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1. Inter-Beat Interval Estimation with Tiramisu Model: A Novel Approach with Reduced Error

   https://doi.org/10.1145/3616020  

   Arefeen, Asiful; Akbari, Ali; Mirzadeh, Seyed Iman; Jafari, Roozbeh; Shirazi, Behrooz A; Ghasemzadeh, Hassan (January 2024, ACM Transactions on Computing for Healthcare)

   Inter-beat interval (IBI) measurement enables estimation of heart-tare variability (HRV) which, in turn, can provide early indication of potential cardiovascular diseases (CVDs). However, extracting IBIs from noisy signals is challenging since the morphology of the signal gets distorted in the presence of noise. Electrocardiogram (ECG) of a person in heavy motion is highly corrupted with noise, known as motion-artifact, and IBI extracted from it is inaccurate. As a part of remote health monitoring and wearable system development, denoising ECG signals and estimating IBIs correctly from them have become an emerging topic among signal-processing researchers. Apart from conventional methods, deep-learning techniques have been successfully used in signal denoising recently, and diagnosis process has become easier, leading to accuracy levels that were previously unachievable. We propose a deep-learning approach leveraging tiramisu autoencoder model to suppress motion-artifact noise and make the R-peaks of the ECG signal prominent even in the presence of high-intensity motion. After denoising, IBIs are estimated more accurately expediting diagnosis tasks. Results illustrate that our method enables IBI estimation from noisy ECG signals with SNR up to -30 dB with average root mean square error (RMSE) of 13 milliseconds for estimated IBIs. At this noise level, our error percentage remains below 8% and outperforms other state-of-the-art techniques. 

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2. A Comparison of Denoising Methods in Onset Determination in Medial Gastrocnemius Muscle Activations during Stance

   https://doi.org/10.3390/sci2030053  

   Zhang, Jian; Soangra, Rahul; E. Lockhart, Thurmon (September 2020, Sci)

   One of the most basic pieces of information gained from dynamic electromyography is accurately defining muscle action and phase timing within the gait cycle. The human gait relies on selective timing and the intensity of appropriate muscle activations for stability, loading, and progression over the supporting foot during stance, and further to advance the limb in the swing phase. A common clinical practice is utilizing a low-pass filter to denoise integrated electromyogram (EMG) signals and to determine onset and cessation events using a predefined threshold. However, the accuracy of the defining period of significant muscle activations via EMG varies with the temporal shift involved in filtering the signals; thus, the low-pass filtering method with a fixed order and cut-off frequency will introduce a time delay depending on the frequency of the signal. In order to precisely identify muscle activation and to determine the onset and cessation times of the muscles, we have explored here onset and cessation epochs with denoised EMG signals using different filter banks: the wavelet method, empirical mode decomposition (EMD) method, and ensemble empirical mode decomposition (EEMD) method. In this study, gastrocnemius muscle onset and cessation were determined in sixteen participants within two different age groups and under two different walking conditions. Low-pass filtering of integrated EMG (iEMG) signals resulted in premature onset (28% stance duration) in younger and delayed onset (38% stance duration) in older participants, showing the time-delay problem involved in this filtering method. Comparatively, the wavelet denoising approach detected onset for normal walking events most precisely, whereas the EEMD method showed the smallest onset deviation. In addition, EEMD denoised signals could further detect pre-activation onsets during a fast walking condition. A comprehensive comparison is discussed on denoising EMG signals using EMD, EEMD, and wavelet denoising in order to accurately define an onset of muscle under different walking conditions. 

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3. Adaptive RF Fingerprint Decomposition in Micro UAV Detection based on Machine Learning

   https://doi.org/10.1109/ICASSP39728.2021.9414985  

   Xu, Chengtao; He, Fengyu; Chen, Bowen; Jiang, Yushan; Song, Houbing (June 2021, ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP))

   null (Ed.)

   Radio frequency (RF) signal classification has significantly been used for detecting and identifying the features of unknown unmanned aerial vehicles (UAVs). This paper proposes a method using empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) on extracting the communication channel characteristics of intruding UAVs. The decomposed intrinsic mode functions (IMFs) except noise components are selected for RF signal pattern recognition based on machine learning (ML). The classification results show that the denoising effects introduced by EMD and EEMD could both fit in improving the detection accuracy with different features of RF communication channel, especially on identifying time-varying RF signal sources. 

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4. High Impulse Noise Intensity Removal in Natural Images Using Convolutional Neural Network

   https://doi.org/10.1109/CCWC47524.2020.9031200  

   Mafi, Mehdi; Izquierdo, Walter; Adjouadi, Malek (January 2020, IEEE 10th Annual Computing and Communication Workshop and Conference (CCWC))

   This paper introduces a new image smoothing filter based on a feed-forward convolutional neural network (CNN) in presence of impulse noise. This smoothing filter integrates a very deep architecture, a regularization method, and a batch normalization process. This fully integrated approach yields an effectively denoised and smoothed image yielding a high similarity measure with the original noise free image. Specific structural metrics are used to assess the denoising process and how effective was the removal of the impulse noise. This CNN model can also deal with other noise levels not seen during the training phase. The proposed CNN model is constructed through a 20-layer network using 400 images from the Berkeley Segmentation Dataset (BSD) in the training phase. Results are obtained using the standard testing set of 8 natural images not seen in the training phase. The merits of this proposed method are weighed in terms of high similarity measure and structural metrics that conform to the original image and compare favorably to the different results obtained using state-of-art denoising filters. 

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5. Periodic Signal Denoising: An Analysis-Synthesis Framework Based on Ramanujan Filter Banks and Dictionaries

   https://doi.org/10.1109/ICASSP39728.2021.9413689  

   Kulkarni, Pranav; Vaidyanathan, P. P. (June 2021, Proc. IEEE Int. Conf. Acoust. Speech, and Signal Proc)

   null (Ed.)

   Ramanujan filter banks (RFB) have in the past been used to identify periodicities in data. These are analysis filter banks with no synthesis counterpart for perfect reconstruction of the original signal, so they have not been useful for denoising periodic signals. This paper proposes to use a hybrid analysissynthesis framework for denoising discrete-time periodic signals. The synthesis occurs via a pruned dictionary designed based on the output energies of the RFB analysis filters. A unique property of the framework is that the denoised output signal is guaranteed to be periodic unlike any of the other methods. For a large range of input noise levels, the proposed approach achieves a stable and high SNR gain outperforming many traditional denoising techniques. 

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