The current development of cloud computing is completely changing the paradigm of data knowledge extraction in huge databases. An example of this technology in the cardiac arrhythmia field is the SCOOP platform, a national-level scientific cloud-based big data service for implantable cardioverter defibrillators. In this scenario, we here propose a new methodology for automatic classification of intracardiac electrograms (EGMs) in a cloud computing system, designed for minimal signal preprocessing. A new compression-based similarity measure (CSM) is created for low computational burden, so-called weighted fast compression distance, which provides better performance when compared with other CSMs in the literature. Using simple machine learning techniques, a set of 6848 EGMs extracted from SCOOP platform were classified into seven cardiac arrhythmia classes and one noise class, reaching near to 90% accuracy when previous patient arrhythmia information was available and 63% otherwise, hence overcoming in all cases the classification provided by the majority class. Results show that this methodology can be used as a high-quality service of cloud computing, providing support to physicians for improving the knowledge on patient diagnosis.

Heart rate (HR) shows oscillations with different periods as a result of the sympatho-vagal balance. The most studied ones are short-period variations and the circadian pattern. However, the existence of rhythms of longer periods has not been systematically studied.The aim of this work is to study HR long-period rhythms in a multicentric database including 336 patients in sinus rhythm, with implanted cardioverter defibrillator. We used two methodological approaches: First a rhythmometric procedure to automatically select the statistically significant rhythms present in the signal; Second, the LASSO path approach to analyze the order of activation of the rhythms, representing the importance of each rhythm.Most of the population showed a significant annual rhythm (78% day/80% night). Weekly and quarterly rhythms were also present (weekly 26% /26% ; quarterly 22%/21%). Monthly rhythm was rarely present. Most present rhythm combinations were annual plus weekly (21%/21%) and annual plus quarterly (19%/18%). The order of activation given by the LASSO path was in agreement with the multicomponent rhythmometric model in the 86%/85% of the cases.The unusual long monitoring period, and the high number of patients, represent an ideal scenario to robustly assess the existence of long-term rhythms.

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