Understanding Electrosurgical Unit Perturbations in Order to Address Hospital Operating Room Electromagnetic Compatibility

Fernández-Chimeno, Mireya; Quílez, Marcos; Silva, F.

doi:10.1109/tbme.2006.873389

Cited by 7 publications

(5 citation statements)

References 9 publications

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“…This causes muscular twitching. They are exceedingly difficult to deal with because of their large frequency range, the frequency of which corresponds with the frequency of the ECG signal [18].…”

Section: Methodsmentioning

confidence: 99%

A Deep Learning Approach to ECG Classification for Atrial Fibrillation, Normal Sinus Rhythm, and Congestive Heart Failure.

Sadigale,

Nale,

Kamble

et al. 2023

Preprint

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Deep learning approaches for medical signal processing have received a lot of interest in recent years, especially in the field of electrocardiogram (ECG) categorization. This study investigates the effectiveness of using the AlexNet architecture, a well-known convolutional neural network (CNN) model, for reliable ECG data categorization. Our suggested model has attained an accuracy of 99.21% after extensive experimentation and validation, indicating its ability to distinguish various cardiac anomalies. The research includes the creation and refinement of a unique AlexNet-based CNN model adapted to the difficulties of ECG data. Using AlexNet's deep hierarchical features, our model outperforms in capturing complicated patterns and minor fluctuations within ECG data.

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Section: Methodsmentioning

confidence: 99%

A Deep Learning Approach to ECG Classification for Atrial Fibrillation, Normal Sinus Rhythm, and Congestive Heart Failure.

Sadigale,

Nale,

Kamble

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…For a better characterization of the electrosurgical device, a test setup was adopted, similar to that in [5], however, the measurement distance was not considered fixed, because in real conditions, in the operating room, the patient, the medical personnel operating and other susceptible medical devices are in close proximity to the electrosurgical device, and our aim was to determine the electromagnetic emissions due to electrosurgery at various distances. The height of the table was the same as in the preliminary tests, but the point of measurement was chosen from the centre of the metal plate on which the arching was made and for all measurements presented in this paper, a monopolar electrical scalpel was used.…”

Section: Methodsmentioning

confidence: 99%

“…The use of electrosurgery can lead to electromagnetic interference of medical devices [3], [4] and an understanding of the electromagnetic fields emitted by these devices when used is very important [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Electromagnetic emissions due to electrosurgery

Dafinescu

David

Tutuianu

2012

2012 International Conference and Exposition on Electrical and Power Engineering

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Electrosurgery devices are an important part of surgical interventions in the modern medical service; however the benefits of using such technology are accompanied by side effects like electromagnetic pollution of the hospital environment. As a consequence, the maximum admitted levels of electric and magnetic fields for general public and occupational exposure could be exceeded in some cases and the risk of electromagnetic interference with medical devices rises while implementing electrosurgery in the operating room. In this paper we address the characterization of the electromagnetic emissions from an electrosurgical device at different distances and determine the maximum field levels for different operating modes of the device with a monopolar scalpel. Also, the surgeon's exposure while performing electrosurgery was determined to be much higher than occupational exposure limits imposed by international standards.

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“…This also creates muscle disturbances. Due to broad spectrum of frequencies it is very difficult to deal with the frequencies and overlaps with the ECG signal frequency [3]. Interference of electromagnetic nature is often known as power line interferences [4].…”

Section: Introductionmentioning

confidence: 99%

“…High power devices may cause some disturbances such as e.g., diathermy. These noises are of impulse nature [5][6]. By taking into consideration all causes of interferences affecting ECG signals, their list includes: 1) power line interference, 2) electrode contact noise, 3) motion artefacts, 4) EMG noise, 5) instrumentation noise etc.…”

Section: Introductionmentioning

confidence: 99%

Baseline Wander Correction and Impulse Noise Suppression Using Cascaded Empirical Mode Decomposition and Improved Morphological Algorithm

Das¹,

Halder²

2017

International Journal of Applied Engineering Research

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When the cardiac impulse passes through the heart, electrical current also spreads from the heart into the adjacent tissues surrounding the heart. A small portion of the current spreads all the way to the surface of the body. If electrodes are placed on the skin on opposite sides of the heart, electrical potentials generated by the current can be recorded; the recording is known as an electrocardiogram. It is used for diagnosis of a number of cardiac diseases and disorders. But the ECG signals are often corrupted by noises and artefacts. For getting the proper information from ECG, it first must be de-noised. ECG signal is dominated mainly by high frequency impulse noises and baseline wander. These noise must be removed for better clinical assessment. In this paper, we propose an improve ECG intensification method based on empirical mode decomposition and improved morphological algorithm. The method is examined through experimental simulations on MIT-BIH databases. Quantitative and qualitative results show that the proposed EMD based method caters improved results for de-noising and baseline wander removal.

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Understanding Electrosurgical Unit Perturbations in Order to Address Hospital Operating Room Electromagnetic Compatibility

Cited by 7 publications

References 9 publications

A Deep Learning Approach to ECG Classification for Atrial Fibrillation, Normal Sinus Rhythm, and Congestive Heart Failure.

A Deep Learning Approach to ECG Classification for Atrial Fibrillation, Normal Sinus Rhythm, and Congestive Heart Failure.

Electromagnetic emissions due to electrosurgery

Baseline Wander Correction and Impulse Noise Suppression Using Cascaded Empirical Mode Decomposition and Improved Morphological Algorithm

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