This work aimed to develop an algorithm for the automatic identification of movement and muscle artifacts in ECG recordings based on a set of basic statistical features and the Higuchi fractal dimension (HFD). The use of a threshold for variance above 0.0225 and an HFD in the range from 1.11 to 1.26 allowed for the classification of one-second ECG segments with and without artifacts with sensitivity, specificity, precision, and negative predictive value of 66.3%, 99.8%, 75.2%, and 99.7, respectively. The accuracy of the Movesense device is very high (99.5%). The proposed method can be helpful in eliminating a significant number of artifacts in ECG signals monitored by Movesense.

Automatic identification of movement and muscle artifacts in ECG based on statistical and nonlinear measures

Massaroni C.
2024-01-01

Abstract

This work aimed to develop an algorithm for the automatic identification of movement and muscle artifacts in ECG recordings based on a set of basic statistical features and the Higuchi fractal dimension (HFD). The use of a threshold for variance above 0.0225 and an HFD in the range from 1.11 to 1.26 allowed for the classification of one-second ECG segments with and without artifacts with sensitivity, specificity, precision, and negative predictive value of 66.3%, 99.8%, 75.2%, and 99.7, respectively. The accuracy of the Movesense device is very high (99.5%). The proposed method can be helpful in eliminating a significant number of artifacts in ECG signals monitored by Movesense.
2024
electrocardiogram (ECG); Higuchi fractal dimension; muscle and movement artifacts; statistical and nonlinear measures
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12610/82320
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