This paper presents a digital signal processing tool developed using Matlab TM , which provides a very low­cost and effective strategy for analog­to­digital conversion of legated paper biomedical maps without requiring dedicated hardware. This software­based approach is particularly helpful for digitalizing biomedical signals acquired from analogical devices equipped with a plottingter. Albeit ...

The biomedical field offers many learning tasks that share unique challenges: large amounts of unpaired data, and a high cost to generate labels. In this work, we develop a method to address these issues with semi-supervised learning in regression tasks (e.g., translation from source to target). Our model uses adversarial signals to learn from unpaired datapoints, and imposes a cycle-loss recon...

Denoising of the ECG signals is required, as they are prone to noises during their acquisition. Currently, denoising techniques for ECG signals are mostly available in the wavelet transform domain. In this paper, an approach for denoising the ECG signals in the Framelet domain is proposed. Initially, signals are decomposed using the Framelet transform. After decomposition, they are denoised usi...

Biomedical Signal and Image Processing (BSIP) constitutes a major field of interest for both educational aspects and in research environment in Biomedical Engineering (BME). In fact, the physiological knowledge improvement in a wide variety of innovative research, as well as the implementation in many clinical procedures, make use extensively of these concepts in more or less sophisticated medi...

Automatic analysis of biomedical time series such as electroencephalogram (EEG) and electrocardiographic (ECG) signals has attracted great interest in the community of biomedical engineering due to its important applications in medicine. In this work, a simple yet effective bag-of-words representation that is able to capture both local and global structure similarity information is proposed for...

Analysis of non stationary signals like audio, speech and biomedical signals require good resolution both in time and frequency as their spectral components are not fixed. There are many applications of time-frequency analysis in non stationary signals like source separation, signal denoising etc. This paper presents an application of time frequency analysis using STFT, Short Time Fourier Trans...

Analysis of non stationary signals like audio, speech and biomedical signals require good resolution both in time and frequency as their spectral components are not fixed. There are many applications of time-frequency analysis in non stationary signals like source separation, signal denoising etc. This paper presents an application of time frequency analysis using STFT, Short Time Fourier Trans...