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In this work, Refined Multiscale Entropy (RMSE) was applied to characterize risk of cardiac death in ischemic cardiomyopathy patients, analyzing heart rate variability (HRV) by means of RR series during daytime and nighttime. RMSE approach measures an entropy rate in different time scales of a series, giving a multiscale characterization of complexity of that series. RMSE showed statistically significant...
The human cardiovascular system exhibits complicated dynamics. Various signal analysis techniques have been utilized to study electrocardiogram (ECG) rate and morphology for their potentials in diagnosis and prediction of cardiovascular diseases. Conventional methods of linear signal theory, including power spectral analysis and time-domain analysis, have been widely adopted to analyze heart rhythm...
Heart rate turbulence (HRT) is commonly assessed by two parameters: turbulence onset (TO) and turbulence slope (TS), both obtained by averaging RR tachograms following a ventricular premature beat (VPB). It has been recently shown that a model-based detection-theoretical approach results in HRT indices outperforming TO/TS in identifying the presence or absence of HRT. The aim of this work is to evaluate...
Despite tremendous development in wireless sensor networks, the abilities are by far yet fully leveraged in the case of online analysis of medical sensor data. We investigate how a data stream management system can be used to query and analyze streaming data from different medical sensors in real-time. By using the open source data stream management system Esper, we have implemented queries, based...
Although atrial fibrillation is the most common sustained cardiac rhythm disturbance, it remains underdiagnosed. One of the most drastic complications is embolism, and strokes in particular. Patients having atrial fibrillation must be identified in order to reduce the number of strokes. The algorithm presented detects atrial fibrillation, even without it being indicated in the analyzed ECG. Based...
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