Detection and removal of pacing artifacts prior to automated analysis of 12-lead ECG

Haq, Kazi T.; Javadekar, Neeraj; Tereshchenko, Larisa G.

doi:10.1016/j.compbiomed.2021.104396

Cited by 4 publications

(3 citation statements)

References 25 publications

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“…In the literature, algorithm accuracy for automatic classification of bioelectric signals is higher than the one in our study when this detection is related to critical conditions in the patient’s life. The detection of heart failure, pacing artifacts, or noise with electrocardiography (ECG) is performed with algorithms whose accuracy ranges from 97% to 100% [ 26 , 27 , 28 ]. Conversely, algorithms used in non-critical applications have an accuracy similar to that of the algorithm analyzed in this study.…”

Section: Discussionmentioning

confidence: 99%

Automatic Identification of Involuntary Muscle Activity in Subacute Patients with Upper Motor Neuron Lesion at Rest—A Validation Study

Merlo

Campanini

2023

Sensors

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Sustained involuntary muscle activity (IMA) is a highly disabling phenomenon that arises in the acute phase of an upper motor neuron lesion (UMNL). Wearable probes for long-lasting surface EMG (sEMG) recordings have been recently recommended to detect IMA insurgence and to quantify its evolution over time, in conjunction with a complex algorithm for IMA automatic identification and classification. In this study, we computed sensitivity (Se), specificity (Sp), and overall accuracy (Acc) of this algorithm by comparing it with the classification provided by two expert assessors. Based on sample size estimation, 6020 10 s-long sEMG epochs were classified by both the algorithm and the assessors. Epochs were randomly extracted from long-lasting sEMG signals collected in-field from 14 biceps brachii (BB) muscles of 10 patients (5F, age range 50–71 years) hospitalized in an acute rehabilitation ward following a stroke or a post-anoxic coma and complete upper limb (UL) paralysis. Among the 14 BB muscles assessed, Se was 85.6% (83.6–87.4%); Sp was 89.7% (88.6–90.7%), and overall Acc was 88.5% (87.6–89.4%) and ranged between 78.6% and 98.7%. The presence of IMA was detected correctly in all patients. These results support the algorithm’s use for in-field IMA assessment based on data acquired with wearable sensors. The assessment and monitoring of IMA in acute and subacute patients with UMNL could improve the quality of care needed by triggering early treatments to lessen long-term complications.

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Section: Discussionmentioning

confidence: 99%

Automatic Identification of Involuntary Muscle Activity in Subacute Patients with Upper Motor Neuron Lesion at Rest—A Validation Study

Merlo

Campanini

2023

Sensors

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“…A custom MATLAB script was used to construct frontal plane VCGs, as previously described ( Haq et al, 2021a ; Haq et al, 2021b ; Haq et al, 2021c ), with lead I as the x-axis and aVF as the y -axis. First, 10 consecutive beats were aligned at their corresponding R-peaks to generate an average waveform.…”

Section: Methodsmentioning

confidence: 99%

Demographic and Methodological Heterogeneity in Electrocardiogram Signals From Guinea Pigs

Haq

Cooper

Berk³

et al. 2022

Front. Physiol.

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Electrocardiograms (ECG) are universally used to measure the electrical activity of the heart; however, variations in recording techniques and/or subject demographics can affect ECG interpretation. In this study, we investigated variables that are likely to influence ECG metric measurements in cardiovascular research, including recording technique, use of anesthesia, and animal model characteristics. Awake limb lead ECG recordings were collected in vivo from adult guinea pigs using a platform ECG system, while recordings in anesthetized animals were performed using both a platform and needle ECG system. We report significant heterogeneities in ECG metric values that are attributed to methodological differences (e.g., ECG lead configuration, ECG recording platform, presence or absence of anesthesia) that persist even within the same cohort of animals. Further, we report that variability in animal demographics is preserved in vivo ECG recordings—with animal age serving as a significant contributor, while sex-specific influences were less pronounced. Methodological approaches and subject demographics should be fully considered when interpreting ECG values in animal models, comparing datasets between studies, or developing artificial intelligence algorithms that utilize an ECG database.

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“…Pacing artifacts were removed by a semi-automated algorithm, updated from the one described in [7], consisting of the following steps: 1. Orthogonal leads XYZ were obtained from the standard 12-lead ECG signal by using the Kors transformation matrix [8].…”

Section: Preprocessingmentioning

confidence: 99%

Spatial Dispersion of Activation and Repolarization Times Associated with Different Cardiac Pacing Modes

Palacios,

Smisek,

Curila

et al. 2023

2023 Computing in Cardiology Conference (CinC)

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Detection and removal of pacing artifacts prior to automated analysis of 12-lead ECG

Cited by 4 publications

References 25 publications

Automatic Identification of Involuntary Muscle Activity in Subacute Patients with Upper Motor Neuron Lesion at Rest—A Validation Study

Automatic Identification of Involuntary Muscle Activity in Subacute Patients with Upper Motor Neuron Lesion at Rest—A Validation Study

Demographic and Methodological Heterogeneity in Electrocardiogram Signals From Guinea Pigs

Spatial Dispersion of Activation and Repolarization Times Associated with Different Cardiac Pacing Modes

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