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85 results on '"Masud, Mohammad Mehedy"'

1. Contrastive Self-Supervised Learning Based Approach for Patient Similarity: A Case Study on Atrial Fibrillation Detection from PPG Signal

Author

Shanto, Subangkar Karmaker, Saha, Shoumik, Rahman, Atif Hasan, Masud, Mohammad Mehedy, and Ali, Mohammed Eunus

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Machine Learning
Abstract

In this paper, we propose a novel contrastive learning based deep learning framework for patient similarity search using physiological signals. We use a contrastive learning based approach to learn similar embeddings of patients with similar physiological signal data. We also introduce a number of neighbor selection algorithms to determine the patients with the highest similarity on the generated embeddings. To validate the effectiveness of our framework for measuring patient similarity, we select the detection of Atrial Fibrillation (AF) through photoplethysmography (PPG) signals obtained from smartwatch devices as our case study. We present extensive experimentation of our framework on a dataset of over 170 individuals and compare the performance of our framework with other baseline methods on this dataset., Comment: 10 pages, 4 figures, Preprint submitted to Journal of Computers in Biology and Medicine

Published
2023

7. BayesBeat: Reliable Atrial Fibrillation Detection from Noisy Photoplethysmography Data

Author

Das, Sarkar Snigdha Sarathi, Shanto, Subangkar Karmaker, Rahman, Masum, Islam, Md. Saiful, Rahman, Atif, Masud, Mohammad Mehedy, and Ali, Mohammed Eunus

Subjects
Computer Science - Machine Learning, Electrical Engineering and Systems Science - Signal Processing
Abstract

Smartwatches or fitness trackers have garnered a lot of popularity as potential health tracking devices due to their affordable and longitudinal monitoring capabilities. To further widen their health tracking capabilities, in recent years researchers have started to look into the possibility of Atrial Fibrillation (AF) detection in real-time leveraging photoplethysmography (PPG) data, an inexpensive sensor widely available in almost all smartwatches. A significant challenge in AF detection from PPG signals comes from the inherent noise in the smartwatch PPG signals. In this paper, we propose a novel deep learning based approach, BayesBeat that leverages the power of Bayesian deep learning to accurately infer AF risks from noisy PPG signals, and at the same time provides an uncertainty estimate of the prediction. Extensive experiments on two publicly available dataset reveal that our proposed method BayesBeat outperforms the existing state-of-the-art methods. Moreover, BayesBeat is substantially more efficient having 40-200X fewer parameters than state-of-the-art baseline approaches making it suitable for deployment in resource constrained wearable devices., Comment: IMWUT March 2022, Vol 6 Article 8 (UbiComp 2022)

Published
2020
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9. Enhanced classification of left ventricular hypertrophy in cardiac patients using extended Siamese CNN.

Author

Farhad, Moomal, Masud, Mohammad Mehedy, Beg, Azam, Ahmad, Amir, and Memon, Sehar

Subjects
*LEFT ventricular hypertrophy, *CARDIAC hypertrophy, *ARTIFICIAL neural networks, *LEFT heart ventricle, *CARDIAC patients, *HEART
Abstract

Objective. Left ventricular hypertrophy (LVH) is the thickening of the left ventricle wall of the heart. The objective of this study is to develop a novel approach for the accurate assessment of LVH) severity, addressing the limitations of traditional manual grading systems. Approach. We propose the Multi-purpose Siamese Weighted Euclidean Distance Model (MSWED), which utilizes convolutional Siamese neural networks and zero-shot/few-shot learning techniques. Unlike traditional methods, our model introduces a cutoff distance-based approach for zero-shot learning, enhancing accuracy. We also incorporate a weighted Euclidean distance targeting informative regions within echocardiograms. Main results. We collected comprehensive datasets labeled by experienced echocardiographers, including Normal heart and various levels of LVH severity. Our model outperforms existing techniques, demonstrating significant precision enhancement, with improvements of up to 13% for zero-shot and few-shot learning approaches. Significance. Accurate assessment of LVH severity is crucial for clinical prognosis and treatment decisions. Our proposed MSWED model offers a more reliable and efficient solution compared to traditional grading systems, reducing subjectivity and errors while providing enhanced precision in severity classification. [ABSTRACT FROM AUTHOR]

Published
2024
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12. Inference Analysis of Video Quality of Experience in Relation with Face Emotion, Video Advertisement, and ITU-T P.1203.

Author

Selma, Tisa, Masud, Mohammad Mehedy, Bentaleb, Abdelhak, and Harous, Saad

Subjects
FACIAL expression & emotions (Psychology), STREAMING video & television, DATA encryption, SATISFACTION, EMOTION recognition
Abstract

This study introduces an FER-based machine learning framework for real-time QoE assessment in video streaming. This study's aim is to address the challenges posed by end-to-end encryption and video advertisement while enhancing user QoE. Our proposed framework significantly outperforms the base reference, ITU-T P.1203, by up to 37.1% in terms of accuracy and 21.74% after attribute selection. Our study contributes to the field in two ways. First, we offer a promising solution to enhance user satisfaction in video streaming services via real-time user emotion and user feedback integration, providing a more holistic understanding of user experience. Second, high-quality data collection and insights are offered by collecting real data from diverse regions to minimize any potential biases and provide advertisement placement suggestions. [ABSTRACT FROM AUTHOR]

Published
2024
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32. Data Security and Privacy

Author

Thuraisingham, Bhavani, primary, Masud, Mohammad Mehedy, additional, Parveen, Pallabi, additional, and Khan, Latifur, additional

Published
2017
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39. Summary and Directions

Author

Thuraisingham, Bhavani, primary, Masud, Mohammad Mehedy, additional, Parveen, Pallabi, additional, and Khan, Latifur, additional

Published
2017
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45. Introduction

Author

Thuraisingham, Bhavani, primary, Masud, Mohammad Mehedy, additional, Parveen, Pallabi, additional, and Khan, Latifur, additional

Published
2017
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49. Data Mining Techniques

Author

Thuraisingham, Bhavani, primary, Masud, Mohammad Mehedy, additional, Parveen, Pallabi, additional, and Khan, Latifur, additional

Published
2017
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