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Technologies 2018, 6(1), 26; https://doi.org/10.3390/technologies6010026

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

Graduate School of Excellence Computational Engineering, Technische Universität Darmstadt, Dolivostraße 15, 64293 Darmstadt, Germany
Received: 11 December 2017 / Revised: 8 February 2018 / Accepted: 13 February 2018 / Published: 15 February 2018
(This article belongs to the Special Issue Physiological Monitoring Technologies)
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Abstract

While the importance of continuous monitoring of electrocardiographic (ECG) or photoplethysmographic (PPG) signals to detect cardiac anomalies is generally accepted in preventative medicine, there remain numerous challenges to its widespread adoption. Most notably, difficulties arise regarding crucial characteristics such as real-time capability, computational complexity, the amount of required training data, and the avoidance of too-restrictive modeling assumptions. We propose a lightweight and model-free approach for the online detection of cardiac anomalies such as ectopic beats in ECG or PPG signals on the basis of the change detection capabilities of singular spectrum analysis (SSA) and nonparametric rank-based cumulative sum (CUSUM) control charts. The procedure is able to quickly detect anomalies without requiring the identification of fiducial points such as R-peaks, and it is computationally significantly less demanding than previously proposed SSA-based approaches. Therefore, the proposed procedure is equally well suited for standalone use and as an add-on to complement existing (e.g., heart rate (HR) estimation) procedures. View Full-Text
Keywords: nonparametric change point detection; singular spectrum analysis; cumulative sums; ECG; PPG; arrhythmias; cardiac monitoring nonparametric change point detection; singular spectrum analysis; cumulative sums; ECG; PPG; arrhythmias; cardiac monitoring
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Lang, M. A Low-Complexity Model-Free Approach for Real-Time Cardiac Anomaly Detection Based on Singular Spectrum Analysis and Nonparametric Control Charts. Technologies 2018, 6, 26.

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