Ventricular fibrillation and ventricular tachycardia (VF/VT), known as shockable (SH) rhythms, are the mainly cause of sudden cardiac arrests (SCA), which is cured efficiently by the automated external defibrillator (AED). The performance of the shock advice algorithm (SAA) applied in the AED has been improved by using machine learning technique and variously conventional features, recently. In this paper, we propose a novel algorithm with relatively high performance for the SCA detection on electrocardiogram (ECG) signal. The algorithm consists of a convolutional neural network as a feature extractor (CNNE) and a Boosting (BS) classifier. A grid search with nested 5-folds cross validation (CV) is used to select the CNNE trained with preprocessed ECG, SH, and NSH signals using the modified variational mode decomposition technique. The deep feature vector learned by this CNNE is extracted at the first fully connected layer and then fed into BS classifier to validate its performance using 5-folds CV procedure. The secondary learning of the BS classifier and the use of three input channels for the CNNE improve certainly the detection performance of the proposed SAA with the validated accuracy of 99.26%, sensitivity of 97.07%, and specificity of 99.44%.

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For cooperative systems, in which an eavesdropper overhears source information through relays' transmission, an optimal relay selection (ORS) scheme has recently been proposed to improve the system secrecy performance. In this paper, we evaluate the secrecy outage probability (SOP) of the ORS scheme with an amplify-and-forward system over independent and nonidentically distributed Rayleigh fading. More specifically, we derive a tight approximation on the SOP. Thereafter, by analyzing asymptotic behaviour of the derived approximation, novel insights are revealed. The derivations are confirmed through Monte Carlo simulations.Index Terms-Cooperative communications, relay selection, physical layer security, secrecy outage probability.1089-7798 (c)

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In this paper, we consider an overlay cognitive radio network, in which a secondary transmitter (ST) is willing to relay the information of a primary transmitter toward a primary receiver. In return, ST can access the licensed band to send its own information superimposed with the primary signal to a secondary receiver. The power-limited ST uses a power splitting protocol to harvest energy from its received signal to increase its transmit power. We analyze the performance of the primary and the secondary systems under independent Nakagami-m fading by deriving their corresponding outage probabilities in integralbased expressions. In addition, by considering the high signalto-noise ratio, we obtain very tight closed-form approximations of the outage probabilities. Thereafter, by further analyzing the approximations, we reveal novel insights on the diversity orders and coding gains of the two systems. Our analytical results are validated through extensive Monte-Carlo simulations.Index Terms-Cognitive radio networks, RF-based energy harvesting, decode-and-forward, outage probability, diversity order, coding gain, Nakagami-m fading.