Outliers detection for accurate HRV-seizure baseline estimation using modern numerical algorithms

Abstract

Abstract Due to inevitable measurement artifacts, accurate baseline estimation remains an important issue in modern heart rate variability (HRV) analysis with partial epilepsy signals. To detect seizures, the HRV is commonly analyzed as a quasi stationary signal, which is correlated with neuroautonomic activity and considered to be noninvasive. A sudden seizure induces outliers and disturbances to the normal baseline that makes it difficult to provide accurate HRV estimation and outliers classification using the traditional boxplot methodology. A standard strategy to detect outliers implies computing the residuals for the estimated baseline and setting thresholds to extract the first and third quartiles from a histogram. In this work, we analyze modern numerical algorithms developed for HRV-seizure baseline estimation. We also propose a new iterative unbiased finite impulse response (I-UFIR) smoothing algorithm developed for colored measurement noise (CMN) using the backward Euler-based state-space model and show its advantages and shortcomings. A comparison of the estimation algorithms is provided using simulated synthetic and real data. It is demonstrated that the I-UFIR smoother is highly robust against sharp HRV changes that allows removing the outliers with a minimum loss in information. It is also shown that the time-frequency analysis allows analyzing accurately the HRV frequencies, provided that the outliers are removed. All methods are tested by partial seizures records taken from patients during continuous electroencephalography, electrocardiography, and video monitoring.