Heart rate monitoring from wrist-type PPG based on singular spectrum analysis with motion decision

2016 ◽  
Author(s):  
Yang Wang ◽  
Zhiwen Liu ◽  
Bin Dong
Keyword(s):  
Heart Rate ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Heart Rate Monitoring ◽  
Singular Spectrum

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.

scholarly journals Multi-Channel Singular Spectrum Analysis on Geocenter Motion and Its Precise Prediction

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1403
Author(s):  
Xin Jin ◽  
Xin Liu ◽  
Jinyun Guo ◽  
Yi Shen
Keyword(s):  
Solid Earth ◽  
Spectrum Analysis ◽  
Mean Squared Error ◽  
Moving Average ◽  
Singular Spectrum Analysis ◽  
Singular Spectrum ◽  
Autoregressive Moving Average Model ◽  
Seasonal Characteristics ◽  
Geocenter Motion ◽  
Moving Average Model

Geocenter is the center of the mass of the Earth system including the solid Earth, ocean, and atmosphere. The time-varying characteristics of geocenter motion (GCM) reflect the redistribution of the Earth’s mass and the interaction between solid Earth and mass loading. Multi-channel singular spectrum analysis (MSSA) was introduced to analyze the GCM products determined from satellite laser ranging data released by the Center for Space Research through January 1993 to February 2017 for extracting the periods and the long-term trend of GCM. The results show that the GCM has obvious seasonal characteristics of the annual, semiannual, quasi-0.6-year, and quasi-1.5-year in the X, Y, and Z directions, the annual characteristics make great domination, and its amplitudes are 1.7, 2.8, and 4.4 mm, respectively. It also shows long-period terms of 6.09 years as well as the non-linear trends of 0.05, 0.04, and –0.10 mm/yr in the three directions, respectively. To obtain real-time GCM parameters, the MSSA method combining a linear model (LM) and autoregressive moving average model (ARMA) was applied to predict GCM for 2 years into the future. The precision of predictions made using the proposed model was evaluated by the root mean squared error (RMSE). The results show that the proposed method can effectively predict GCM parameters, and the prediction precision in the three directions is 1.53, 1.08, and 3.46 mm, respectively.

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