Symmetrical Compression Distance for Arrhythmia Discrimination in Cloud-Based Big-Data Services

Abstract

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.

Application of Entropy-Based Features to Predict Defibrillation Outcome in Cardiac Arrest

“…The classifier was evaluated following a leave one patient out cross-validation (LOPCV) scheme [51,52], where shocks within each patient were classified using the optimal SVM obtained for the rest of the shocks. The balanced error rate (BER) was used to optimize the SVMs because it equally weights the SE and SP and is therefore insensitive to class imbalance.…”

Big Data for Health

“…Automatic classifications are necessary since the dataset sizes are beyond the capability of manual interpretation within a reasonable time period. New compression-based measures are, therefore, proposed as highquality cloud computing services to reduce the computation time for the automated classification of different types of cardiac arrhythmia [92]. In many situations, measurements must be interpreted together with the context under which the data is collected.…”

LSTM-Based ECG Classification for Continuous Monitoring on Personal Wearable Devices

“…In addition, it allows data to stay on the wearable device and hence avoids privacy issues of cloudassisted processing. Our approach is different from offline processing of stored ECG signals, or remote processing on powerful cloud servers [4], [5]. Authors Continuous monitoring on wearable devices require the automated ECG classification algorithm to be both accurate and light-weight at the same time.…”