Identifying Outliers in HRV-Seizure Signals using p-shift UFIR Baseline Estimates

JORGE MUNOZ-MINJARES; YURIY SHMALIY; MISAEL LOPEZ-RAMIREZ; JORGE M. CRUZ-DUARTE

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Identifying Outliers in HRV-Seizure Signals using p-shift UFIR Baseline Estimates

机译：使用P移UFIR基线估计识别HRV癫痫发信号中的异常值

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Heart rate variability (HRV) is typically associated with neuroautonomic activity and viewed as a major non-invasive tool to detect seizures. The HRV has been assumed and analyzed as a stationary signal. However the presence of seizures can violate estimates of statistical parameters and conventional techniques intended to remove outliers can be inaccurate. A useful approach implies setting thresholds to compute the first and third quartiles from histogram data or residuals based on the estimated baseline. In this paper, we propose an accurate method to identify outliers in HRV measurements with partial epilepsy retaining relevant information. The baseline perturbed by the seizure in the HRV data is removed using the p-shift unbiased finite impulse response (UFIR) smoothing filter operating on optimal horizons. The residuals histogram is plotted and the upper bound (UB) and lower bound (LB) are computed as thresholds. A comparison is provided of a typical points detected in HRV/seizures based on several methods used to estimate the baseline. A time/frequency analysis is supplied to show the difference between the raw HRV and HRV without outliers. The method proposed is tested by partial seizures records taken from patients during continuous EEG/ECG and video monitoring.

机译：心率变异性（HRV）通常与神经虚程相关联，并视为检测癫痫发作的主要非侵入性工具。 HRV已被假定和分析为静止信号。然而，癫痫发作的存在可以违反统计参数的估计，并且旨在删除异常值的传统技术可以不准确。一种有用的方法意味着设置阈值来基于估计的基线计算从直方图数据或残差来计算第一和第三四分位数。在本文中，我们提出了一种准确的方法来识别HRV测量中的异常值，部分癫痫留下相关信息。通过在最佳视野上运行的P型移位非偏见的有限脉冲响应（UFIR）平滑过滤器，去除由HRV数据中癫痫发作的基线。绘制残差直方图，并且上限（UB）和下限（LB）被计算为阈值。提供了基于用于估计基线的几种方法在HRV /癫痫发作中检测到的典型点的比较。提供时间/频率分析，以显示RAW HRV和HRV之间的差异而没有异常值。所提出的方法是通过在连续EEG / ECG和视频监测期间从患者中的部分癫痫发作记录进行测试。

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来源
《WSEAS Transactions on Signal Processing》 |2018年第1期|共6页
作者
JORGE MUNOZ-MINJARES; YURIY SHMALIY; MISAEL LOPEZ-RAMIREZ; JORGE M. CRUZ-DUARTE;
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作者单位

Universidad de Guanajuato Deparment of Electronic Engineering;

Universidad de Guanajuato Deparment of Electronic Engineering;

Universidad de Guanajuato Deparment of Electronic Engineering;

Universidad de Guanajuato Deparment of Electronic Engineering;

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原文格式 PDF
正文语种 eng
中图分类声学;
关键词
Heart rate variability (HRV); Outliers; Seizures; P-shift (UFIR) smoothing filter;

机译：心率变异性（HRV）;异常值;癫痫发作;P班（UFIR）平滑过滤器;

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1. Identifying Outliers in HRV-Seizure Signals using p-shift UFIR Baseline Estimates [J] . JORGE MUNOZ-MINJARES, YURIY SHMALIY, MISAEL LOPEZ-RAMIREZ, WSEAS Transactions on Signal Processing . 2018,第Pta1期

机译：使用P移UFIR基线估计识别HRV癫痫发信号中的异常值
2. Online Chemical Sensor Signal Processing Using Estimation Theory: Quantification of Binary Mixtures of Organic Compounds in the Presence of Linear Baseline Drift and Outliers [J] . Sothivelr Karthick, Bender Florian, Josse Fabien, Sensors Journal, IEEE . 2016,第3期

机译：使用估计理论的在线化学传感器信号处理：在线性基线漂移和离群值存在下对有机化合物的二元混合物进行定量
3. Estimating complicated baselines in analytical signals using the iterative training of Bayesian regularized artificial neural networks [J] . Mani-Varnosfaderani Ahmad, Kanginejad Atefeh, Gilany Kambiz, Analytica chimica acta . 2016,第Null期

机译：使用贝叶斯正则化人工神经网络的迭代训练估算分析信号中的复杂基线
4. Outlier detection to identify artefacts in EEG signals [C] . Cluitmans P.J.M., Van De Velde M. Engineering in Medicine and Biology Society, 2000. Proceedings of the 22nd Annual International Conference of the IEEE . 2000

机译：异常检测以识别EEG信号中的伪像
5. A comparison of comorbidity, depression, and social support of elderly home care patients who are identified as outliers and elderly home care patients who are identified as non-outliers under the Medicare Prospective Payment System. [D] . Landry, Michael Thomas. 2001

机译：在Medicare前瞻性付款系统下，被识别为异常值的老年家庭护理患者和被识别为非异常值的老年家庭护理患者的合并症，抑郁和社会支持的比较。
6. 1823. Signal or Noise? A Comparison of Methods to Identify Outliers in Antimicrobial Use (AU) [O] . Rebekah W Moehring, Eric Lofgren, Elizabeth Dodds Ashley, -1

机译：1823.信号还是噪声？确定抗菌药物使用异常值（AU）的方法的比较
7. Figure 9: Six images were found, which were clearly outliers (A–F). An outlier analysis was performed, paying attention to: (G) the influence of each particular image on the final model according to Cook’s distance measure, (H) the studentized residuals (residuals divided by their estimated standard deviation), (I) the p-values of the outlier test (Bonferroni correction) and (J) the hat-values in order to identify influential observations. [O] . -1

机译：图9：找到了六个图像，显然是异常值（A-F）。进行了异常分析，注意：（g）根据库克距离测量的最终模型的每个特定图像对最终模型的影响，（h）学生化的残差（残留物除以其估计标准偏差），（i）p - 异常试验（Bonferroni校正）和（j）帽子值的值，以识别有影响力的观察结果。

Identifying Outliers in HRV-Seizure Signals using p-shift UFIR Baseline Estimates

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