1,677 research outputs found
A better way to monitor EKG
No full textNew study by Zeeshan Syed, assistant professor in the U-M's dept. of Electrical Engineering and Computer Science shows a better way to monitor EKG's for heart problems. New U-M research shows it can save lives Interview with Zeeshan Syedhttp://deepblue.lib.umich.edu/bitstream/2027.42/93373/1/syed_sept_11.mp
Unsupervised Similarity-Based Risk Stratification for Cardiovascular Events Using Long-Term Time-Series Data
No full textIn medicine, one often bases decisions upon a comparative analysis of patient data. In this paper, we build upon this observation and describe similarity-based algorithms to risk stratify patients for major adverse cardiac events. We evolve the traditional approach of comparing patient data in two ways. First, we propose similarity-based algorithms that compare patients in terms of their long-term physiological monitoring data. Symbolic mismatch identifies functional units in long-term signals and measures changes in the morphology and frequency of these units across patients. Second, we describe similarity-based algorithms that are unsupervised and do not require comparisons to patients with known outcomes for risk stratification. This is achieved by using an anomaly detection framework to identify patients who are unlike other patients in a population and may potentially be at an elevated risk. We demonstrate the potential utility of our approach by showing how symbolic mismatch-based algorithms can be used to classify patients as being at high or low risk of major adverse cardiac events by comparing their long-term electrocardiograms to that of a large population. We describe how symbolic mismatch can be used in three different existing methods: one-class support vector machines, nearest neighbor analysis, and hierarchical clustering. When evaluated on a population of 686 patients with available long-term electrocardiographic data, symbolic mismatch-based comparative approaches were able to identify patients at roughly a two-fold increased risk of major adverse cardiac events in the 90 days following acute coronary syndrome. These results were consistent even after adjusting for other clinical risk variables.National Science Foundation (U.S.) (CAREER award 1054419
COMBINED DETECTION OF URINARY MICRO ALBUMIN, Α1‑MICROGLOBULIN AND N-ACETYL-Β-D-GLUCOSAMINIDASE IN THE EARLY DIAGNOSIS OF DIABETIC NEPHROPATHY
No full textDr. Ameena Fatima, Dr. Zeeshan Rashid and Dr. Syed Ali Naqi and Dr. Hasnain Mahboob
COMPARISON BETWEEN ESOPHAGEAL FOREIGN BODY IN ADULT AND PEDIATRIC POPULATION
No full textDr. Muhammad Zeeshan Tariq*, Dr. Huda Rauf and Dr. Syed Muhammad Muntazir Mehd
EFFECT OF MODIFIED NEGATIVE PRESSURE WOUND THERAPY
No full text*Dr. Syed Muhammad Muntazir Mehdi, Dr. Huda Rauf and Dr. Muhammad Zeeshan Tari
ANALYSIS OF THE SURGICAL TREATMENT OF FRACTURE IN HIV POSITIVE PATIENTS: A CLINICAL STUDY
No full textDr. Syed Ali Naqi*, Dr. Zeeshan Rashid and Dr. Hasnain Mahboo
Mediapipe based Preprocessed VGGFace2 Dataset
No full textVGGFace2 Dataset and Face Mesh PreprocessingIntroductionThe VGGFace2 dataset is a large-scale face recognition dataset containing over 3.31 million images of 9,131 identities, with an average of 362 images per identity. The dataset is designed to include extensive variations in pose, age, illumination, ethnicity, and profession, making it one of the most diverse and challenging face recognition datasets available. For more details, please refer to the original publication:VGGFace2: A dataset for recognizing faces across pose and age - DOI: 10.48550/arXiv.1710.08092
Preprocessing Using MediaPipe 3D Face MeshOn this dataset, we applied the MediaPipe-based 3D face mesh algorithm to accurately detect faces while removing all background elements, including hair. Our preprocessing strictly retained facial landmarks, ensuring that only the essential facial features were preserved. This approach significantly enhanced the accuracy and generalization of our model, as the model was trained exclusively on landmark-based facial data.
Training and PerformanceThe preprocessed data was utilized to train Xception model, which resulted in remarkably accurate outcomes due to the strictly landmark-based facial representation. The model demonstrated robust performance including explainable-AI, proving that eliminating unnecessary background elements contributed positively to its efficiency and reliability.
CitationIf you use this dataset or the preprocessed version in your work, please cite both of the following:
VGGFace2 Dataset:
@article{Cao2018VGGFace2, title={VGGFace2: A dataset for recognizing faces across pose and age}, author={Cao, Qiong and Shen, Li and Xie, Weidi and Parkhi, Omkar M and Zisserman, Andrew}, journal={arXiv preprint arXiv:1710.08092}, year={2018}}
DOI: [10.48550/arXiv.1710.08092](https://doi.org/10.48550/arXiv.1710.08092) Preprocessed Dataset using MediaPipe:@dataset{Shah2025_MediaPipe_FaceMesh, title={MediaPipe-based 3D Face Mesh Preprocessed VGGFace2 Dataset}, author={Shah, Syed Taimoor Hussain and Shah, Syed Adil Hussain and Zamir, Ammara and Qayyum, Kainat and Shah, Syed Baqir Hussain and Fatima, Syeda Maryam and Deriu, Marco Agostino}, year={2025}, doi={10.5281/zenodo.15078557}} DOI: [10.5281/zenodo.15078557](https://doi.org/10.5281/zenodo.15078557)
ContactFor any questions or further details, please feel free to contact us.Syed Taimoor Hussain ShahPolitoBIOMed Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Turin, ItalyEmail: [email protected]: 0000-0002-6010-677
Quantifying morphology changes in time series data with skew
No full textThis paper examines strategies to quantify differences in the morphology of time series while accounting for time skew in the observed data. We adapt four measures originally designed for signal shape comparison: Dynamic Time-Warping (DTW), Earth Mover's Distance (EMD), Frochet Distance (FD), and Hausdorff Distance (HD). These morphology difference metrics on time series are compared in discriminative power and noise resistance on ECG signals as well as on a synthetic dataset. We use data from our experiments to shed light on the relative strengths of the methods
Hydraulic simulations to evaluate and predict design and operation of the Chashma Right Bank Canal
No full textIrrigation systems / Irrigation canals / Flow control / Velocity / Canal regulation techniques / Hydraulics / Simulation models / Design / Operations / Crop-based irrigation / Distributary canals / Water delivery / Policy / Protective irrigation / Water allocation / Water requirements / Sedimentation / Water distribution / Equity / Water conveyance / Pakistan / Chashma Right Bank Canal
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