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Artifact Detection

The Experts below are selected from a list of 1518 Experts worldwide ranked by ideXlab platform

Carolyn Mcgregor – 1st expert on this subject based on the ideXlab platform

  • An Artifact Detection Framework for Clinical Decision Support Systems
    IFMBE Proceedings, 2020
    Co-Authors: Shermeen Nizami, James R. Green, Carolyn Mcgregor

    Abstract:

    This research develops a standardized framework to integrate Artifact Detection (AD) in computerized Clinical Decision Support Systems (CDSS). Review of the state of the art has revealed a number of limitations currently preventing the widespread implementation of AD algorithms within CDSS. To address those limitations, this paper develops a novel component-based AD framework for integration in CDSS. The novelty of this research is the development of a Common Reference Model (CRM) with standard definitions for component interfaces. These definitions include common physiologic data attributes of: (1) type; (2) frequency; (3) length; and (4) Signal Quality Indicator.

  • Implementation of Artifact Detection in Critical Care: A Methodological Review
    IEEE Reviews in Biomedical Engineering, 2013
    Co-Authors: Shermeen Nizami, James R. Green, Carolyn Mcgregor

    Abstract:

    Artifact Detection (AD) techniques minimize the impact of Artifacts on physiologic data acquired in critical care units (CCU) by assessing quality of data prior to clinical event Detection (CED) and parameter derivation (PD). This methodological review introduces unique taxonomies to synthesize over 80 AD algorithms based on these six themes: 1) CCU; 2) physiologic data source; 3) harvested data; 4) data analysis; 5) clinical evaluation; and 6) clinical implementation. Review results show that most published algorithms: a) are designed for one specific type of CCU; b) are validated on data harvested only from one OEM monitor; c) generate signal quality indicators (SQI) that are not yet formalized for useful integration in clinical workflows; d) operate either in standalone mode or coupled with CED or PD applications; e) are rarely evaluated in real-time; and f) are not implemented in clinical practice. In conclusion, it is recommended that AD algorithms conform to generic input and output interfaces with commonly defined data: 1) type; 2) frequency; 3) length; and 4) SQIs. This shall promote: a) reusability of algorithms across different CCU domains; b) evaluation on different OEM monitor data; c) fair comparison through formalized SQIs; d) meaningful integration with other AD, CED and PD algorithms; and e) real-time implementation in clinical workflows.

  • EMBC – Service oriented architecture to support real-time implementation of Artifact Detection in critical care monitoring
    Conference proceedings : … Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and, 2011
    Co-Authors: Shermeen Nizami, James R. Green, Carolyn Mcgregor

    Abstract:

    The quality of automated real-time critical care monitoring is impacted by the degree of signal Artifact present in clinical data. This is further complicated when different clinical rules applied for disease Detection require source data at different frequencies and different signal quality. This paper proposes a novel multidimensional framework based on service oriented architecture to support real-time implementation of clinical Artifact Detection in critical care settings. The framework is instantiated through a Neonatal Intensive Care case study which assesses signal quality of physiological data streams prior to Detection of late-onset neonatal sepsis. In this case study requirements and provisions of Artifact and clinical event Detection are determined for real-time clinical implementation, which forms the second important contribution of this paper.

Ki H. Chon – 2nd expert on this subject based on the ideXlab platform

  • Developing a novel noise Artifact Detection algorithm for smartphone PPG signals: Preliminary results
    2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), 2018
    Co-Authors: Syed Khairul Bashar, David D. Mcmanus, Apurv Soni, Ki H. Chon

    Abstract:

    Pulsatile signals recorded from a smartphone are often corrupted with noise Artifacts, which hampers accuracy of the peak Detection and consequently leads to inaccurate heart rate estimation. In this paper, we propose a novel approach which uses an algorithm based on variable frequency complex demodulation (VFCDM) to detect noise Artifacts in the smartphone’s pulsatile signal recorded from a fingertip video. The ultimate goal is to increase the accuracy of atrial fibrillation (AF) Detection. In the time-frequency spectra obtained from VFCDM, thresholds are imposed on both the magnitude of the dominant frequency component at each time instant and on the successive difference of the significant frequency component in the heart rate range to enable accurate noise Artifact Detection. For this preliminary analysis, the performance of the proposed method has been evaluated on 200 subjects; the data were collected during a smartphone-based AF screening study in India. The proposed method is shown to detect noise Artifacts in pulsatile signals with 91.16% accuracy, demonstrating the potential to reduce false alarms when only data segments identified as clean are used for AF Detection.

  • BHI – Developing a novel noise Artifact Detection algorithm for smartphone PPG signals: Preliminary results
    2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), 2018
    Co-Authors: Syed Khairul Bashar, David D. Mcmanus, Apurv Soni, Ki H. Chon

    Abstract:

    Pulsatile signals recorded from a smartphone are often corrupted with noise Artifacts, which hampers accuracy of the peak Detection and consequently leads to inaccurate heart rate estimation. In this paper, we propose a novel approach which uses an algorithm based on variable frequency complex demodulation (VFCDM) to detect noise Artifacts in the smartphone’s pulsatile signal recorded from a fingertip video. The ultimate goal is to increase the accuracy of atrial fibrillation (AF) Detection. In the time-frequency spectra obtained from VFCDM, thresholds are imposed on both the magnitude of the dominant frequency component at each time instant and on the successive difference of the significant frequency component in the heart rate range to enable accurate noise Artifact Detection. For this preliminary analysis, the performance of the proposed method has been evaluated on 200 subjects; the data were collected during a smartphone-based AF screening study in India. The proposed method is shown to detect noise Artifacts in pulsatile signals with 91.16% accuracy, demonstrating the potential to reduce false alarms when only data segments identified as clean are used for AF Detection.

  • photoplethysmograph signal reconstruction based on a novel hybrid motion Artifact Detection reduction approach part i motion and noise Artifact Detection
    Annals of Biomedical Engineering, 2014
    Co-Authors: Jo Woon Chong, Ki H. Chon, David D. Mcmanus, S M A Salehizadeh, Chad E Darling, Yitzhak Mendelson

    Abstract:

    Motion and noise Artifacts (MNA) are a serious obstacle in utilizing photoplethysmogram (PPG) signals for real-time monitoring of vital signs. We present a MNA Detection method which can provide a clean vs. corrupted decision on each successive PPG segment. For motion Artifact Detection, we compute four time-domain parameters: (1) standard deviation of peak-to-peak intervals (2) standard deviation of peak-to-peak amplitudes (3) standard deviation of systolic and diastolic interval ratios, and (4) mean standard deviation of pulse shape. We have adopted a support vector machine (SVM) which takes these parameters from clean and corrupted PPG signals and builds a decision boundary to classify them. We apply several distinct features of the PPG data to enhance classification performance. The algorithm we developed was verified on PPG data segments recorded by simulation, laboratory-controlled and walking/stair-climbing experiments, respectively, and we compared several well-established MNA Detection methods to our proposed algorithm. All compared Detection algorithms were evaluated in terms of motion Artifact Detection accuracy, heart rate (HR) error, and oxygen saturation (SpO2) error. For laboratory controlled finger, forehead recorded PPG data and daily-activity movement data, our proposed algorithm gives 94.4, 93.4, and 93.7% accuracies, respectively. Significant reductions in HR and SpO2 errors (2.3 bpm and 2.7%) were noted when the Artifacts that were identified by SVM-MNA were removed from the original signal than without (17.3 bpm and 5.4%). The accuracy and error values of our proposed method were significantly higher and lower, respectively, than all other Detection methods. Another advantage of our method is its ability to provide highly accurate onset and offset Detection times of MNAs. This capability is important for an automated approach to signal reconstruction of only those data points that need to be reconstructed, which is the subject of the companion paper to this article. Finally, our MNA Detection algorithm is real-time realizable as the computational speed on the 7-s PPG data segment was found to be only 7 ms with a Matlab code.

Shermeen Nizami – 3rd expert on this subject based on the ideXlab platform

  • An Artifact Detection Framework for Clinical Decision Support Systems
    IFMBE Proceedings, 2020
    Co-Authors: Shermeen Nizami, James R. Green, Carolyn Mcgregor

    Abstract:

    This research develops a standardized framework to integrate Artifact Detection (AD) in computerized Clinical Decision Support Systems (CDSS). Review of the state of the art has revealed a number of limitations currently preventing the widespread implementation of AD algorithms within CDSS. To address those limitations, this paper develops a novel component-based AD framework for integration in CDSS. The novelty of this research is the development of a Common Reference Model (CRM) with standard definitions for component interfaces. These definitions include common physiologic data attributes of: (1) type; (2) frequency; (3) length; and (4) Signal Quality Indicator.

  • Implementation of Artifact Detection in Critical Care: A Methodological Review
    IEEE Reviews in Biomedical Engineering, 2013
    Co-Authors: Shermeen Nizami, James R. Green, Carolyn Mcgregor

    Abstract:

    Artifact Detection (AD) techniques minimize the impact of Artifacts on physiologic data acquired in critical care units (CCU) by assessing quality of data prior to clinical event Detection (CED) and parameter derivation (PD). This methodological review introduces unique taxonomies to synthesize over 80 AD algorithms based on these six themes: 1) CCU; 2) physiologic data source; 3) harvested data; 4) data analysis; 5) clinical evaluation; and 6) clinical implementation. Review results show that most published algorithms: a) are designed for one specific type of CCU; b) are validated on data harvested only from one OEM monitor; c) generate signal quality indicators (SQI) that are not yet formalized for useful integration in clinical workflows; d) operate either in standalone mode or coupled with CED or PD applications; e) are rarely evaluated in real-time; and f) are not implemented in clinical practice. In conclusion, it is recommended that AD algorithms conform to generic input and output interfaces with commonly defined data: 1) type; 2) frequency; 3) length; and 4) SQIs. This shall promote: a) reusability of algorithms across different CCU domains; b) evaluation on different OEM monitor data; c) fair comparison through formalized SQIs; d) meaningful integration with other AD, CED and PD algorithms; and e) real-time implementation in clinical workflows.

  • EMBC – Service oriented architecture to support real-time implementation of Artifact Detection in critical care monitoring
    Conference proceedings : … Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and, 2011
    Co-Authors: Shermeen Nizami, James R. Green, Carolyn Mcgregor

    Abstract:

    The quality of automated real-time critical care monitoring is impacted by the degree of signal Artifact present in clinical data. This is further complicated when different clinical rules applied for disease Detection require source data at different frequencies and different signal quality. This paper proposes a novel multidimensional framework based on service oriented architecture to support real-time implementation of clinical Artifact Detection in critical care settings. The framework is instantiated through a Neonatal Intensive Care case study which assesses signal quality of physiological data streams prior to Detection of late-onset neonatal sepsis. In this case study requirements and provisions of Artifact and clinical event Detection are determined for real-time clinical implementation, which forms the second important contribution of this paper.