An Improved Independent Component Analysis Algorithm Based on Artificial Immune System

Chen, Liyuan; Lu, Chi-Jie

doi:10.7763/ijmlc.2013.v3.279

Cited by 8 publications

(3 citation statements)

References 15 publications

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“…The ICA method allows for separating the statistically independent signals and it has been already successfully used for the fECG extraction [25]. This algorithm is relatively computationally demanding; therefore, several faster variants of this method have been introduced [29], [99]. The most commonly used variant, the so-called FastICA algorithm, was successfully applied to extract the fECG from aECGs [72].…”

Section: Methodsmentioning

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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Section: Methodsmentioning

confidence: 99%

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

et al. 2021

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“…If the method is applied to signals where there are multiple Gaussian sources, then it cannot extract these sources [27,58]. In recent years, many improved ICA algorithms have been proposed [26,[59][60][61][62]. One of them, the FastICA algorithm, is widely used due to its fast convergence.…”

Section: Independent Component Analysismentioning

confidence: 99%

A novel algorithm based on ensemble empirical mode decomposition for non-invasive fetal ECG extraction

et al. 2021

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Non-invasive fetal electrocardiography appears to be one of the most promising fetal monitoring techniques during pregnancy and delivery nowadays. This method is based on recording electrical potentials produced by the fetal heart from the surface of the maternal abdomen. Unfortunately, in addition to the useful fetal electrocardiographic signal, there are other interference signals in the abdominal recording that need to be filtered. The biggest challenge in designing filtration methods is the suppression of the maternal electrocardiographic signal. This study focuses on the extraction of fetal electrocardiographic signal from abdominal recordings using a combination of independent component analysis, recursive least squares, and ensemble empirical mode decomposition. The method was tested on two databases, the Fetal Electrocardiograms, Direct and Abdominal with Reference Heartbeats Annotations and the PhysioNet Challenge 2013 database. The evaluation was performed by the assessment of the accuracy of fetal QRS complexes detection and the quality of fetal heart rate determination. The effectiveness of the method was measured by means of the statistical parameters as accuracy, sensitivity, positive predictive value, and F1-score. Using the proposed method, when testing on the Fetal Electrocardiograms, Direct and Abdominal with Reference Heartbeats Annotations database, accuracy higher than 80% was achieved for 11 out of 12 recordings with an average value of accuracy 92.75% [95% confidence interval: 91.19–93.88%], sensitivity 95.09% [95% confidence interval: 93.68–96.03%], positive predictive value 96.36% [95% confidence interval: 95.05–97.17%] and F1-score 95.69% [95% confidence interval: 94.83–96.35%]. When testing on the Physionet Challenge 2013 database, accuracy higher than 80% was achieved for 17 out of 25 recordings with an average value of accuracy 78.24% [95% confidence interval: 73.44–81.85%], sensitivity 81.79% [95% confidence interval: 76.59–85.43%], positive predictive value 87.16% [95% confidence interval: 81.95–90.35%] and F1-score 84.08% [95% confidence interval: 80.75–86.64%]. Moreover, the non-invasive ST segment analysis was carried out on the records from the Fetal Electrocardiograms, Direct and Abdominal with Reference Heartbeats Annotations database and achieved high accuracy in 7 from in total of 12 records (mean values μ < 0.1 and values of ±1.96σ < 0.1).

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“…It has been applied in the fields of meteorology [45], oceanography [46], volcanology [47,48], and remote sensing [49]. This study, however, uses wavelet transform for extracting the representative periodic components which affect the data series because ICA often leads to local minimum solution and the suitable source signals are not isolated [50]. Moreover, the order of the independent components (ICs) is difficult to be determined in comparison with wavelet transform.…”

Section: Extraction Of a Representative Time Series By Waveletmentioning

confidence: 99%

Identifying and Evaluating Chaotic Behavior in Hydro-Meteorological Processes

Kim

Lee

et al. 2015

Advances in Meteorology

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The aim of this study is to identify and evaluate chaotic behavior in hydro-meteorological processes. This study poses the two hypotheses to identify chaotic behavior of the processes. First, assume that the input data is the significant factor to provide chaotic characteristics to output data. Second, assume that the system itself is the significant factor to provide chaotic characteristics to output data. For solving this issue, hydro-meteorological time series such as precipitation, air temperature, discharge, and storage volume were collected in the Great Salt Lake and Bear River Basin, USA. The time series in the period of approximately one year were extracted from the original series using the wavelet transform. The generated time series from summation of sine functions were fitted to each series and used for investigating the hypotheses. Then artificial neural networks had been built for modeling the reservoir system and the correlation dimension was analyzed for the evaluation of chaotic behavior between inputs and outputs. From the results, we found that the chaotic characteristic of the storage volume which is output is likely a byproduct of the chaotic behavior of the reservoir system itself rather than that of the input data.

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An Improved Independent Component Analysis Algorithm Based on Artificial Immune System

Cited by 8 publications

References 15 publications

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

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

A novel algorithm based on ensemble empirical mode decomposition for non-invasive fetal ECG extraction

Identifying and Evaluating Chaotic Behavior in Hydro-Meteorological Processes

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