Adaptive mean and trend removal of heart rate variability using Kalman filtering

Schlögl, Alois; Fortin, Jürgen; Habenbacher, W.; Akay, Metin

doi:10.1109/iembs.2001.1018997

Cited by 8 publications

(6 citation statements)

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“…Trend filtering is an important task in signal analysis. It has a wide range of applications in areas like macroeconomics [17,18], social sciences [19], revenue management [20], and biological and medical sciences [21][22][23]. The presence of the trend term in a signal often gives rise to false extrema which in return degrades the performance of data analysis methods [24].…”

Section: Trend Filteringmentioning

confidence: 99%

Novel Filtering Methods for Image and Video Processing Applications

Ibrahim¹

2021

Preprint

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During the last few years, digital filtering methods for image/video processing applications have reached a satisfactory level. However, their performance degrades in the presence of noise, trend, motion, shape deformation, intensity inhomogeneity, shadows, or low image quality, to name a few. To cope with these challenges, this dissertation presents novel filtering methods for image/video processing applications that outperform the existing and state-of-the-art methods. The dissertation starts by introducing a novel trend filtering method that transforms the inter-frame registration problem into low complexity trend filtering problem. In the proposed method, Laplacian eigenmaps in conjunction with the modified empirical mode decomposition has been used to suppress the noise artifacts and the trend term. In multi-dimensional signals, the trend term is often referred to as non-uniform illumination or global intensity inhomogeneity. This dissertation presents a new filtering method for estimating the global intensity inhomogeneity in two dimensional and volume images. Global intensity inhomogeneity often arises due to the imperfections of data acquisition device, direction of source light, and properties of the subject under study. The proposed method generates a high-pass filter based on the grey-weighted distance transform of the frequency content of an image/volume. It provides an accurate estimation of global intensity inhomogeneity without any parameter tweaking, which makes it applicable to many imaging modalities. The dissertation also presents a filtering methodology to cope with local intensity inhomogeneity that gives rise to shadow artifacts. These artifacts appear as sharp discontinuities and are often corrected at different scales and orientations. The proposed method makes use of decimation-free directional filter bank to suppress the local intensity inhomogeneity and shadow artifacts irrespective of scale and orientation. In addition to intensity inhomogeneity correction, the dissertation also presents a filtering method that utilizes the Gabor filter bank to generate rotation invariant feature codes. The effectiveness of the proposed method has been evaluated in both identification and verification modes for fingerprint recognition. The uniqueness of the presented filtering methods lies in the fact that they are essentially parameter free and can easily be scaled to higher dimensions. This makes them applicable to many different image/video processing applications with least of effort from the end user, i.e., eliminating the user biases.

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Section: Trend Filteringmentioning

confidence: 99%

Novel Filtering Methods for Image and Video Processing Applications

Ibrahim¹

2021

Preprint

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“…Kalman Filter is a predictor model that estimates the state of a process using time update and measurement update equations [8]. We try to predict the health parameter values like heart rate, blood pressure etc.…”

Section: Prediction Using Kalman Filtermentioning

confidence: 99%

Online Incremental Learning Algorithm for anomaly detection and prediction in health care

Raghuraman

Senthurpandian

Shanmugasundaram

et al. 2014

2014 International Conference on Recent Trends in Information Technology

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Anomaly Detection in health care by monitoring the vital health parameters of patients is a challenging problem in machine learning. The existing algorithms do not process the data incrementally and hence are not very effective in predicting the anomalies accurately and at the correct instance. In this paper, in order to process the health data in an online fashion a novel Online Incremental Learning Algorithm (OILA) is proposed. The OILA predicts the health parameters using a regression based approach with a feedback mechanism to reduce error. An alert is generated when an anomaly is seen in the health parameters, thus alerting the doctor to be cautious. The algorithm is compared with Kalman Filter for comparing the prediction capabilities of OILA with Kalman Filter. The proposed algorithm is validated with real time health parameter data sets for health parameters namely heart rate and blood pressure.

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“…The most popular methods are the moving average method [1,2,10,[15][16][17] and least squares fit using polynomials, which includes the linear trend removal and kriging methods [1][2][3][4]6,10,11,13,14,[16][17][18][19]25]. The other methods are the first differencing method [1,3,12,13], the spectral method [1,4,10], and the method of repeatedly employing the Kalman filter [21,22]. As well as these methods, the following tools employing the wavelet transformation have been widely applied as detrending filters [23,[26][27][28][29][30]: Daubechies family wavelet (dbN), Symlet wavelet (symN), Coiflet wavelet (coifN), biorthogonal wavelet pair (biorNr.Nd), and reverse biorthogonal wavelet pair (rbioNr.Nd).…”

Section: Introductionmentioning

confidence: 99%

“…In different applications, both with or without the detrending procedure frequently affect the following results: accuracy of the spectral method [1,4,10,11], stationary or nonstationary property [1][2][3][4][12][13][14][15], accuracy of the kriging method [14], standard deviation of regression or kriging method [1][2][3]12,[16][17][18], and spectral density [11,12,19,20], etc. Thus, the detrending procedure is important in many problems [1][2][3][4][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%