Tasnim Nishat Islam on LinkedIn: Python Data Science Code hacks: Part 2 (2024)

Tasnim Nishat Islam

Ph.D@UMBC, CETelehealth | Wireless Networks | ML

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Python Data Science Code hacks: Part2 Tasnim Nishat Islam on LinkedIn

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  • Tasnim Nishat Islam

    Ph.D@UMBC, CETelehealth | Wireless Networks | ML

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    I have completed my first semester of PhD with a CGPA 4.00/4.00 alhamdulillah. The three courses I took this semester has enhanced my knowledge for my research.The course Independent Study in CMPE is research based where I have made significant progress on my research on developing machine learning enabled wearable device solutions for remote health care. The poster can be found in this link: https://lnkd.in/epNBcbbtIntroduction to Machine learning was taught by Francis Ferraro. The course covered basics of ML formulation, CNN, encoder-decoder and ended with reinforcement learning. I really liked the assignments and the projects: https://lnkd.in/e3rxMscPDigital Signal Processing was taught by Seung-Jun Kim. The course covered basics of continuous and digital signal processing like Signal properties, Z-transform, sampling method, filter design. The assignments:https://lnkd.in/evdqWbQMI would like to thank my supervisor Mohamed Younis for constantly guiding me throughout this semester. Personally, I felt this semester has been a training of balancing coursework-researchwork-life, extremely proud of myself to learn new skillsets of adult life, hope to improve the skills throughout the PhD journey.#phd #coursework

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  • Tasnim Nishat Islam

    Ph.D@UMBC, CETelehealth | Wireless Networks | ML

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    Got featured in the news article! https://lnkd.in/dBwmYYDB

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  • Tasnim Nishat Islam

    Ph.D@UMBC, CETelehealth | Wireless Networks | ML

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    spot me hehe

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  • Tasnim Nishat Islam

    Ph.D@UMBC, CETelehealth | Wireless Networks | ML

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    I presented my poster in UMBC COEIT Research day. Special Thanks to my PI for the project Dr. Mohamed Younis who has guided me in the research and for the poster preparation. Here is the project details:Project Name: REAL TIME SEIZURE DETECTION THROUGH PPG MEASUREMENTSAbstract: Mental health has drawn increased attention in recent years, motivated by the social impact of the COVID-19 pandemic. Given the need to detect issues in early stages, relying on the conventional doctor visit based approach is not scalable and is too costly. The development of wearable mental health monitoring solutions is an effective means for filling the gap where individuals are provided with an assessment that can help them in adjusting their lifestyle. Seizure is one of the most causes of mental health problems and may cause epilepsy further. We opt to serve such a goal by devising a novel wearable-based approach for detecting seizure in real time. In this paper, we are experimenting with a benchmark dataset in namely, the OpenSeizure Dataset. For this dataset, the PPG and accelerator data is provided and the incident is labeled as Seizure and non-seizure. Seizure detection with PPG Data is challenging since discord can be encountered between brain signals and heart signals during the time of Seizure. Specifically, we are exploring the use of Photoplethysmography (PPG) data for the compatibility with wristbands. Although Electroencephalogram (EEG) is usually used to assess seizure, acquisition of EEG data is much more logistically-involving and expensive than ECG or PPG data. To assess seizure, we apply lightweight CNN Network to the PPG signals. The paper evaluates the different statistical features and their computational complexity.Poster link: https://lnkd.in/efpnkiqP#healthcare #AI #PPG #wearabledevice #poster

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  • Tasnim Nishat Islam

    Ph.D@UMBC, CETelehealth | Wireless Networks | ML

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    Part of my undergrad thesis work has been published in Elsevier Healthcare Analytics Alhamdulillah. Thank you Dr. Hafiz Imtiaz sir for constant guidance.Article link: https://lnkd.in/eXKeR-WcTitle: A robust neural network for privacy-preserving heart rate estimation in remote healthcare systemsAbstract: In this study, we propose a computationally-light and robust neural network for estimating heart rate in remote healthcare systems. We develop a model that can be trained on consumer-grade graphics processing units (GPUs), and can be deployed on edge devices for swift inference. We propose a hybrid model based on convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) architectures for estimating heart rate from Electrocardiogram (ECG) and Photoplethysmography (PPG) signals. Considering the sensitive nature of the ECG signals, we ensure a formal privacy guarantee, differential privacy, for the model training. We perform a tight accounting of the overall privacy budget of our training algorithm using the Rényi Differential Privacy technique. We demonstrate that our model outperforms state-of-the-art networks on a benchmark dataset for both ECG and PPG signals despite having a much smaller number of trainable parameters and, consequently, much smaller training and inference times. Our CNN-BiLSTM architecture can also provide excellent heart rate estimation performance even under strict privacy constraints. We develop a prototype Arduino-based data collection system that is low-cost, efficient, and useful for providing access to modern healthcare services to people living in remote areas.

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  • Tasnim Nishat Islam

    Ph.D@UMBC, CETelehealth | Wireless Networks | ML

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    Check out my other blogs: https://lnkd.in/eB_ftTHx

    Python Data Science Code hacks: Part 1 Tasnim Nishat Islam on LinkedIn

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