RecogNoise: Machine-Learning-Based Recognition of Noisy Segments in Electrocardiogram Signals

Amin Aminifar1, Soheil Khooyooz1, Anice Jahanjoo2, Salar Shakibhamedan2, Nima TaheriNejad1,2
1 Heidelberg University, Germany
2 Technische Universit ̈at Wien (TU Wien), Austria
IEEE International Symposium on Circuits and Systems (ISCAS), 2024
DOI: https://doi.org/10.1109/ISCAS58744.2024.10558670

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Graphical abstract

RR Interval Computation Based on Pan and Tompkins and Two Moving Average Algorithms for Noisy Signals
RR Interval Computation Based on Pan and Tompkins and Two Moving Average Algorithms for Noisy Signals

Abstract

Today, wearable technology is frequently used for continuous monitoring of physiological indicators in the health-care domain. However, mobile-health and wearable devices are generally used in ambulatory settings, hence vulnerable to noise. This interferes with the accuracy of Machine Learning (ML) models running on such systems and their decision-making procedures. To address this issue, we first need to identify the presence of noise. In this paper, we propose RecogNoise to detect noisy segments in Electrocardiography (ECG) recordings using heartbeat detection algorithms and ML. We evaluate our approach based on the MIT-BIH arrhythmia database and three types of noise, i.e., Electrode Motion (EM) , Baseline Wander (BW), and Muscle Artifact (MA), with different Signal to Noise Ratios (SNRs). We show that RecogNoise can detect noisy segments with an F1-score of 86.9% and an accuracy of 88.3%.

BibTeX

@inproceedings{aminifar2024recognoise,
  title={Recognoise: Machine-learning-based recognition of noisy segments in electrocardiogram signals},
  author={Aminifar, Amin and Khooyooz, Soheil and Jahanjoo, Anice and Shakibhamedan, Salar and TaheriNejad, Nima},
  booktitle={2024 IEEE International Symposium on Circuits and Systems (ISCAS)},
  pages={1--5},
  year={2024},
  organization={IEEE}
}