Cardiac signal processing is usually a computationally demanding task as signals are heavily contaminated by noise and other artifacts. In this paper, an effective approach for peak point detection and localization in noisy electrocardiogram (ECG) signals is presented. Six stages characterize the implemented method, which adopts the Hilbert transform and a thresholding technique for the detection of zones inside the ECG signal which could contain a peak. Subsequently, the identified zones are analyzed using the wavelet transform for R point detection and localization. The conceived signal processing technique has been evaluated, adopting ECG signals belonging to MIT-BIH Noise Stress Test Database, which includes specially selected Holter recordings characterized by baseline wander, muscle artifacts and electrode motion artifacts as noise sources. The experimental results show that the proposed method reaches most satisfactory performance, even when challenging ECG signals are adopted. The results obtained are presented, discussed and compared with some other R wave detection algorithms indicated in literature, which adopt the same database as a test bench. In particular, for a signal to noise ratio (SNR) equal to 6 dB, results with minimal interference from noise and artifacts have been obtained, since Se e +P achieve values of 98.13% and 96.91, respectively.
Modern cities are moving towards novel approaches for urban sustainability for improving citizenship's life quality, thus aiming at the Smart City model. Environmental and mobility issues represent two key areas where policy makers address their interventions and, amongst them, noise pollution is one of the most significant causes of public concern. However, noise monitoring campaigns are expensive and require skilled personnel. A viable alternative is represented by Mobile Crowd Sensing (MCS) paradigm, which exploits mobile devices as sensing platforms. In this paper, we propose a MCSbased platform that exploits noise measurements collected by citizens and offers a suggestion system to city managers about noise abatement measures (in terms of both estimated noise reduction and average installation costs). Several field tests demonstrated the feasibility of this approach as a suitable way to support city managers and to widen the possibilities of collaborative urban noise monitoring.
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