A Low-Complexity Model-Free Approach for Real-Time Cardiac Anomaly Detection Based on Singular Spectrum Analysis and Nonparametric Control Charts

Abstract

While the importance of continuous monitoring of electrocardiographic (ECG) or 1 photoplethysmographic (PPG) signals to detect cardiac anomalies is generally accepted in preventative medicine, 2 there remain major barriers to its actual widespread adoption. Most notably, current approaches tend to lack 3 real-time capability, exhibit high computational cost, and be based on restrictive modeling assumptions or 4 require large amounts of training data. We propose a lightweight and model-free approach for the online 5 detection of cardiac anomalies such as ectopic beats in ECG or PPG signals based on the change detection 6 capabilities of Singular Spectrum Analysis (SSA) and nonparametric rank-based cumulative sum (CUSUM) 7 control charts. The procedure is able to quickly detect anomalies without requiring the identification of fiducial 8 points such as R-peaks and is computationally significantly less demanding than previously proposed SSA-based 9 approaches. Therefore, the proposed procedure is equally well suited for standalone use and as an add-on to 10 complement existing (e.g. heart rate (HR) estimation) procedures.