The Experts below are selected from a list of 22350 Experts worldwide ranked by ideXlab platform
Yu Chen - One of the best experts on this subject based on the ideXlab platform.
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EMBC - Stability analysis of QRS features to evaluate Signal Quality for multi-lead QRS dectection
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and, 2011Co-Authors: Zhe Zhang, Carolyn Lall, Yu ChenAbstract:In automated ECG monitoring, QRS detection performance is dependent on noise measurements on individual leads. A new Signal Quality measurement based on stability analysis of QRS complex features has been developed to assess individual ECG lead Quality. The new method was evaluated on the records of the MIT-BIH arrhythmia and NST databases. Results showed that the new Signal Quality measurement can be used to accurately assess ECG Signal Quality and can be easily incorporated into an existing multi-lead QRS detection algorithm for performance improvement.
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Stability analysis of QRS features to evaluate Signal Quality for multi-lead QRS dectection
2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011Co-Authors: Zhe Zhang, Carolyn Lall, Yu ChenAbstract:In automated ECG monitoring, QRS detection performance is dependent on noise measurements on individual leads. A new Signal Quality measurement based on stability analysis of QRS complex features has been developed to assess individual ECG lead Quality. The new method was evaluated on the records of the MIT-BIH arrhythmia and NST databases. Results showed that the new Signal Quality measurement can be used to accurately assess ECG Signal Quality and can be easily incorporated into an existing multi-lead QRS detection algorithm for performance improvement.
Mohanasankar Sivaprakasam - One of the best experts on this subject based on the ideXlab platform.
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EMBC - Ultrasound Signal Quality parameterization for image-free evaluation of arterial stiffness.
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and, 2014Co-Authors: Malay Ilesh Shah, Jayaraj Joseph, Mohanasankar SivaprakasamAbstract:We are in process of developing an image-free, single element ultrasound system for automated evaluation of arterial stiffness, we call it ARTSENS. The lack of a guiding image for arterial visualization necessitates intelligent analysis of ultrasound radio frequency (RF) echo Signals to obtain reliable measurements. In this paper, we propose a novel algorithm to parameterize the echo Signal received from the common carotid artery (CCA) to improve accuracy and reliability of arterial stiffness measurement. The echo Signal Quality is parameterized using features such as sharpness of arterial wall and energy ratio. A Signal Quality score is calculated by integrating the results from each feature. This score is used to triage the set of available echo Signals recorded from each subject and select the best Signal for computation of stiffness values. The performance of Signal Quality algorithm is tested using a database of carotid artery echo Signals recorded from 28 human volunteers. It was observed that both the accuracy and reliability of the stiffness measurements were improved after triaging using the Signal Quality parameterization algorithm.
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Ultrasound Signal Quality parameterization for image-free evaluation of arterial stiffness
2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2014Co-Authors: Malay Shah, Jayaraj Joseph, Mohanasankar SivaprakasamAbstract:We are in process of developing an image-free, single element ultrasound system for automated evaluation of arterial stiffness, we call it ARTSENS. The lack of a guiding image for arterial visualization necessitates intelligent analysis of ultrasound radio frequency (RF) echo Signals to obtain reliable measurements. In this paper, we propose a novel algorithm to parameterize the echo Signal received from the common carotid artery (CCA) to improve accuracy and reliability of arterial stiffness measurement. The echo Signal Quality is parameterized using features such as sharpness of arterial wall and energy ratio. A Signal Quality score is calculated by integrating the results from each feature. This score is used to triage the set of available echo Signals recorded from each subject and select the best Signal for computation of stiffness values. The performance of Signal Quality algorithm is tested using a database of carotid artery echo Signals recorded from 28 human volunteers. It was observed that both the accuracy and reliability of the stiffness measurements were improved after triaging using the Signal Quality parameterization algorithm.
Zhe Zhang - One of the best experts on this subject based on the ideXlab platform.
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EMBC - Stability analysis of QRS features to evaluate Signal Quality for multi-lead QRS dectection
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and, 2011Co-Authors: Zhe Zhang, Carolyn Lall, Yu ChenAbstract:In automated ECG monitoring, QRS detection performance is dependent on noise measurements on individual leads. A new Signal Quality measurement based on stability analysis of QRS complex features has been developed to assess individual ECG lead Quality. The new method was evaluated on the records of the MIT-BIH arrhythmia and NST databases. Results showed that the new Signal Quality measurement can be used to accurately assess ECG Signal Quality and can be easily incorporated into an existing multi-lead QRS detection algorithm for performance improvement.
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Stability analysis of QRS features to evaluate Signal Quality for multi-lead QRS dectection
2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011Co-Authors: Zhe Zhang, Carolyn Lall, Yu ChenAbstract:In automated ECG monitoring, QRS detection performance is dependent on noise measurements on individual leads. A new Signal Quality measurement based on stability analysis of QRS complex features has been developed to assess individual ECG lead Quality. The new method was evaluated on the records of the MIT-BIH arrhythmia and NST databases. Results showed that the new Signal Quality measurement can be used to accurately assess ECG Signal Quality and can be easily incorporated into an existing multi-lead QRS detection algorithm for performance improvement.
Xiao Hu - One of the best experts on this subject based on the ideXlab platform.
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Electrocardiogram Signal Quality Assessment Based on Structural Image Similarity Metric
IEEE Transactions on Biomedical Engineering, 2018Co-Authors: Yalda Shahriari, Richard Fidler, Michele M. Pelter, Andrea Villaroman, Xiao HuAbstract:Objective: We developed an image-based electrocardiographic (ECG) Quality assessment technique that mimics how clinicians annotate ECG Signal Quality. Methods: We adopted the structural similarity measure (SSIM) to compare images of two ECG records that are obtained from displaying ECGs in a standard scale. Then, a subset of representative ECG images from the training set was selected as templates through a clustering method. SSIM between each image and all the templates were used as the feature vector for the linear discriminant analysis classifier. We also employed three commonly used ECG Signal Quality index (SQI) measures: baseSQI, kSQI, and sSQI to compare with the proposed image Quality index (IQI) approach. We used 1926 annotated ECGs, recorded from patient monitors, and associated with six different ECG arrhythmia alarm types which were obtained previously from an ECG alarm study at the University of California, San Francisco (UCSF). In addition, we applied the templates from the UCSF database to test the SSIM approach on the publicly available PhysioNet Challenge 2011 data. Results: For the UCSF database, the proposed IQI algorithm achieved an accuracy of 93.1% and outperformed all the SQI metrics, baseSQI, kSQI, and sSQI, with accuracies of 85.7%, 63.7%, and 73.8% respectively. Moreover, evaluation of our algorithm on the PhysioNet data showed an accuracy of 82.5%. Conclusion : The proposed algorithm showed better performance for assessing ECG Signal Quality than traditional Signal processing methods. Significance: A more accurate assessment of ECG Signal Quality can lead to a more robust ECG-based diagnosis of cardiovascular conditions.
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Robust Assessment of Photoplethysmogram Signal Quality in the Presence of Atrial Fibrillation
2018 Computing in Cardiology Conference (CinC), 2018Co-Authors: Tania Pereira, Kais Gadhoumi, Rene Colorado, Kevin J Keenan, Karl Meisel, Xiao HuAbstract:A great deal of algorithms currently available to assess the Quality of photoplethysmogram (PPG) Signals is based on the similarity between pulses to derive Signal Quality indices. This approach has limitations when pulse morphology become variable due to the presence of some arrhythmia as in the case of atrial fibrillation (AFib). AFib is a heart arrhythmia characterized in the electrocardiogram mainly by an irregular irregularity. This arrhythmicity is reflected on PPG pulses by the presence of non-uniform pulses and poses challenges in the evaluation of the Signal Quality. In this work, we first test the performance of few algorithms from the body of methods reported in literature using a dataset of PPG records with AFib, and demonstrate their limitation. Second, we present a novel SVM-based classifier for PPG Quality assessment in 30s-long segments of PPG records extracted from pulse oximetry data of 13 stroke patients admitted to the UCSF medical center neuro ICU. 40 time-domain, frequency domain and non-linear features were extracted from all segments. Using an independent test set, the classifier reached a 0.94 accuracy, 0.95 sensitivity and 0.91 specificity. These results demonstrate the robustness of the proposed method in properly evaluating PPG Signal Quality in the presence of atrial fibrillation.
J.y. Wang - One of the best experts on this subject based on the ideXlab platform.
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A new method for evaluating ECG Signal Quality for multi-lead arrhythmia analysis
Computers in Cardiology, 2002Co-Authors: J.y. WangAbstract:A simple and computationally efficient new Signal Quality measure that is responsive to combinations of both physiological and non-physiological noise has been developed The new Signal Quality measure is based on the use of the area differences between successive QRS complexes. The Signal Quality assessment for each lead is made on the basis of the characteristics of the statistical distribution of the area differences obtained over a period of time. The new Signal Quality measure was evaluated using the 44 non-paced patient records from the MIT-BIH two-channel arrhythmia database. Results presented in histogram and cumulative histogram plots showed that the Signal Quality can be accurately assessed using this new method. Of the total 44 non-paced records, the new method identified 22 records where one of the two leads had much better Signal Quality than the other lead When this information was used for arrhythmia analysis, the averaged PVC false positive rate was 0.47% for leads that were selected by the new method, and 2.56% for leads that were not selected These results clearly showed that the new Signal Quality measure developed can be used to accurately assess the ECG Signal Quality and can be incorporated easily into existing arrhythmia algorithms for performance improvement.
