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

Technologies ◽  
2018 ◽  
Vol 6 (1) ◽  
pp. 26 ◽  
Author(s):  
Michael Lang
Keyword(s):  
Anomaly Detection ◽  
Real Time ◽  
Spectrum Analysis ◽  
Control Charts ◽  
Singular Spectrum Analysis ◽  
Low Complexity ◽  
Cardiac Anomaly ◽  
Singular Spectrum ◽  
Model Free ◽  
Model Free Approach

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

2017 ◽  
Author(s):  
Michael Lang
Keyword(s):  
Real Time ◽  
Spectrum Analysis ◽  
Control Charts ◽  
Singular Spectrum Analysis ◽  
Computational Cost ◽  
Cardiac Anomaly ◽  
Cardiac Anomalies ◽  
Singular Spectrum ◽  
Model Free ◽  
Model Free Approach

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 major barriers to its actual widespread adoption. Most notably, current approaches tend to lack real-time capability, exhibit high computational cost, and be based on restrictive modeling assumptions or require large amounts of training data. We propose a lightweight and model-free approach for the online detection of cardiac anomalies such as ectopic beats in ECG or PPG signals based on 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 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.

Singular Spectrum Analysis With Conditional Predictions for Real‐Time State Estimation and Forecasting

2019 ◽  
Vol 46 (3) ◽  
pp. 1851-1860 ◽  
Author(s):  
H. Reed Ogrosky ◽  
Samuel N. Stechmann ◽  
Nan Chen ◽  
Andrew J. Majda
Keyword(s):  
State Estimation ◽  
Real Time ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Singular Spectrum

Multivariate singular spectrum analysis for forecasting revisions to real-time data

2011 ◽  
Vol 38 (10) ◽  
pp. 2183-2211 ◽  
Author(s):  
Kerry Patterson ◽  
Hossein Hassani ◽  
Saeed Heravi ◽  
Anatoly Zhigljavsky
Keyword(s):  
Real Time ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Time Data ◽  
Singular Spectrum ◽  
Real Time Data ◽  
Multivariate Singular Spectrum Analysis

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

2018 ◽  
Author(s):  
Michael Lang
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Real Time ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Detection Performance ◽  
Change Point Detection ◽  
Heart Rate Variability Analysis ◽  
Singular Spectrum ◽  
Ectopic Beats

BACKGROUND Heart rate variability (HRV) is derived from the series of R-R intervals extracted from an electrocardiographic (ECG) measurement. Ideally all components of the R-R series are the result of sinoatrial node depolarization. However, the actual R-R series are contaminated by outliers due to heart rhythm disturbances such as ectopic beats, which ought to be detected and corrected appropriately before HRV analysis. OBJECTIVE We have introduced a novel, lightweight, and near real-time method to detect and correct anomalies in the R-R series based on the singular spectrum analysis (SSA). This study aimed to assess the performance of the proposed method in terms of (1) detection performance (sensitivity, specificity, and accuracy); (2) root mean square error (RMSE) between the actual N-N series and the approximated outlier-cleaned R-R series; and (3) how it benchmarks against a competitor in terms of the relative RMSE. METHODS A lightweight SSA-based change-point detection procedure, improved through the use of a cumulative sum control chart with adaptive thresholds to reduce detection delays, monitored the series of R-R intervals in real time. Upon detection of an anomaly, the corrupted segment was substituted with the respective outlier-cleaned approximation obtained using recurrent SSA forecasting. Next, N-N intervals from a 5-minute ECG segment were extracted from each of the 18 records in the MIT-BIH Normal Sinus Rhythm Database. Then, for each such series, a number (randomly drawn integer between 1 and 6) of simulated ectopic beats were inserted at random positions within the series and results were averaged over 1000 Monte Carlo runs. Accordingly, 18,000 R-R records corresponding to 5-minute ECG segments were used to assess the detection performance whereas another 180,000 (10,000 for each record) were used to assess the error introduced in the correction step. Overall 198,000 R-R series were used in this study. RESULTS The proposed SSA-based algorithm reliably detected outliers in the R-R series and achieved an overall sensitivity of 96.6%, specificity of 98.4% and accuracy of 98.4%. Furthermore, it compared favorably in terms of discrepancies of the cleaned R-R series compared with the actual N-N series, outperforming an established correction method on average by almost 30%. CONCLUSIONS The proposed algorithm, which leverages the power and versatility of the SSA to both automatically detect and correct artifacts in the R-R series, provides an effective and efficient complementary method and a potential alternative to the current manual-editing gold standard. Other important characteristics of the proposed method include the ability to operate in near real-time, the almost entirely model-free nature of the framework which does not require historical training data, and its overall low computational complexity.

Model-free approach and methodology for data anomaly detection for real time diagnostic solution

CCECE 2010 ◽  
2010 ◽  
Author(s):  
Amar Kumar ◽  
Alka Srivastava ◽  
Nita Goel ◽  
Amiya Nayak
Keyword(s):  
Anomaly Detection ◽  
Real Time ◽  
Model Free ◽  
Model Free Approach
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