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S. M. A. Salehizadeh

Researcher at University of Connecticut

Publications -  21
Citations -  540

S. M. A. Salehizadeh is an academic researcher from University of Connecticut. The author has contributed to research in topics: Multi-swarm optimization & Particle swarm optimization. The author has an hindex of 10, co-authored 21 publications receiving 447 citations. Previous affiliations of S. M. A. Salehizadeh include Amirkabir University of Technology & Worcester Polytechnic Institute.

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Journal ArticleDOI

A Novel Time-Varying Spectral Filtering Algorithm for Reconstruction of Motion Artifact Corrupted Heart Rate Signals During Intense Physical Activities Using a Wearable Photoplethysmogram Sensor

TL;DR: The results show that the SpaMA method has a potential for PPG-based HR monitoring in wearable devices for fitness tracking and health monitoring during intense physical activities and dynamics of heart rate variability can be accurately captured.
Journal ArticleDOI

Photoplethysmograph signal reconstruction based on a novel hybrid motion artifact detection-reduction approach. Part I: Motion and noise artifact detection

TL;DR: The accuracy and error values of the proposed MNA detection method were significantly higher and lower, respectively, than all other detection methods, and it is able to provide highly accurate onset and offset detection times of MNAs.
Journal ArticleDOI

A Robust Motion Artifact Detection Algorithm for Accurate Detection of Heart Rates From Photoplethysmographic Signals Using Time–Frequency Spectral Features

TL;DR: An approach based on using the time–frequency spectrum of PPG to first detect the MNA-corrupted data and next discard the nonusable part of the corrupted data, which consistently provided higher detection rates than the other three methods, with accuracies greater than 95% for all data.
Journal ArticleDOI

Photoplethysmograph Signal Reconstruction based on a Novel Motion Artifact Detection-Reduction Approach. Part II: Motion and Noise Artifact Removal

TL;DR: It is shown that the proposed IMAR approach can reliably reconstruct MNA corrupted data segments, as the estimated HR and SpO2 values do not significantly deviate from the uncorrupted reference measurements.
Proceedings ArticleDOI

Local Optima Avoidable Particle Swarm Optimization

TL;DR: Numerical results indicate that LOAPSO is considerably competitive due to its ability to avoid being trapped in local optima and to find the functions' global optimum as well as better convergence performance.