Key result
A review of over 50 heart rate variability features combined with a support vector machine classifier showed comparable prediction performance for sudden cardiac death to other studies.
Why the study?
ICD decision algorithms still have low specificity leading to inappropriate shocks and reduced quality of life, requiring improved detection of abnormal rhythms.
A review and comparative study of heart rate variability features demonstrates that combining these features with a support vector machine classifier yields prediction performance comparable to other studies for detecting abnormal cardiac rhythms in ICDs.
ICDs remain foundational for SCD prevention in high-risk patients; review leaves open algorithm refinements for contemporary populations.
Over the last four decades, implantable cardioverter defibrillators (ICDs) have been widely deployed to reduce sudden cardiac death (SCD) risk in patients with a history of life-threatening arrhythmia. By continuous monitoring of the heart rate, ICDs can use decision algorithms to distinguish normal cardiac sinus rhythm or supra-ventricular tachycardia from abnormal cardiac rhythms like ventricular tachycardia and ventricular fibrillation and deliver appropriate therapy such as an electrical stimulus. Despite the success of ICDs, more research is still needed, particularly in decision-making algorithms. Because of low specificity in practical devices, patients with ICDs still receive inappropriate shocks, which may lead to inadvertent mortality and reduction of quality of life. At the same time, higher sensitivity can lead to the use of newer tiered therapies. The purpose of this study is to review the literature on common signal features used in detection algorithms for abnormal cardiac sinus rhythm, as well as reviewing datasets used for algorithm development in previous studies. More than 50 different features to address heart rate changes before SCD have been reviewed and general methodology on this area proposed based on variety of studies on ICDs functionality. A comparative study on the prediction performance of these features, using a common database, is also presented. By combining these features with a support vector machine classifier, achieved results have compared well with other studies.
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Parsi et al. (2019) conducted a review in Sudden cardiac death risk in patients with implantable cardioverter defibrillators. Heart rate variability features and support vector machine classifier was evaluated on Prediction performance of signal features for abnormal cardiac sinus rhythm. A review of over 50 heart rate variability features combined with a support vector machine classifier showed comparable prediction performance for sudden cardiac death to other studies.
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