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%.

As an alternative to complex cryptographic techniques, PLS is emerging as a promising paradigm to protect wireless communication systems from eavesdropping attacks by exploiting the physical characteristics of the wireless channels. Among various PLS approaches, the one based on cooperative communication is favorable and has received a lot of interest from the research community. Although PLS schemes with half-duplex relays have been extensively researched, the issue of PLS in cooperative systems with full-duplex (FD) relays is far from being comprehensively understood. In this article, we first present the state of the art on PLS approaches proposed for FD cooperative systems. We then provide a case study in which a source-based jamming scheme is proposed to enhance the secrecy performance of a cooperative system with an untrusted FD relay. Finally, we outline several interesting yet challenging future research problems in this topic.

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 letter, we evaluate the secrecy outage probability (SOP) of the ORS scheme with an amplify-and-forward system over independent and non-identically 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.

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.

Sudden cardiac arrests are mainly caused by ventricular fibrillation and ventricular tachycardia, which are known as shockable (SH) rhythms and properly curable by electronic defibrillators. In this paper, we propose a novel algorithm to decide whether an electrocardiogram (ECG) signal is SH or nonshockable (NSH). The algorithm selects 20 features from both the preprocessed ECG and its NSH signal using a modified variational mode decomposition technique, and uses the support vector machine for the binary SH/NSH classification. The 20 features are identified as an efficient set of the most informative, among 54 candidate features, by comparing the balanced error rate of each combination, based on two layers of feature selection. This feature set is validated with the evaluation data in the public database using a fivefolds cross validation procedure. The proposed algorithm results in accuracy of 99.00%, sensitivity of 97.36%, and specificity of 99.16%.