Learn More
Transmission of biomedical signals through communication channels is being used increasingly in clinical practice. This technique requires dealing with large volumes of information, and the electroencephalographic (EEG) signal is an example of this situation. In the EEG, various channels are recorded during several hours, resulting in a great demand of(More)
The recent use of long-term records in electroencephalography is becoming more frequent due to its diagnostic potential and the growth of novel signal processing methods that deal with these types of recordings. In these cases, the considerable volume of data to be managed makes compression necessary to reduce the bit rate for transmission and storage(More)
Due to the large volume of information generated in an electroencephalographic (EEG) study, compression is needed for storage, processing or transmission for analysis. In this paper we evaluate and compare two lossy compression techniques applied to EEG signals. It compares the performance of compression schemes with decomposition by filter banks or wavelet(More)
Coding distortion in lossy electroencephalographic (EEG) signal compression methods is evaluated through tractable objective criteria. The percentage root-mean-square difference, which is a global and relative indicator of the quality held by reconstructed waveforms, is the most widely used criterion. However, this parameter does not ensure compliance with(More)
A direct waveform mean-shape vector quantization (MSVQ) is proposed here as an alternative for electrocardiographic (ECG) signal compression. In this method, the mean values for short ECG signal segments are quantized as scalars and compression of the single-lead ECG by average beat substraction and residual differencing their waveshapes coded through a(More)
This paper describes an adaptive solution for threshold management of speaker verification systems. The reported algorithm estimates speaker-dependent thresholds based on successful verification sessions considering the minimization of a relation-based cost function. The optimum speaker verification threshold to authenticate a remotely located client is not(More)
This paper studies the effect of feature dimensions on an MFCC/IMFCC-GMM based text-independent speaker verification system (SVS). A typical baseline system is used to evaluate the impact of features based on the number of Mel, inverted Mel, delta and double delta coefficients while keeping other system parameters constant for all experiments. The relevance(More)
This paper presents a novel neural network-based approach to short-term, multi-step-ahead wind speed forecasting. The methodology combines predictions from a set of feed forward neural networks whose inputs comprehend a set of 11 explanatory variables related to past averages of wind speed, direction, temperature and time of the day, and their outputs(More)
  • 1