Safety threshold of R-wave amplitudes in patients with implantable cardioverter defibrillator.

OBJECTIVE: A safety threshold for baseline rhythm R-wave amplitudes during follow-up of implantable cardioverter defibrillators (ICD) has not been established. We aimed to analyse the amplitude distribution and undersensing rate during spontaneous episodes of ventricular fibrillation (VF), and define a safety amplitude threshold for baseline R-waves. METHODS: Data were obtained from an observational multicentre registry conducted at 48 centres in Spain. Baseline R-wave amplitudes and VF events were prospectively registered by remote monitoring. Signal processing algorithms were used to compare amplitudes of baseline R-waves with VF R-waves. All undersensed R-waves after the blanking period (120 ms) were manually marked. RESULTS: We studied 2507 patients from August 2011 to September 2014, which yielded 229 VF episodes (cycle length 189.6±29.1 ms) from 83 patients that were suitable for R-wave comparisons (follow-up 2.7±2.6 years). The majority (77.6%) of VF R-waves (n=13953) showed lower amplitudes than the reference baseline R-wave. The decrease in VF amplitude was progressively attenuated among subgroups of baseline R-wave amplitude (≥17; ≥12 to <17; ≥7 to <12; ≥2.2 to <7 mV) from the highest to the lowest: median deviations -51.2% to +22.4%, respectively (p=0.027). There were no significant differences in undersensing rates of VF R-waves among subgroups. Both the normalised histogram distribution and the undersensing risk function obtained from the ≥2.2 to <7 mV subgroup enabled the prediction that baseline R-wave amplitudes ≤2.5 mV (interquartile range: 2.3-2.8 mV) may lead to ≥25% of undersensed VF R-waves. CONCLUSIONS: Baseline R-wave amplitudes ≤2.5 mV during follow-up of patients with ICDs may lead to high risk of delayed detection of VF. TRIAL REGISTRATION NUMBER: NCT01561144; results.

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.