Artifact Detection

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Carolyn Mcgregor - One of the best experts 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.

  • Service oriented architecture to support real-time implementation of Artifact Detection in critical care monitoring
    2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011
    Co-Authors: Shermeen Nizami, James Robert 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 - One of the best experts 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, David D. Mcmanus, Ki H. Chon, 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.

  • automatic motion and noise Artifact Detection in holter ecg data using empirical mode decomposition and statistical approaches
    IEEE Transactions on Biomedical Engineering, 2012
    Co-Authors: Jinseok Lee, David D. Mcmanus, Sneh Merchant, Ki H. Chon
    Abstract:

    We present a real-time method for the Detection of motion and noise (MN) Artifacts, which frequently interferes with accurate rhythm assessment when ECG signals are collected from Holter monitors. Our MN Artifact Detection approach involves two stages. The first stage involves the use of the first-order intrinsic mode function (F-IMF) from the empirical mode decomposition to isolate the Artifacts' dynamics as they are largely concentrated in the higher frequencies. The second stage of our approach uses three statistical measures on the F-IMF time series to look for characteristics of randomness and variability, which are hallmark signatures of MN Artifacts: the Shannon entropy, mean, and variance. We then use the receiver-operator characteristics curve on Holter data from 15 healthy subjects to derive threshold values associated with these statistical measures to separate between the clean and MN Artifacts' data segments. With threshold values derived from 15 training data sets, we tested our algorithms on 30 additional healthy subjects. Our results show that our algorithms are able to detect the presence of MN Artifacts with sensitivity and specificity of 96.63% and 94.73%, respectively. In addition, when we applied our previously developed algorithm for atrial fibrillation (AF) Detection on those segments that have been labeled to be free from MN Artifacts, the specificity increased from 73.66% to 85.04% without loss of sensitivity (74.48%-74.62%) on six subjects diagnosed with AF. Finally, the computation time was less than 0.2 s using a MATLAB code, indicating that real-time application of the algorithms is possible for Holter monitoring.

Shermeen Nizami - One of the best experts 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.

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

David D. Mcmanus - One of the best experts 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, David D. Mcmanus, Ki H. Chon, 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.

  • automatic motion and noise Artifact Detection in holter ecg data using empirical mode decomposition and statistical approaches
    IEEE Transactions on Biomedical Engineering, 2012
    Co-Authors: Jinseok Lee, David D. Mcmanus, Sneh Merchant, Ki H. Chon
    Abstract:

    We present a real-time method for the Detection of motion and noise (MN) Artifacts, which frequently interferes with accurate rhythm assessment when ECG signals are collected from Holter monitors. Our MN Artifact Detection approach involves two stages. The first stage involves the use of the first-order intrinsic mode function (F-IMF) from the empirical mode decomposition to isolate the Artifacts' dynamics as they are largely concentrated in the higher frequencies. The second stage of our approach uses three statistical measures on the F-IMF time series to look for characteristics of randomness and variability, which are hallmark signatures of MN Artifacts: the Shannon entropy, mean, and variance. We then use the receiver-operator characteristics curve on Holter data from 15 healthy subjects to derive threshold values associated with these statistical measures to separate between the clean and MN Artifacts' data segments. With threshold values derived from 15 training data sets, we tested our algorithms on 30 additional healthy subjects. Our results show that our algorithms are able to detect the presence of MN Artifacts with sensitivity and specificity of 96.63% and 94.73%, respectively. In addition, when we applied our previously developed algorithm for atrial fibrillation (AF) Detection on those segments that have been labeled to be free from MN Artifacts, the specificity increased from 73.66% to 85.04% without loss of sensitivity (74.48%-74.62%) on six subjects diagnosed with AF. Finally, the computation time was less than 0.2 s using a MATLAB code, indicating that real-time application of the algorithms is possible for Holter monitoring.

Nigel H. Lovell - One of the best experts on this subject based on the ideXlab platform.

  • robust ppg motion Artifact Detection using a 1 d convolution neural network
    Computer Methods and Programs in Biomedicine, 2020
    Co-Authors: Nigel H. Lovell, Siewcheok Ng
    Abstract:

    Abstract Background and Objectives Continuous monitoring of physiological parameters such as photoplethysmography (PPG) has attracted increased interest due to advances in wearable sensors. However, PPG recordings are susceptible to various Artifacts, and thus reducing the reliability of PPG-driven parameters, such as oxygen saturation, heart rate, blood pressure and respiration. This paper proposes a one-dimensional convolution neural network (1-D-CNN) to classify five-second PPG segments into clean or Artifact-affected segments, avoiding data-dependent pulse segmentation techniques and heavy manual feature engineering. Methods Continuous raw PPG waveforms were blindly allocated into segments with an equal length (5s) without leveraging any pulse location information and were normalized with Z-score normalization methods. A 1-D-CNN was designed to automatically learn the intrinsic features of the PPG waveform, and perform the required classification. Several training hyperparameters (initial learning rate and gradient threshold) were varied to investigate the effect of these parameters on the performance of the network. Subsequently, this proposed network was trained and validated with 30 subjects, and then tested with eight subjects, with our local dataset. Moreover, two independent datasets downloaded from the PhysioNet MIMIC II database were used to evaluate the robustness of the proposed network. Results A 13 layer 1-D-CNN model was designed. Within our local study dataset evaluation, the proposed network achieved a testing accuracy of 94.9%. The classification accuracy of two independent datasets also achieved satisfactory accuracy of 93.8% and 86.7% respectively. Our model achieved a comparable performance with most reported works, with the potential to show good generalization as the proposed network was evaluated with multiple cohorts (overall accuracy of 94.5%). Conclusion This paper demonstrated the feasibility and effectiveness of applying blind signal processing and deep learning techniques to PPG motion Artifact Detection, whereby manual feature thresholding was avoided and yet a high generalization ability was achieved.

  • Effect of ECG quality measures on piecewise-linear trend Detection for telehealth decision support systems
    2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, 2010
    Co-Authors: Stephen J. Redmond, Jim Basilakis, Nigel H. Lovell
    Abstract:

    Fledgling clinical decision support systems (DSSs) are being designed on the false assumption that consistent, good-quality signals are created in the unsupervised telehealth environment, but it has in fact been shown that signal quality is often very poor. Hence, it is important to investigate the detrimental impact of failing to recognize erroneous clinical parameter values. This study combines previous work in this area, related to Artifact Detection in electrocardiogram (ECG) signals, and piecewise-linear trend Detection in longitudinal heart rate parameter records, to investigate the impact of choosing to ignore ECG signal quality prior to trend Detection in the heart rate (HR) records. Using an Artifact Detection algorithm to improve the HR estimates from the ECG signals, when compared to reference HR values derived from human annotated 2453 ECGs from nine patients, resulted in a decrease in the estimation bias from 2.54 BPM (beat per minute) to 0.70 BPM and a decrease in the standard error from 0.47 BPM to 0.17 BPM. The application of the same Artifact Detection also results in a significant improvement in trend fitting, when compared to a fitting of the reference HR values, by reducing the mean RMSE value of the error in the trend fit from 2.14 BPM to 0.78 BPM and standard error from 0.49 BPM to 0.10 BPM. As trend Detection will be a component of future telehealth decision support systems, signal quality measures for unsupervised measurements are of paramount importance.

  • An investigation of the impact of Artifact Detection on heart rate determination from unsupervised electrocardiogram recordings
    IET Irish Signals and Systems Conference (ISSC 2009), 2009
    Co-Authors: Stephen J. Redmond, Jim Basilakis, Branko G. Celler, Nigel H. Lovell
    Abstract:

    A variation of an existing technique for movement Artifact Detection in single lead ECG signals acquired in the unsupervised telehealth environment is examined. The impact on heart rate (HR) estimation is investigated using this Artifact Detection technique to remove noisy sections of signal. The estimated Artifact masking and HR values are compared to a gold standard scoring, performed by consensus of an expert panel. The employment of the proposed Artifact Detection scheme shows an improvement in the estimated values of HR; the error in the estimated HR, from 126 of 192 signals, was less than ±0.5 BPM; compared to only 67 of 212 signals using no Artifact Detection; the estimation bias was reduced from an underestimation of -1.33 BPM to -0.63 BPM; the standard deviation of the error was reduced from 4.81 BPM to 3.58 BPM. The results indicate that the automated interpretation of inherently noisy ECG recordings, from the telehealth environment, becomes a feasible proposition when ECG signal quality indicators are leveraged.

  • An investigation of the impact of Artifact Detection on heart rate determination from unsupervised electrocardiogram recordings
    IET Irish Signals and Systems Conference (ISSC 2009), 2009
    Co-Authors: Stephen J. Redmond, Jim Basilakis, Branko G. Celler, Nigel H. Lovell
    Abstract:

    A variation of an existing technique for movement Artifact Detection in single lead ECG signals acquired in the unsupervised telehealth environment is examined. The impact on heart rate (HR) estimation is investigated using this Artifact Detection technique to remove noisy sections of signal. The estimated Artifact masking and HR values are compared to a gold standard scoring, performed by consensus of an expert panel. The employment of the proposed Artifact Detection scheme shows an improvement in the estimated values of HR; the error in the estimated HR, from 126 of 192 signals, was less than ±0.5 BPM; compared to only 67 of 212 signals using no Artifact Detection; the estimation bias was reduced from an underestimation of -1.33 BPM to -0.63 BPM; the standard deviation of the error was reduced from 4.81 BPM to 3.58 BPM. The results indicate that the automated interpretation of inherently noisy ECG recordings, from the telehealth environment, becomes a feasible proposition when ECG signal quality indicators are leveraged. (6 pages)