Multistage principal component analysis based method for abdominal ECG decomposition

Petrolis, Robertas; Gintautas, Vladas; Kriščiukaitis, Algimantas

doi:10.1088/0967-3334/36/2/329

Cited by 17 publications

(16 citation statements)

References 17 publications

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“…Multistage PCA (MSPCA) has been used for the study of abdominal electrocardiogram decomposition. 27 MSPCA aims to identify the variables that can be well estimated by a linear model. It involves 2 steps: top-down and down-top steps.…”

Section: General Tools For Time Series Analysis Principal Component Amentioning

confidence: 99%

Utilization of Time Series Tools in Life-sciences and Neuroscience

2020

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Time series tools are part and parcel of modern day research. Their usage in the biomedical field; specifically, in neuroscience, has not been previously quantified. A quantification of trends can tell about lacunae in the current uses and point towards future uses. We evaluated the principles and applications of few classical time series tools, such as Principal Component Analysis, Neural Networks, common Auto-regression Models, Markov Models, Hidden Markov Models, Fourier Analysis, Spectral Analysis, in addition to diverse work, generically lumped under time series category. We quantified the usage from two perspectives, one, information technology professionals’, other, researchers utilizing these tools for biomedical and neuroscience research. For understanding trends from the information technology perspective, we evaluated two of the largest open source question and answer databases of Stack Overflow and Cross Validated. We quantified the trends in their application in the biomedical domain, and specifically neuroscience, by searching literature and application usage on PubMed. While the use of all the time series tools continues to gain popularity in general biomedical and life science research, and also neuroscience, and so have been the total number of questions asked on Stack overflow and Cross Validated, the total views to questions on these are on a decrease in recent years, indicating well established texts, algorithms, and libraries, resulting in engineers not looking for what used to be common questions a few years back. The use of these tools in neuroscience clearly leaves room for improvement.

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Section: General Tools For Time Series Analysis Principal Component Amentioning

confidence: 99%

Utilization of Time Series Tools in Life-sciences and Neuroscience

2020

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“…Principal Component Analysis (PCA) is a linear method based on the decomposition of multi-channel signals [8][9][10][11] . x .…”

Section: Pcamentioning

confidence: 99%

Earth Gravity Tide Signal Decomposition Model Based on PCA and Geophysical Information Extraction

Zhong¹,

Quan²

2015

Proceedings of the 2015 International Forum on Energy, Environment Science and Materials

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Abstract. Principal Component Analysis (PCA) is a main approach based on the second order statistics. It can remove the correlation between the signal components. PCA has been widely used in blind source separation and received attention because of its potential application in signal processing. PCA's principle algorithm, its simulation steps and its application in the earth gravity tide signal are introduced. The decomposition model can help us to analyze the geophysical information in signal. The result shows that PCA is a potential method in the earth gravity tide signal. So we can extract useful geophysical information from it to understand the interior structure of the earth and earthquake precursors.

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“…A number of NI-fECG extraction methods have been introduced in the past, including principal component analysis (PCA) [23], [24], independent component analysis (ICA) [23], [25], wavelet transform (WT) [26], adaptive neuro-fuzzy inference system (ANFIS) [27] or least mean squares (LMS) and recursive least squares (RLS) algorithms [28]. Recent studies [29]- [32] have shown that hybrid methods (HM), which combine the advantages of nonadaptive and adaptive approach to fECG extraction, achieve greater accuracy in fECG extraction than when using the methods individually.…”

Section: Introductionmentioning

confidence: 99%

Non-Invasive Fetal Electrocardiogram Extraction Based on Novel Hybrid Method for Intrapartum ST Segment Analysis

et al. 2021

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This study focuses on non-invasive fetal electrocardiogram extraction based on a novel hybrid method, which combines the advantages of non-adaptive and adaptive approaches for non-invasive fetal electrocardiogram morphological analysis. Besides estimating fetal heart rate, which is the main parameter used in the clinical practice, this study provides non-invasive ST segment analysis on data from Abdominal and Direct Fetal Electrocardiogram Database consisting of simultaneous traditionalgold standard invasive fetal scalp electrode and non-invasive fetal electrocardiogram recorded during delivery. This innovative approach utilizing the combination of independent component analysis and recursive least squares algorithms has the potential to extract valuable information from non-invasive fetal electrocardiogram in order to identify eventual sign of fetal distress. This was a prospective observational study of non-invasive fetal electrocardiogram, using 4 abdominally sited electrodes, against the traditional fetal scalp electrode on 8 patients. In terms of fetal heart rate estimation, the accuracy was high for all 8 tested patients with average value equaled 0.20 beats per minute and average value of 1.96 standard deviation equaled 5.80 beats per minute. In 7 patients, it was possible to perform the ST segment analysis with high accuracy in determining T/QRS in comparison with the reference fetal scalp electrode signal with average values and 1.96 standard deviation equaled 0.008 and 0.031 respectively. This study thus demonstrates that ST segment analysis is feasible using non-invasive fECG using the proposed hybrid method. INDEX TERMS Non-invasive fetal electrocardiography (NI-fECG), fetal heart rate (fHR), ST segment analysis (ST-analysis), hybrid method (HM), independent component analysis and recursive least squares (ICA-RLS), electronic fetal monitoring (EFM), fetal distress (FD), fetal scalp electrode (FSE).

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Multistage principal component analysis based method for abdominal ECG decomposition

Cited by 17 publications

References 17 publications

Utilization of Time Series Tools in Life-sciences and Neuroscience

Utilization of Time Series Tools in Life-sciences and Neuroscience

Earth Gravity Tide Signal Decomposition Model Based on PCA and Geophysical Information Extraction

Non-Invasive Fetal Electrocardiogram Extraction Based on Novel Hybrid Method for Intrapartum ST Segment Analysis

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