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Medical Devices PhD Qualifying Exam

Given by H. Troy Nagle

Instructions and Study Guide

Please register your intent to take the exam by 5:00pm on September 24 to

Exam Details

• Date and Time: Date 9/25/2023, 1:00pm
• Location: EB3-4142. Three-hour in-person exam (due in writing or email to by 4:00pm on 9/25/2023)
• Format: This exam focuses on the application of a basic understanding of
medical devices, and the science and engineering principles, which underpin
their design and development. You will be expected to answer qualitative
(essay) questions that require critical thinking on the topics listed below.
• The references and resources provided contain all topics that will be tested.


• One Overview is listed as a resource. Specific sections may be useful
depending on the particulars of your answers to the questions.
• Four other journal articles are also recommended. You have access to all
electronic versions of all documents listed via the NC State library and Pubmed.
If you are unable to access the documents, please email the instructor.
• No other materials are required; other resources (non-AI) can be used. Cite your
• You may either submit the assignment via pen and paper or electronically.

Potential Topics

General topics and concepts may include:
• Basic principles of vital signs monitoring, in particular:
o Sensor selection, signal capturing devices, performance metrics, and
device testing methods
• Innovative signal-processing algorithms in specific target applications
• Methods to demonstrate new device feasibility
• Implementation of new concepts in vital-signs monitoring

References/Resources  link to pdf files


Shaffer F, Ginsberg JP. An Overview of Heart Rate Variability Metrics and Norms.
Front Public Health. 2017 Sep 28;5:258.

Other Journal Articles

Dogan, H., Dogan, R.O. A Comprehensive Review of Computer-based Techniques
for R-Peaks/QRS Complex Detection in ECG Signal. Arch Computat Methods Eng
30, 3703–3721 (2023).

Hoog Antink, C., Mai, Y., Peltokangas, M. et al. Accuracy of heart rate variability
estimated with reflective wrist-PPG in elderly vascular patients. Sci Rep 11, 8123

João Vitorino, Lourenço Rodrigues, Eva Maia, Isabel Praça, and André Lourenço,
Adversarial Robustness and Feature Impact Analysis for Driver Drowsiness
Detection, arXiv:2303.13649 [cs.LG].

Syem Ishaque, Naimul Khan and Sri Krishnan, Trends in Heart-Rate Variability
Signal Analysis, Frontiers in Digital Health, February 2021,

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