Search

In this paper, it is presented that electromyography (EMG) is a shot noise based on the generation of EMG. A novel filter is proposed by applying a neural network (NN) ensemble where the noisy input signal and the desired one are the same in a learning process. Both incremental and batch mode are applied in the learning process of NNs that is better than generalized NN filters. This NN ensemble filter not only reduces additive and multiplicative white noise inside signals, but also preserves the signals' characteristics. In clinical EEG and EMG signals processing, the filter is capable of reducing EMG in the clinical EEG, and it is proved that there is randomness in EMG.

Obstructive sleep apnea (OSA) is a serious disorder characterized by intermittent events of upper airway collapse during sleep. Snoring is the most common nocturnal symptom of OSA. Almost all OSA patients snore, but not all snorers have the disease. Recently, researchers have attempted to develop automated snore analysis technology for the purpose of OSA diagnosis. These technologies commonly require, as the first step, the automated identification of snore/breathing episodes (SBE) in sleep sound recordings. Snore intensity may occupy a wide dynamic range (>95 dB) spanning from the barely audible to loud sounds. Low-intensity SBE sounds are sometimes seen buried within the background noise floor, even in high-fidelity sound recordings made within a sleep laboratory. The complexity of SBE sounds makes it a challenging task to develop automated snore segmentation algorithms, especially in the presence of background noise. In this paper, we propose a fundamentally novel approach based on artificial neural network (ANN) technology to detect SBEs. Working on clinical data, we show that the proposed method can detect SBE at a sensitivity and specificity exceeding 0.892 and 0.874 respectively, even when the signal is completely buried in background noise (SNR