Design and pilot testing of a 26‐gauge impedance‐electromyography needle in wild‐type and ALS mice

Rutkove, Seward B.; Le, Mai Quynh; Ruehr, Sophie A; Nagy, Janice A.; Semple, Carson; Sanchez, Benjamin

doi:10.1002/mus.27551

Cited by 6 publications

(9 citation statements)

References 16 publications

(23 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As we described previously, 20 in this device, only the distal most rim of the needle along the tip serves as the E2 electrode here. This much smaller recording area near the concentric inner electrode may impact the EMG data itself.…”

Section: Discussionmentioning

confidence: 99%

“…While this iEMG needle has E1 at the tip and E2 is the barrel around it, it is not a typical concentric needle in which the entire barrel serves as a reference electrode. As we described previously, 20 in this device, only the distal most rim of the needle along the tip serves as the E2 electrode here. This much smaller recording area near the concentric inner electrode may impact the EMG data itself.…”

Section: Discussionmentioning

confidence: 99%

“…22 To facilitate the iEMG needle insertion, a small hole was then placed in the skin using a 21-gauge angiocath needle just proximal to the left knee. A prototype iEMG needle 20 (Haystack Diagnostics, Inc, Boston, MA, USA) was then passed through the hole in the skin and into the center of quadriceps with an effort to keep the needle parallel to the long axis of the muscle so that all six electrodes remained within the muscle group itself. Impedance measurements were then made with the mView impedance system (Myolex, Inc, Boston, MA, USA) interfaced to an iEMG needle connector.…”

Section: Animal Proceduresmentioning

confidence: 99%

“…18 Recently, EIM technology has been combined into a concentric needle EMG to create a single "impedance-EMG" or "iEMG" device to allow concurrent collection of both signals and improve diagnostic outcomes. 19,20 In this study, we sought to assess the potential of ML-based iEMG for discriminating healthy muscle from dystrophic muscle by studying a mouse model of Duchenne muscular dystrophy, the D2-mdx mouse. Our goal was to present an approach for doing so to establish proof-of-principle for use in future more challenging classification problems.…”

Section: Introductionmentioning

confidence: 99%

“…And like EMG, ML has been employed in EIM as another approach to data interpretation, improving its capability in disease discrimination in mouse models beyond that obtainable with just single frequency measurements 18 . Recently, EIM technology has been combined into a concentric needle EMG to create a single “impedance‐EMG” or “iEMG” device to allow concurrent collection of both signals and improve diagnostic outcomes 19,20 …”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Combining electromyographic and electrical impedance data sets through machine learning: A study in D2‐mdx and wild‐type mice

Pandeya,

Sanchez,

Nagy

et al. 2023

Muscle and Nerve

Self Cite

View full text Add to dashboard Cite

Introduction/AimsNeedle impedance‐electromyography (iEMG) assesses the active and passive electrical properties of muscles concurrently by using a novel needle with six electrodes, two for EMG and four for electrical impedance myography (EIM). Here, we assessed an approach for combining multifrequency EMG and EIM data via machine learning (ML) to discriminate D2‐mdx muscular dystrophy and wild‐type (WT) mouse skeletal muscle.MethodsiEMG data were obtained from quadriceps of D2‐mdx mice, a muscular dystrophy model, and WT animals. EIM data were collected with the animals under deep anesthesia and EMG data collected under light anesthesia, allowing for limited spontaneous movement. Fourier transformation was performed on the EMG data to provide power spectra that were sampled across the frequency range using three different approaches. Random forest‐based, nested ML was applied to the EIM and EMG data sets separately and then together to assess healthy versus disease category classification using a nested cross‐validation procedure.ResultsData from 20 D2‐mdx and 20 WT limbs were analyzed. EIM data fared better than EMG data in differentiating healthy from disease mice with 93.1% versus 75.6% accuracy, respectively. Combining EIM and EMG data sets yielded similar performance as EIM data alone with 92.2% accuracy.DiscussionWe have demonstrated an ML‐based approach for combining EIM and EMG data obtained with an iEMG needle. While EIM‐EMG in combination fared no better than EIM alone with this data set, the approach used here demonstrates a novel method of combining the two techniques to characterize the full electrical properties of skeletal muscle.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Animal Proceduresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Combining electromyographic and electrical impedance data sets through machine learning: A study in D2‐mdx and wild‐type mice

Pandeya,

Sanchez,

Nagy

et al. 2023

Muscle and Nerve

Self Cite

View full text Add to dashboard Cite

show abstract

Electrical impedance myography in healthy volunteers

Offit,

Mohammad Khanli,

et al. 2023

Muscle and Nerve

View full text Add to dashboard Cite

Introduction/AimsElectrical impedance myography (EIM) is a noninvasive technique being used in clinical studies to characterize muscle by phase, reactance, and resistance after application of a low‐intensity current. The aim of this study was to obtain 50‐kHz EIM data from healthy volunteers (HVs) for use in future clinical and research studies, perform reliability tests on EIM outcome measures, and compare findings with muscle ultrasound variables.MethodsFour arm and four leg muscles of HVs were evaluated using an EIM device with two sensors, P/N 20‐0045 and P/N 014‐009. Muscles were evaluated individually and eight‐muscle average (8MU), four‐muscle upper extremity average, and four‐muscle lower extremity average. An intraclass correlation coefficient (ICC) was applied to assess interrater, intrarater, and intersensor reliability using a subset of HVs. Ultrasound studies on muscle thickness and elastography were also performed on a subset of HVs.ResultsFor the P/N 20‐0045 sensor, the 8MU EIM mean and standard deviation (n = 41) was 14.54 ± 3.31 for phase, 7.04 ± 1.22 for reactance, and 28.91 ± 7.63 for resistance. Reliability for 8MU phase (n = 22) was good to excellent for both interrater (n = 22, ICC = 0.920, 95% CI 0.820 to 0.966) and intrarater (n = 22, ICC = 0.950, 95% CI 0.778 to 0.983). The P/N 014‐009 sensor had similar reliability findings. Correlation analyses showed no association between EIM and muscle thickness.DiscussionEIM is a reproducible measure of muscle physiology. Obtaining EIM values from HVs allows us to gain a better understanding how EIM may be altered in diseased muscle.

show abstract

Neurophysiological and imaging biomarkers of lower motor neuron dysfunction in motor neuron diseases/amyotrophic lateral sclerosis: IFCN handbook chapter

Shin-Yi Lin,

Howells,

Rutkove

et al. 2024

Clinical Neurophysiology

View full text Add to dashboard Cite

Design and pilot testing of a 26‐gauge impedance‐electromyography needle in wild‐type and ALS mice

Cited by 6 publications

References 16 publications

Combining electromyographic and electrical impedance data sets through machine learning: A study in D2‐mdx and wild‐type mice

Combining electromyographic and electrical impedance data sets through machine learning: A study in D2‐mdx and wild‐type mice

Electrical impedance myography in healthy volunteers

Neurophysiological and imaging biomarkers of lower motor neuron dysfunction in motor neuron diseases/amyotrophic lateral sclerosis: IFCN handbook chapter

Contact Info

Product

Resources

About