger airways as a result of vibrations that are generated due to air velocity and turbulence. RSs are known to be highly non-stationary and non-linear due to variations in the airflow rate and airflow volumes during the respiratory cycle. The non-linearity of the RS stems mainly from the complex turbulent flow dynamics and its structural interaction with the larger airway walls.RSs detected from the chest wall and mouth may be classified as normal and adventitious sounds. Owing to the presence of adventitious (abnormal) sounds, such signals convey valuable information pertaining to the underlying malfunctioning of the pulmonary system. On the other hand, normal sounds include those generated by healthy Original Article An Alternative Respiratory Sounds Classification System Utilizing Artificial Neural NetworksRami J. Oweis, Enas W. Abdulhay, Amer Khayal, Areen AwadBackground: Computerized lung sound analysis involves recording lung sound via an electronic device, followed by computer analysis and classification based on specific signal characteristics as non-linearity and nonstationarity caused by air turbulence. An automatic analysis is necessary to avoid dependence on expert skills. Methods:This work revolves around exploiting autocorrelation in the feature extraction stage. All process stages were implemented in MATLAB. The classification process was performed comparatively using both artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS) toolboxes. The methods have been applied to 10 different respiratory sounds for classification. Results:The ANN was superior to the ANFIS system and returned superior performance parameters. Its accuracy, specificity, and sensitivity were 98.6%, 100%, and 97.8%, respectively. The obtained parameters showed superiority to many recent approaches. Conclusions: The promising proposed method is an efficient fast tool for the intended purpose as manifested in the performance parameters, specifically, accuracy, specificity, and sensitivity. Furthermore, it may be added that utilizing the autocorrelation function in the feature extraction in such applications results in enhanced performance and avoids undesired computation complexities compared to other techniques. (Biomed J 2015;38:153-161)

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