Andy Gao's MSR Thesis Talk: Addressing Time-series Signal Quality in Healthcare Data

[Artur Dubrawski]

Autonians,

If you are available please join this important talk by Andy who will be
delivering his MS thesis presentation tomorrow at 3pm.

Cheers,
Artur

---------- Forwarded message ---------
From: Chufan Gao <chufang at andrew.cmu.edu>
Date: Wed, Aug 10, 2022 at 10:28 AM
Subject: Re: MSR Thesis Talk: Addressing Time-series Signal Quality in
Healthcare Data
To: <ri-people at lists.andrew.cmu.edu>, Artur Dubrawski <awd at cs.cmu.edu>,
Jeff Schneider <jeff4 at andrew.cmu.edu>, Clermont, Gilles <cler at pitt.edu>,
Benedikt Boecking <boecking at andrew.cmu.edu>, Barbara (B.J.) Fecich <
barbarajean at cmu.edu>


Hi All,

A gentle reminder that this is happening tomorrow at 3pm EST in NSH A507!
*Zoom:* https://cmu.zoom.us/j/5223311585
<https://www.google.com/url?q=https://cmu.zoom.us/j/5223311585&sa=D&source=calendar&ust=1660068159564898&usg=AOvVaw36AlMyStV4M9wAJcfGvQ2R>


Sincerely,
Chufan Gao
Robotics Institute, AutonLab, Carnegie Mellon University


On Thu, Aug 4, 2022 at 2:30 PM Chufan Gao <chufang at andrew.cmu.edu> wrote:

>
>
> I will be giving my MSR thesis talk on *Thursday, August 11th, 2022 at
> 3:00 PM - 4:00 PM EST in NSH A507*. Everyone is invited!
>
> *Date: *Thursday, August 11th, 2022
> *Time: *3:00 PM - 4:00 PM EST
> *Location: *Newell-Simon Hall (NSH) A507
> *Zoom:* https://cmu.zoom.us/j/5223311585
> <https://www.google.com/url?q=https://cmu.zoom.us/j/5223311585&sa=D&source=calendar&ust=1660068159564898&usg=AOvVaw36AlMyStV4M9wAJcfGvQ2R>
>
>
> *Title:* Addressing Time-series Signal Quality in Healthcare Data
>
> *Abstract: *Healthcare data time-series signal quality assessment (SQA)
> plays a vital role in the accuracy and reliability of machine learning
> algorithms to analyze health metrics. However, these signals are often
> corrupted with different kinds of noises and artifacts, including Baseline
> Wander, Muscle Artifacts, Powerline Interference, and Equipment Failure.
> This can lead to vital, potentially deadly, errors in the medical domain.
> This can include inaccurate calculation of basic health features like Heart
> Rate, clinical alarm burnout from bedside monitors, as well as disrupting
> general downstream machine learning tasks. While some work has been done in
> the area of open-source signal quality analysis in general, there are very
> few open source implementations of signal quality analysis frameworks that
> attempt to reproduce and expand on existing results on open source
> datasets.
>
> First, we propose an open-source implementation of signal quality indices
> (SQIs) for analysis of electrocardiogram (ECG), plethysmography, and more.
> We aim to codify and reproduce SQIs and results from The Physionet Signal
> Quality Classification 2011 Challenge. We show that these SQIs may be used
> for signal quality outlier detection in a real world clinical dataset from
> University of Pittsburgh Medical Center (UPMC). Secondly, in the case of
> another common healthcare SQA issue: ECG denoising, we compare Wavelet,
> EMD, and Convolutional Autoencoder denoising techniques. We show that
> Convolutional Autoencoder denoising performs the best on the open MIT-BIH
> Arrhythmia Noise Stress Test dataset, and evaluate it on the UPMC dataset.
> To our knowledge, we are the first to provide an open source implementation
> of these two SQA tasks that is validated on public datasets. Ideally, this
> work serves as an accessible, open source, toolkit for signal quality
> analysis and ECG denoising.
>
> *Committee:*
> Professor Artur Dubrawski (advisor)
> Professor Jeff Schneider
> Professor Gilles Clermont
> PhD Student Benedikt Boecking
>
> Sincerely,
> Chufan Gao
> Robotics Institute, AutonLab, Carnegie Mellon University
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mailman.srv.cs.cmu.edu/pipermail/autonlab-users/attachments/20220810/bbae2acf/attachment.html>