Detecting life-threatening patterns in Point-of-care ECG using efficient memory and processor power.

Francisco Bischoff on March 10, 2022

Abstract

Currently, Point-of-Care (POC) ECG monitoring works either as plot devices or alarms for abnormal cardiac rhythms using predefined normal trigger ranges and some rhythm analysis, which raises the problem of false alarms. In comparison, complex 12-derivation ECG machines are not suitable to use as simple monitors and are used with strict techniques for formal diagnostics. We aim to identify, on streaming data, life-threatening hearth electric patterns to reduce the number of false alarms, using low CPU and memory maintaining robustness. The study design is comparable to a diagnostic study, where high accuracy is essential. Physionet’s 2015 challenge yielded very good algorithms for reducing false alarms. However, none of the authors reported benchmarks, memory usage, robustness test, or context invariance that could assure its implementation on real monitors to reduce alarm fatigue indeed. We expect to identify the obstacles of detecting life-threatening ECG changes within memory, space, and CPU constraints and to reduce ECG monitor’s false alarms using the proposed methodology, and assess the feasibility of implementing the algorithm in the real world and other settings than ICU monitors.

The research team is well experienced in time-series and has studied the Matrix Profile since its beginning, being founders of the Matrix Profile Foundation whose goal is to have a concise and stable cross-language API for developing with the Matrix Profile technology.^1,2

Related Works

The CinC/Physionet Challenge 2015 produced several papers aiming to reduce false alarms on their dataset. On the following table it is listed the five life-threatening alarms present in their dataset.

Definition of the 5 alarm types used in CinC/Physionet Challenge 2015 challenge.

Alarm	Definition
Asystole	No QRS for at least 4 seconds
Extreme Bradycardia	Heart rate lower than 40 bpm for 5 consecutive beats
Extreme Tachycardia	Heart rate higher than 140 bpm for 17 consecutive beats
Ventricular Tachycardia	5 or more ventricular beats with heart rate higher than 100 bpm
Ventricular Flutter/Fibrillation	Fibrillatory, flutter, or oscillatory waveform for at least 4 seconds

About the ongoing project

The document submitted for approval is here.

To follow the thesis timeline you can access the full Gantt chart at Zenhub. Click here (you need a github account, but that’s it).

Reproducible Research³

This thesis will follow the compendium principles:

Following Standards

Aiming to create secure materials that are FAIR findable, accessible, interoperable, reusable

Research Data Management

• RDM checklist⁴
• Anticipate data products as part of your thesis outputs
• Think about what technologies to use

Missing values are a fact of life

• Usually, best solution is to leave blank
• NA or NULL are also good options
• NEVER use 0. Avoid numbers like -999
• Don’t make up your own code for missing values

Raw data are sacrosanct

• Don’t, not even with a barge pole, not for one second, touch or otherwise edit the raw data files. Do an manipulations in script

Three principles for good (file) names

Machine readable

• Regular expression and globbing friendly

  • Avoid spaces, punctuation, accented characters, case sensitivity

• Easy to compute on

  • Deliberate use of delimiters

  • Deliberate use of "-" and "_" allows recovery of metadata from the filenames:

    • "_" underscore used to delimit units of metadata I want to access later

    • "-" hyphen used to delimit words so our eyes don’t bleed

Human readable

• Borrowing the concept from slugs from semantic URLs

Play well with default ordering

• Put something numeric first

• Use the ISO 8601 standard for dates

• Left pad other numbers with zeros

License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Package dependencies

References

1. Bischoff F, Rodrigues PP. Tsmp: An r package for time series with matrix profile. Published online April 2019. doi:10.13140/rg.2.2.13040.30726

2. Van Benschoten A, Ouyang A, Bischoff F, Marrs T. MPA: A novel cross-language API for time series analysis. Journal of Open Source Software. 2020;5(49):2179. doi:10.21105/joss.02179

3. Krystalli A. R for Reproducible Research. Published online 2019. https://annakrystalli.me/rrresearch/

4. Centre EDC. Checklist for a data management plan. v.4.0. Published 2013. Accessed April 8, 2021. http://www.dcc.ac.uk/resources/data-management-plans