ECG Beat Classification Using Linear Prediction Error Signal

1991 ◽  
pp. 465-470
Author(s):  
Zygmunt Frankiewicz ◽  
Anwar Shrouf
Keyword(s):  
Prediction Error ◽  
Linear Prediction ◽  
Error Signal ◽  
Beat Classification

scholarly journals ECG Beat Classification using a Sliding Window and Correlation of the Three-bit Linear Prediction Error Signal

2020 ◽  
Vol 16 ◽  
Keyword(s):  
Prediction Error ◽  
Linear Prediction ◽  
Sudden Cardiac Arrest ◽  
Premature Ventricular Contraction ◽  
Sliding Window ◽  
Error Signal ◽  
Ventricular Contraction ◽  
Ecg Signals ◽  
Beat Classification

Sudden cardiac arrest (SCA) is responsible for half of all deaths due to heart disease. Most SCAs could be avoided by obtaining an early diagnosis from ECG recordings. The long-term monitoring systems record a large number of beats and require automatic detection and classification of the premature ventricular contraction (PVC) beats. Several ECG beat classification algorithms based on different methodologies have been developed and implemented. This paper presents a novel algorithm for automatic recognition of a premature ventricular contraction (PVC) beat based on a three-bit linear prediction error signal (LPES). The algorithm is composed of three main stages: signal denoising and QRS detection; nonlinear transformation of the linear prediction error signal e(n); and a sliding window. The proposed algorithm was tested using ECG signals from two recognized arrhythmia databases, MIT-BIH and AHA. The selected signals contained normal beats as well as abnormal beats. Sensitivity and specificity parameters were used to measure the accuracy of the proposed classifier. The sensitivity achieved using the proposed algorithm was 96.3% and the specificity was 99.0%. In addition to its accuracy, the main advantages of using the proposed algorithm are its simplicity and robustness.

Estimating Optimum Filter Length in Linear Prediction

1997 ◽  
Vol 51 (5) ◽  
pp. 718-720 ◽  
Author(s):  
O.-P. Sievänen
Keyword(s):  
Raman Spectra ◽  
Prediction Error ◽  
Linear Prediction ◽  
Signal To Noise Ratio ◽  
New Method ◽  
Signal To Noise ◽  
Filter Length ◽  
Optimum Filter ◽  
Good Signal ◽  
Prediction Filter

In this article a new method to estimate optimum filter length in linear prediction is described. Linear prediction was used to enhance resolution of a spectrum. In particular, the dependence of prediction error on filter length has been studied. With calculations of simulated spectra it is shown that the prediction error falls rapidly when the filter length attains its optimum value. This effect is quite pronounced when the spectrum has a good signal-to-noise ratio and the modified covariance method is used to calculate prediction filter coefficients. The method is illustrated with applications to real Raman spectra.

scholarly journals A quantitative reward prediction error signal in the ventral pallidum

2020 ◽  
Vol 23 (10) ◽  
pp. 1267-1276 ◽  
Author(s):  
David J. Ottenheimer ◽  
Bilal A. Bari ◽  
Elissa Sutlief ◽  
Kurt M. Fraser ◽  
Tabitha H. Kim ◽  
...  
Keyword(s):  
Prediction Error ◽  
Ventral Pallidum ◽  
Error Signal ◽  
Reward Prediction Error ◽  
Reward Prediction
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