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 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.

Automatic Near Real-Time Outlier Detection and Correction in Cardiac Interbeat Interval Series for Heart Rate Variability Analysis: Singular Spectrum Analysis-Based Approach

“…The objective of this study was to introduce a general framework of low computational complexity that allows for an automatic near real-time detection and correction of outliers in the R-R series based on the singular spectrum analysis (SSA). The main novel contributions of this work include (1) the application of a recently proposed model-free lightweight SSA change-point detection (l-SSA-CPD) algorithm [32]; (2) a modification of l-SSA-CPD through the use of an adaptive control limit sequential ranks (AC-SRC) control chart [33] to drastically reduce detection delays; and (3) upon detection of an anomaly, the substitution of the corrupted tachogram segment by an approximation obtained through recurrent SSA forecasting based on a small outlier-free tachogram segment.…”

“…Recently, this author proposed a low-complexity model-free approach based on SSA and nonparametric cumulative sum (CUSUM) control charts for real-time cardiac anomaly detection, referred to as l-SSA-CPD [32]. It was shown that l-SSA-CPD reliably detects anomalies even when directly applied to unprocessed (ie, no preprocessing was performed) raw ECG and photoplethysmographic records from common databases publicly available through Physiobank [34].…”

Control Limits for an Adaptive Self-Starting Distribution-Free CUSUM Based on Sequential Ranks

“…Hence, in addition to the approach followed in [18], h SRC for a fixed k SRC can be obtained through a straightforward Monte Carlo procedure (see, e.g., [24] at 12) without requiring any historical training data.…”

Application of Sliding Nest Window Control Chart in Data Stream Anomaly Detection

“…Several combination models have been proposed such as the combination of the sliding window and control chart for Internet data stream [26] anomaly detection, the combination of sliding window, and partial differential equation sorting (PDEs) algorithm to detect an abnormal data stream [27], data stream anomaly detection combining the sliding window, and integrated learning [28]. There have been several control chart methods for data stream anomaly detection such as non-parametric accumulation and control charts electrocardiographic (ECG) anomaly detection [29], weighted calculation cumulative sum (CUSUM) study [30], CUSUM-based satellite power supply system anomaly detection [31], and an online consumption forecast based on CUSUM [32]. The contributions of this paper include adopting the sliding window nested way to increase trend prediction accuracy, increasing the out-of-bounds detection rate, weakening the influence of the current point, and using the CUSUM algorithm to combine the above two points and then test the real-time data stream.…”