Robust Methods for Automated Selection of Cardiac Signals After Blind Source Separation

Wedekind, Daniel; Kleyko, Denis; Osipov, Evgeny; Malberg, Hagen; Zaunseder, Sebastian; Wiklund, Urban

doi:10.1109/tbme.2017.2788701

Cited by 7 publications

(3 citation statements)

References 32 publications

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“…Our study is completely different from the studies in [12] and [14] because we are trying to detect heartbeat indirectly by observing micro facial expression. Hence, common properties of ECG or MCG signals cannot be incorporated for automatic components selection process.…”

Section: Introductionmentioning

confidence: 85%

“…Thus, it needs to be selected automatically. In [12] authors proposed automated component selection routines based on heartbeat detections. Furthermore, their approach is compared with standard concepts such as higher order moments and frequencydomain features for automatic component selection by validating on ECG dataset which consists of healthy subjects performing a motion protocol and the MIT-BIH Arrhythmia Database [13].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Towards Human Pulse Rate Estimation from Face Video: Automatic Component Selection and Comparison of Blind Source Separation Methods

Ostankovich

Prathap

Afanasyev

2018

2018 International Conference on Intelligent Systems (IS)

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Human heartbeat can be measured using several different ways appropriately based on the patient condition which includes contact base such as measured by using instruments and non-contact base such as computer vision assisted techniques. Non-contact based approached are getting popular due to those techniques are capable of mitigating some of the limitations of contact-based techniques especially in clinical section. However, existing vision guided approaches are not able to prove high accurate result due to various reason such as the property of camera, illumination changes, skin tones in face image, etc. We propose a technique that uses video as an input and returns pulse rate in output. Initially, key point detection is carried out on two facial subregions: forehead and nose-mouth. After removing unstable features, the temporal filtering is applied to isolate frequencies of interest. Then four component analysis methods are employed in order to distinguish the cardiovascular pulse signal from extraneous noise caused by respiration, vestibular activity and other changes in facial expression. Afterwards, proposed peak detection technique is applied for each component which extracted from one of the four different component selection algorithms. This will enable to locate the positions of peaks in each component. Proposed automatic components selection technique is employed in order to select an optimal component which will be used to calculate the heartbeat. Finally, we conclude with comparison of four component analysis methods (PCA, FastICA, JADE, SHIBBS), processing face video datasets of fifteen volunteers with verification by an ECG/EKG Workstation as a ground truth.

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Section: Introductionmentioning

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Towards Human Pulse Rate Estimation from Face Video: Automatic Component Selection and Comparison of Blind Source Separation Methods

Ostankovich

Prathap

Afanasyev

2018

2018 International Conference on Intelligent Systems (IS)

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show abstract

“…3) Electrocardiogram ECG: Because of ECG is highly correlated with the diagnosis of many diseases, it has become an important part of the field of biomedical signal processing; research directions include signal monitoring, model algorithm optimization, disease screening, etc [34][35][36]. 4) Proposed model: According to the data characteristics of biological signals, an algorithm model for signal recognition is constructed [37][38].…”

Section: Distribution Of Scientific Research Forcesmentioning

confidence: 99%

International publication trends in Biomedical Signal Processing research: A bibliometric analysis (2009-2018)

Cui

Zhang

Yan

et al. 2019

Preprint

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Background This study aimed to analyze and assess the scientific outputs of biomedical signal processing by using bibliometric analysis.Methods Data were obtained from the WoSCC of Thomson Reuters, on January 21, 2019. VOSviewer (Leiden University, Van Eck and Waltman, Netherlands) and carrot 2 (Poznan University of Technology, Dawid Weiss, Poland) were used to analyze the knowledge maps and clusters of countries, research area and hot topics.Results A total of 335 articles on biomedical signal processing were identified. The number of publications increased only mildly during from 2009 (n=14) to 2018 (n=62). The majority of articles were published in the USA, and the leading institute was University of California System. Van Huffel S was the top authors on the topic, and the research area of “Engineering” generated the most publications. Cluter analysis (keywords and terms) indicated that “algorithm” and “extracted features” was the most hot topics on biomedical signal processing.Conclusion Overall, Through analysis of biosignal single processing related research in the past 10 years, the results found that the close international cooperation in this field, and the future research trends may be the signal acquisition methods and signal processing algorithms. They can provide reference for researchers in related field to choose research directions and find cooperative resources.

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Blind Information Extraction of Machine Faults Based on Separating Matrix

Li¹,

Tan²,

Yun³

2020

2020 IEEE 5th International Conference on Intelligent Transportation Engineering (ICITE)

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Robust Methods for Automated Selection of Cardiac Signals After Blind Source Separation

Abstract: The availability of robust cardiac component selectors for solving permutation indeterminacy facilitates the usage of spatio-temporal BSS to extract cardiac signals in artifact-sensitive minimum-contact vital signs monitoring techniques.

Cited by 7 publications

References 32 publications

Towards Human Pulse Rate Estimation from Face Video: Automatic Component Selection and Comparison of Blind Source Separation Methods

Towards Human Pulse Rate Estimation from Face Video: Automatic Component Selection and Comparison of Blind Source Separation Methods

International publication trends in Biomedical Signal Processing research: A bibliometric analysis (2009-2018)

Blind Information Extraction of Machine Faults Based on Separating Matrix

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