Removal of Electrocardiogram Artifacts From Local Field Potentials Recorded by Sensing-Enabled Neurostimulator

Sensing-enabled neurostimulators are an advanced technology for chronic observation of brain activities, and show great potential for closed-loop neuromodulation and as implantable brain-computer interfaces. However, local field potentials (LFPs) recorded by sensing-enabled neurostimulators can be contaminated by electrocardiogram (ECG) signals due to complex recording conditions and limited common-mode-rejection-ratio (CMRR). In this study, we propose a solution for removing such ECG artifacts from local field potentials (LFPs) recorded by a sensing-enabled neurostimulator. A synchronized monopolar channel was added as an ECG reference, and two pre-existing methods, i.e., template subtraction and adaptive filtering, were then applied. ECG artifacts were successfully removed and the performance of the method was insensitive to residual stimulation artifacts. This approach to removal of ECG artifacts broadens the range of applications of sensing-enabled neurostimulators.

Artifacts in electrocardiogram interpreted as cardiac arrhythmias: Reports of clinical cases

Background: Artifact waves in the ECG and Holter recording are not rare in clinical practice and can be mistaken for tachyarrhythmia. Objective: To orient the practicing physicians to differentiate these artifacts from cardiac arrhythmias. Patients and Methods: Thirteen patients with incorrectly diagnosed cardiac arrhythmias by ECG or Holter recording then distinguished to be ECG artifacts were included in this study. The patients were collected from the author’s private practice in the northern Iraqi governorate of Sulaimanya during the period from June 2015 to August 2020. The differentiation of the artifact waves from the arrhythmias were made by careful inspection of the ECG, identification of the R waves within the artifact waves and correlating the artifact waves with the patient’s symptoms. Results: The artifacts were mistaken for ventricular fibrillation in two patients, ventricular tachycardia in four, atrial fibrillation in two, atrial flutter in four, and in one patient bradycardia of high grade atrio-ventricular block. Conclusion: Distinguishing artifact in ECG and differentiating them from cardiac arrhythmia is important to avoid mismanagement.

Techniques to Manage ECG Artifacts When Working With Surface EMG

Surface electromyographic (SEMG) amplitude signals can often contain rhythmic spikes due to cardiogenic electrical activity. The author discusses the impact of this activity on SEMG biofeedback training and techniques to reduce the problems caused by cardiogenic electrical activity. In particular, changing the low frequency cutoff of the digital filter settings to reduce cardiogenic electrical activity is recommended as a procedure to improve the fidelity of SEMG amplitude signals.

Pseudoatrial Flutter: When the Problem Lies Outside the Heart

Electrocardiogram (ECG) artifacts are a common problem in emergency medicine. Generally these artifacts are induced by movement disorders, which generate electrical interference with the ECG recording. If these disorders are not promptly recognized, consequences can lead to hospitalization and execution of unnecessary diagnostic tests, thereby increasing the costs and clinical risks such as nosocomial infections and thromboembolism. We present a pseudoatrial flutter generated by a Parkinson’s-like movement.

Removal of ECG artifacts from EMG signals with different artifact magnitudes by template subtraction

This work investigates the performance of eventsynchronous noise cancelling algorithms to separate ECG artifacts from thoracic EMG recordings. In focus are the precise detection of heart beats, the exact time alignment of the QRS segments and the construction of a template QRS signal. Among the utilized methods is a modified structural intensity (SI) approach based on the observation of extrema in low pass filtered versions of the second derivative as a detector. An adoption of this approach is also used to obtain the exact time alignment of QRS segments. Artificial test signals are constructed based on addition of ECG and sEMG with different magnitudes. We show that approaches based on the observation of extrema in different scales yield superior results.

Multi-Scale Heart Beat Entropy Measures for Mental Workload Assessment of Ambulant Users

Mental workload assessment is crucial in many real life applications which require constant attention and where imbalance of mental workload resources may cause safety hazards. As such, mental workload and its relationship with heart rate variability (HRV) have been well studied in the literature. However, the majority of the developed models have assumed individuals are not ambulant, thus bypassing the issue of movement-related electrocardiography (ECG) artifacts and changing heart beat dynamics due to physical activity. In this work, multi-scale features for mental workload assessment of ambulatory users is explored. ECG data was sampled from users while they performed different types and levels of physical activity while performing the multi-attribute test battery (MATB-II) task at varying difficulty levels. Proposed features are shown to outperform benchmark ones and further exhibit complementarity when used in combination. Indeed, results show gains over the benchmark HRV measures of 24 . 41 % in accuracy and of 27 . 97 % in F1 score can be achieved even at high activity levels.