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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.