Baseline Drift Removal of ECG Signal

2018 ◽  
pp. 379-396
Author(s):  
Akash Kumar Bhoi ◽  
Karma Sonam Sherpa ◽  
Bidita Khandelwal
Keyword(s):  
Finite Impulse Response ◽  
Moving Average ◽  
Low Frequency ◽  
Baseline Drift ◽  
Fundamental Properties ◽  
St Segment ◽  
Before And After ◽  
Low Pass ◽  
Electrocardiogram Ecg ◽  
Weighted Moving Average

The filtering techniques are primarily used for preprocessing of the signal and have been implemented in a wide variety of systems for Electrocardiogram (ECG) analysis. It should be remembered that filtering of the ECG is contextual and should be performed only when the desired information remains undistorted. Removal of baseline drift is required in order to minimize changes in beat morphology that do not have cardiac origin, which is especially important when subtle changes in the ‘‘low-frequency'' ST segment are analyzed for the diagnosis of ischemia. Here, for baseline drift removal different filters such as Median, Low Pass Butter Worth, Finite Impulse Response (FIR), Weighted Moving Average and Stationary Wavelet Transform (SWT) are implemented. The fundamental properties of signal before and after baseline drift removal are statistically analyzed.

Baseline Drift Removal of ECG Signal

2016 ◽  
pp. 134-152
Author(s):  
Akash Kumar Bhoi ◽  
Karma Sonam Sherpa ◽  
Bidita Khandelwal
Keyword(s):  
Finite Impulse Response ◽  
Moving Average ◽  
Low Frequency ◽  
Baseline Drift ◽  
Fundamental Properties ◽  
St Segment ◽  
Before And After ◽  
Low Pass ◽  
Electrocardiogram Ecg ◽  
Weighted Moving Average

The filtering techniques are primarily used for preprocessing of the signal and have been implemented in a wide variety of systems for Electrocardiogram (ECG) analysis. It should be remembered that filtering of the ECG is contextual and should be performed only when the desired information remains undistorted. Removal of baseline drift is required in order to minimize changes in beat morphology that do not have cardiac origin, which is especially important when subtle changes in the ‘‘low-frequency'' ST segment are analyzed for the diagnosis of ischemia. Here, for baseline drift removal different filters such as Median, Low Pass Butter Worth, Finite Impulse Response (FIR), Weighted Moving Average and Stationary Wavelet Transform (SWT) are implemented. The fundamental properties of signal before and after baseline drift removal are statistically analyzed.

scholarly journals Degradation Trend Estimation and Prognosis of Large Low Speed Slewing Bearing Lifetime

2014 ◽  
Vol 493 ◽  
pp. 343-348 ◽  
Author(s):  
Bu Yung Kosasih ◽  
Wahyu Caesarendra ◽  
Kiet Tieu ◽  
Achmad Widodo ◽  
Craig A.S. Moodie ◽  
...  
Keyword(s):  
Time Domain ◽  
Moving Average ◽  
Low Frequency ◽  
Vibration Signal ◽  
Accurate Information ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Low Speed ◽  
Slewing Bearing ◽  
Low Pass

In many applications, degradation of bearing conditions is usually monitored by changes in time-domain features. However, in low speed (< 10 rpm) slewing bearing, these changes are not easily detected because of the low energy and low frequency of the vibration. To overcome this problem, a combined low pass filter (LPF) and adaptive line enhancer (ALE) signal pre-conditioning method is used. Time-domain features such as root mean square (RMS), skewness and kurtosis are extracted from the output signal of the combined LPF and ALE method. The extracted features show accurate information about the incipient of fault as compared to extracted features from the original vibration signal. This information then triggers the prognostic algorithm to predict the remaining lifetime of the bearing. The algorithm used to determine the trend of the non-stationary data is auto-regressive integrated moving average (ARIMA).

scholarly journals ECG Noise Reduction with the Use of the Ant Lion Optimizer Algorithm

2019 ◽  
Vol 9 (4) ◽  
pp. 4525-4529
Author(s):  
K. H. Hii ◽  
V. Narayanamurthy ◽  
F. Samsuri
Keyword(s):  
Finite Impulse Response ◽  
Median Filter ◽  
Cutoff Frequency ◽  
Ecg Signal ◽  
Ecg Signals ◽  
Ant Lion Optimizer ◽  
Low Pass ◽  
Ant Lion ◽  
Baseline Wander ◽  
Electrocardiogram Ecg

The electrocardiogram (ECG) signal is susceptible to noise and artifacts and it is essential to remove that noise in order to support any decision making for automatic heart disorder diagnosis systems. In this paper, the use of Ant Lion Optimizer (ALO) for optimizing and identifying the cutoff frequency of the ECG signal for low-pass filtering is investigated. Generally, the spectrums of the ECG signal are extracted from two classes: arrhythmia and supraventricular. Baseline wander is removed by a moving median filter. A dataset of the extracted features of the ECG spectrums is used to train the ALO. The performance of the ALO is investigated. The ALO-identified cutoff frequency is applied to a Finite Impulse Response (FIR) filter and the resulting signal is evaluated against the original clean and conventional filtered ECG signals. The results show that the intelligent ALO-based system successfully denoised the ECG signals more effectively than the conventional method. The accuracy percentage increased by 2%.

scholarly journals A Hierarchical Method for Removal of Baseline Drift from Biomedical Signals: Application in ECG Analysis

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Yurong Luo ◽  
Rosalyn H. Hargraves ◽  
Ashwin Belle ◽  
Ou Bai ◽  
Xuguang Qi ◽  
...  
Keyword(s):  
Separation Process ◽  
Electrode Impedance ◽  
Accurate Analysis ◽  
Baseline Drift ◽  
Biomedical Signals ◽  
Ecg Signals ◽  
Hierarchical Method ◽  
St Segment ◽  
Baseline Wander ◽  
Electrocardiogram Ecg

Noise can compromise the extraction of some fundamental and important features from biomedical signals and hence prohibit accurate analysis of these signals. Baseline wander in electrocardiogram (ECG) signals is one such example, which can be caused by factors such as respiration, variations in electrode impedance, and excessive body movements. Unless baseline wander is effectively removed, the accuracy of any feature extracted from the ECG, such as timing and duration of the ST-segment, is compromised. This paper approaches this filtering task from a novel standpoint by assuming that the ECG baseline wander comes from an independent and unknown source. The technique utilizes a hierarchical method including a blind source separation (BSS) step, in particular independent component analysis, to eliminate the effect of the baseline wander. We examine the specifics of the components causing the baseline wander and the factors that affect the separation process. Experimental results reveal the superiority of the proposed algorithm in removing the baseline wander.

scholarly journals Relationship between rainfall and streamflow in the La Plata Basin: annual cycles, interdecadal and multidecadal variability

2021 ◽  
Author(s):  
Carla Gulizia ◽  
Inés Camilloni
Keyword(s):  
Moving Average ◽  
Low Frequency ◽  
Physical Relationship ◽  
Annual Cycles ◽  
La Plata ◽  
La Plata Basin ◽  
Basin Scale ◽  
Wide Range ◽  
Low Pass ◽  
Spatio Temporal

The aim of this study is to understand the interaction between rainfall and streamflow variability in the La Plata basin (LPB) along a wide range of timescales. LPB is divided in six sub-basins associated to the main rivers (Paraguay, Paraná, Uruguay and Iguazú). The amplification of the streamflow response is addressed in order to evaluate to what extent river discharges variability can be explained by precipitation fluctuations. Mean annual cycles corresponding to 1931-2010 period and to each of the decades comprising it are analyzed. Streamflow interdecadal changes are observed in most of the gauging stations. In addition, an 11-year moving-average filter is applied to the normalized annual time series. Results exhibit a considerable higher percentage of explained variance in the streamflow filtered series, highlighting the predominance of low frequency variability present in these compared to those of precipitation. Consistently, river discharges show higher spectral density over decadal/interdecadal frequencies compared to precipitation analysis. A simple statistical approach to advance in the understanding of the complex rainfall-streamflow physical relationship is addressed with promising results: streamflow spectrums are derived directly from the precipitation spectrum, transformed by a 'basin' operator, characteristic of the basin itself. It is assumed that watersheds acts on precipitation as spatio-temporal integrators operating as low-pass filters, like a moving average. Streamflow power spectrums are simulated assuming that the underlying process is an autoregressive moving average (ARMA). Considering as the only input the sub-basin areal-averaged precipitation timeseries, results show that simulated streamflow spectrums fits effectively the observations at the sub-basin scale.

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