Adaptive Denoising of Respiratory Sounds with a Hybrid Discrete Wavelet Transform and 1D CNN Framework /
Naila Ashraf
- 63 Soft Copy 30cm
Accurate analysis of respiratory sounds holds key importance in precise diagnosis of pulmonary diseases. These sounds are sometimes noisy which require an effective diagnosing method for noise reduction and correct analysis of lung sounds. Traditional denoising methods are limited to spectral overlap with background noise. This study attempts for denoising biomedical signals utilizing discrete wavelet convolutional neural network (DW-CNN), an adaptive filter consists of multiresolution ability of discrete wavelet transform (DWT) and deep feature learning ability of neural networks, which preserve the signal details. Encoder-decoder structure of DW-CNN with inverse DWT accurately reconstructs the signal, outperforming traditional wavelet denoising. LS signals under real noise conditions were denoised using DW-CNN, where DWT replaced traditional pooling layers. Results were quantitatively compared with baseline traditional methods using Signal to Noise Ratio (SNR) and Root Mean Square Error (RMSE) metrics. On average there is an improvement of 9.61 dB in SNR and a reduction of 0.35489 in RMSE. Final results highlight the better performance of implemented model over conventional methods, increasing the scope of combined model in pattern recognition, clinical diagnostics and wearable devices.