In diagnosis of diseases Ultrasonic devices are frequently used by healthcare professionals. The medical imaging devices namely X-ray, CT/MRI and ultrasound are producing abundant images which are used by medical practitioners in the process of diagnosis . The main problem faced by them is the noise introduced due to the consequence of the coherent nature of the wave transmitted. These noises corrupt the image and often lead to incorrect diagnosis. In general, ECG signals affected by noises such as baseline wandering, power line interference, electromagnetic interference and high frequency noises during data acquisition. In the recent paper we have considered the Discrete Wavelet Transform (DWT) based wavelet Denoising have incorporated using different Thresholding techniques to remove major sources of noises from the acquired ECG signals. The experimental results shows the significant reduction of White Gaussian noise and it retains the ECG signal morphology effectively. Different performance measures were considered to select the appropriate wavelet function and Thresholding rule for efficient noise removal methods such as Mean Square Error (MSE),Peak Signal to Noise Ratio (PSNR) and Percentage Root Mean Square Difference (PRD) . The experimental result shows the db wavelet and BayesShrink Thresholding rule is optimal for reducing noise in the real time ECG signals.
Katika utambuzi wa magonjwa Vifaa vya ultrasonic hutumiwa mara nyingi na wataalamu wa huduma za afya. Vifaa vya upimaji wa picha za matibabu, yaani X-ray, CT, MRI na ultrasound, vinazalisha picha nyingi ambazo hutumiwa na wataalamu wa matibabu katika mchakato wa utambuzi. Tatizo kuu wanakabiliwa na ni kelele kuletwa kutokana na matokeo ya asili coherent ya wimbi kupitishwa. Sauti hizo huharibu picha na mara nyingi husababisha utambuzi usio sahihi. Kwa ujumla, ishara za ECG huathiriwa na kelele kama vile kutembea kwa msingi, kuingiliana kwa mstari wa umeme, kuingiliana kwa umeme na kelele za masafa ya juu wakati wa upatikanaji wa data. Katika makala hii, tulizungumzia jinsi ya kutumia Discrete Wavelet Transform (DWT) na mbinu nyingine za kuondoa kelele kutoka kwa ishara za ECG. Matokeo ya majaribio yanaonyesha kupunguzwa kwa kiasi kikubwa kwa kelele nyeupe ya Gaussian na inahifadhi morphology ya ishara ya ECG kwa ufanisi. Kwa mfano, kuna njia za kuondoa kelele za sauti kama vile Average Square Error (MSE), Peak Signal to Noise Ratio (PSNR), na Percentage Root Mean Square Difference (PRD). Matokeo ya majaribio yanaonyesha db wavelet na BayesShrink Thresholding sheria ni bora kwa kupunguza kelele katika wakati halisi ECG ishara.