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The confidence of medical equipment is intimately related to false alarms. The higher the number of false events occurs, the less truthful is the equipment. In this sense, reducing (or suppressing) false positive alarms is hugely desirable. In this work, we propose a feasible and real-time approach that works as a validation method for a heartbeat segmentation third-party algorithm. The approach is based on convolutional neural networks (CNNs), which may be embedded in dedicated hardware. Our proposal aims to detect the pattern of a single heartbeat and classifies them into two classes: a heartbeat and not a heartbeat. For this, a seven-layer convolution network is employed for both data representation and classification. We evaluate our approach in two well-settled databases in the literature on the raw heartbeat signal. The first database is a conventional on-the-person database called MIT-BIH, and the second is one less uncontrolled off-the-person type database known as CYBHi. To evaluate the feasibility and the performance of the proposed approach, we use as a baseline the Pam-Tompkins algorithm, which is a well-known method in the literature and still used in the industry. We compare the baseline against the proposed approach: a CNN model validating the heartbeats detected by a third-party algorithm. In this work, the third-party algorithm is the same as the baseline for comparison purposes. The results support the feasibility of our approach showing that our method can enhance the positive prediction of the Pan-Tompkins algorithm from $$97.84\%$$ 97.84 % /$$90.28\%$$ 90.28 % to $$100.00\%$$ 100.00 % /$$96.77\%$$ 96.77 % by slightly decreasing the sensitivity from $$95.79\%$$ 95.79 % /$$96.95\%$$ 96.95 % to $$92.98\%/$$ 92.98 % / $$95.71\%$$ 95.71 % on the MIT-BIH/CYBHi databases.

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A hybrid population-based metaheuristic, Hybrid Canonical Dierential Evolutionary Particle Swarm Optimization (hC-DEEPSO), is applied to solve Security Constrained Optimal Power Flow (SCOPF) problems. Despite the inherent diculties of tackling these real-world problems, they must be solved several times a day taking into account operation and security conditions. A combination of the C-DEEPSO metaheuristic coupled with a multipoint search operator is proposed to better exploit the search space in the vicinity of the best solution found so far by the current population in the rst stages of the search process. A simple diversity mechanism is also applied to avoid premature convergence and to escape from local optima. A experimental design is devised to ne-tune the parameters of the proposed algorithm for each instance of the SCOPF problem. The eectiveness of the proposed hC-DEEPSO is tested on the IEEE 57-bus, IEEE 118-bus and IEEE 300-bus standard systems. The numerical results obtained by the proposed hC-DEEPSO are compared with other evolutionary methods reported in this literature to prove the potential and capability of the proposed hC-DEEPSO for solving the SCOPF at acceptable economical and technical levels.