A large part of the biomedical research spectrum is dedicated to develop electrocardiogram (ECG) signal processing techniques to contribute to early diagnosis. However, it is common to find that ECG analysis methods reported are confined to off-line PC host operation. The authors present an arrhythmia classification method implemented on a Digital Signal Processing (DSP) platform intended for on-line, real-time ambulatory operation to classify eight heartbeat conditions: normal sinus rhythm (N), auricular fibrillation (AF), premature atrial contraction (PAC), left bundle branch block (LBBB), right bundle branch block (RBBB), premature ventricular contraction (PVC), sinoauricular heart block (SHB) and supraventricular tachycardia (SVT). The algorithm uses a wavelet transform process based on quadratic wavelets for identifying individual ECG waves and obtain a fiducial marker array. Classification is conducted by means of a Probabilistic Neural Network. The algorithm is tested with 17 ECG records obtained from the PhysioNet repository. The proposed classification procedure was tested initially on MATLAB and the results where compared with the equivalent analogue data fed to a DSP-based ECG data acquisition prototype through an arbitrary waveform generator. The results derived from confusion matrix tests yielded on-line classification accuracy of 92.69% (AF), 97.15% (N), 76.82% (PAC), 91.06% (LBBB), 87.5% (RBBB), 71.04% (PVC), 91.94% (SHB) and 95.45% (SVT), overall classification rate of 92.746% and 100% agreement between the MATLAB and on-line DSP implementations. The results suggest that the method and prototype presented may be suitable for being implemented on wearable sensing applications auxiliary for on-line, real-time diagnosis

The proliferation of cyber-physical systems is driving the growing need to establish links between these systems and virtual environments. Particularly in the smart grid, the high costs of some devices and the imminent need to prevent manipulation of these devices in production environments make mechanisms that allow the manipulation of these physical objects in virtual environments necessary; this has been called a digital twin (DT). Conversley, cyberattacks are growing in all cyber-physical systems and cybersecurity is essential to the critical infrastructure of the smart grid. This work shows a small implementation of a DT system for smart metering systems in a smart home environment for testing cybersecurity issues. The results show that the use of DTs is feasible in various contexts of the smart grid, particularly in cybersecurity testing.

Image segmentation applied to medical image analysis is still a critical and important task. Although there exist several segmentation algorithms that have been widely studied in literature, these are subject to segmentation problems such as over- and under-segmentation as well as non-closed edges. In this paper, a simple method that combines well-known segmentation algorithms is presented. This method is applied to detect acid-fast bacilli (AFB) in bacilloscopies used to diagnose pulmonary tuberculosis (TB). This diagnosis can be performed through different tests, and the most used worldwide is smear microscopy because of its low cost and effectiveness. This diagnosis technique is based on the analysis and counting of the bacilli in the bacilloscopy observed under an optical microscope. The proposed method is used to segment the bacilli in digital images from bacilloscopies processed using Ziehl-Neelsen (ZN) staining. The proposed method is fast, has a low computational cost and good efficiency compared to other methods. The bacilli image segmentation is performed by image processing and analysis techniques, probability concepts and classifiers. In this work, a Bayesian classifier based on a Gaussian mixture model (GMM) is used. The segmentations' results are validated by using the Jaccard index, which indicates the efficiency of the classifier.