Symmetrical compression distance for arrhythmia discrimination in cloud-based big-data services.

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.

Diagnostic stability and evolution of bipolar disorder in clinical practice: a prospective cohort study.

OBJECTIVE: To evaluate the long-term stability of International Classification of Diseases-10th revision bipolar affective disorder (BD) in multiple settings. METHOD: A total of 34 368 patients received psychiatric care in the catchment area of a Spanish hospital (1992-2004). The analyzed sample included patients aged > or =18 years who were assessed on > or =10 occasions and received a diagnosis of BD at least once (n = 1153; 71,543 assessments). Prospective and retrospective consistencies and the proportion of subjects who received a BD diagnosis in > or =75% of assessments were calculated. Factors related to diagnostic shift were analyzed with traditional statistical methods and Markov's models. RESULTS: Thirty per cent of patients received a BD diagnosis in the first assessment and 38% in the last assessment. Prospective and retrospective consistencies were 49% and 38%. Twenty-three per cent of patients received a BD diagnosis during > or =75% of the assessments. CONCLUSION: There was a high prevalence of misdiagnosis and diagnostic shift from other psychiatric disorders to BD. Temporal consistency was lower than in other studies.