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We analyzed the possibility of detecting and predicting ventricular fibrillation (VF), a medical emergency that may put people's lives at risk, as the medical prognosis depends on the time in which medical personnel intervene. Therefore, besides immediate detection of VF, the possibility of predicting VF 40 or even 50 min in advance was analyzed. For testing the proposed algorithm, we used ECG signals taken from the MIT-BIH database, namely, Malignant Ventricular Ectopy Database, Sudden Cardiac Death Holter Database and Normal Sinus Rhythm Database. The presented method is based on features extracted from the ECG signals in the time domain, frequency domain and complexity measures. For VF detection, the possibility of identifying the VF episode in the first 3 s after its occurrence was tested. For this, the first 3 s immediately after the appearance of VF were cut out and the features were computed on these sections. For VF prediction, 3 min of the ECG signal clipped 40 or 50 min before VF onset was used. Then, on these pieces of ECG signal, the specific features were calculated for 1 s segments. For the normal signal situation, 3 min was randomly selected from the database with normal ECGs. For the classification or detection stage, both an MLP-type neural network and the classifiers from the Machine Learning toolbox of the MATLAB® environment were used. The results obtained for both detection and classification are over 94% in both cases. The novelty of our results compared to those previously obtained is the time interval with which the possibility of prediction was analyzed, namely, 50 min in advance of the VF installation date. This means that the patient will be informed that it is possible to suffer a VF and has time to take the necessary measures to overcome a possible medical emergency. [ABSTRACT FROM AUTHOR]

This paper presents a new algorithm for electrocardiogram (ECG) signal compression based on local extreme extraction, adaptive hysteretic filtering and Lempel-Ziv-Welch (LZW) coding. The algorithm has been verified using eight of the most frequent normal and pathological types of cardiac beats and an multi-layer perceptron (MLP) neural network trained with original cardiac patterns and tested with reconstructed ones. Aspects regarding the possibility of using the principal component analysis (PCA) to cardiac pattern classification have been investigated as well. A new compression measure called 'quality score,' which takes into account both the reconstruction errors and the compression ratio, is proposed. Index Terms--Biomedical signal processing, data compression, neural networks (NNs), signal processing.

The Problem of linear inductance- and capacitance-time (L/T, C/T) conversions is approached through the systematic study of four possibilities to build astable multivibrators using piecewise linear resistances obtained from the one operational amplifier (OA) negative impedance converter (NIC) configuration. A new L/T converter with grounded inductance is found. Formulas for the time period taking into consideration the losses as well as the OA saturation output resistance are derived.

Performance and behavior verification of electronic components and systems in order to fit applications requirements are indispensable nowadays. There is a particular interest in finding the application yield caused by the process variation of the electronic components and revealing the component's factors which cause failure in the application. In order to address these two problems, the Failure Probability (FP) estimation of an electronic system is computed, and the impact of component's factors is analyzed, by considering factors variations as well as various operational conditions. Since FP estimation can be computationally expensive, it is necessary to find more efficient methods. In this paper, we propose a method that can both estimate FP and quantify the impact of factors based on behavioral system modeling and system's performances metamodeling. FP is calculated by re-sampling the metamodel, hence, converting a small numbers problem into a large numbers problem. A comparison between this method with other existing methods such as counting and distribution fitting is made for an E-Bike case. The results show that the approach based on metamodeling can lead to comparable results with state-of-the-art for FP estimation methods with the advantage of revealing the factors which cause the application failure. [ABSTRACT FROM AUTHOR]