Open AccessMachine learning powered tools for automated analysis of muscle sympathetic nerve activity recordings
Author(s) -
Nolde Janis M.,
Marisol LugoGavidia Leslie,
Carnagarin Revathy,
Azzam Omar,
Galindo Kiuchi Márcio,
Mian Ajmal,
Schlaich Markus P.
Publication year - 2021
Publication title -
physiological reports
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.918
H-Index - 39
ISSN - 2051-817X
DOI - 10.14814/phy2.14996
Subject(s) - computer science , microneurography , signal processing , replicate , signal (programming language) , artificial intelligence , noise (video) , machine learning , toolbox , pattern recognition (psychology) , medicine , digital signal processing , computer hardware , baroreflex , heart rate , statistics , mathematics , blood pressure , image (mathematics) , radiology , programming language
Automated analysis and quantification of physiological signals in clinical practice and medical research can reduce manual labor, increase efficiency, and provide more objective, reproducible results. To build a novel platform for the analysis of muscle sympathetic nerve activity (MSNA), we employed state‐of‐the‐art data processing and machine learning applications. Data processing methods for integrated MSNA recordings were developed to evaluate signals regarding the overall quality of the signal, the validity of individual signal peaks regarding the potential to be MSNA bursts and the timing of their occurrence. An overall probability score was derived from this flexible platform to evaluate each individual signal peak automatically. Overall, three deep neural networks were designed and trained to validate individual signal peaks randomly sampled from recordings representing only electrical noise and valid microneurography recordings. A novel data processing method for the whole signal was developed to differentiate between periods of valid MSNA signal recordings and periods in which the signal was not available or lost due to involuntary movement of the recording electrode. A probabilistic model for timing of the signal bursts was implemented as part of the system. Machine Learning algorithms and data processing tools were implemented to replicate the complex decision‐making process of manual MSNA analysis. Validation of manual MSNA analysis including intra‐ and inter‐rater validity and a comparison with automated MSNA tools is required. The developed toolbox for automated MSNA analysis can be extended in a flexible way to include algorithms based on other datasets.