Electrodiagnostic Testing for the Diagnosis and Management of Amyotrophic Lateral Sclerosis

Howard, Ileana M.; Rad, Nassim

doi:10.1016/j.pmr.2018.06.003

Cited by 5 publications

(3 citation statements)

References 40 publications

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“…The EMG signals were recorded by inserting a fine wire electrode inside the biceps brachii (long head& court head) from five places in the muscle at three levels of insertion (deep, medium, low). A standard concentric needle electrode offers better selectivity of motor unit action potentials with a low spatial gradient [7]. The complete data consists of two classes, amyotrophic lateral [3,4,18].…”

Section: Data Acquisitionmentioning

confidence: 99%

“…There are various well-established clinical examinations methods for neuromuscular disorders, the frequently used are genetic and electromyography (EMG) tests, which can practically help in an early diagnostic of ALS disease [6,7]. In this context, electromyography has been considered to be the best clinical Bulletin of Electr Eng & Inf ISSN: 2302-9285 …”

Section: Introductionmentioning

confidence: 99%

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A comparative study of wavelet families for electromyography signal classification based on discrete wavelet transform

Achmamad

Jbari

2020

Bulletin EEI

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Automatic detection of neuromuscular disorders performed using electromyography (EMG) has become an interesting domain for many researchers. In this paper, we present an approach to evaluate and classify the non-stationary EMG signals based on discrete wavelet transform (DWT). Most often researches did not consider the effect of DWT factors on the performance of EMG signals classification. This problem is still an interesting unsolved challenge. However, the selection of appropriate mother wavelet and related level decomposition is an essential issue that should be addressed in DWT-based EMG signals classification. The proposed method consists of decomposing a raw EMG signal into different sub-bands. Several statistical features were extracted from each sub-band and six wavelet families were investigated. The feature vector was used as inputs to support vector machine (SVM) classifier for the diagnosis of neuromuscular disorders. The obtained results achieve satisfactory performances with optimal DWT factors using 10-fold cross-validation. From the classification performances, it was found that sym14 is the most suitable mother wavelet at the 8th optimal wavelet level of decomposition. These simulation results demonstrated that the proposed method is very reliable for reducing cost computational time of automated neuromuscular disorders system and removing the redundancy information.

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Section: Data Acquisitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A comparative study of wavelet families for electromyography signal classification based on discrete wavelet transform

Achmamad

Jbari

2020

Bulletin EEI

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“…Electrodes are used to stimulate and record electrical activity from the mammalian central nervous system and have been widely applied in the medical field for the investigation of spinal cord injuries, strokes, sensory deficits, and neurological disorders. Neuromodulation, an evolving therapeutic approach based on the recording and delivery of electrical signals to targeted neurological sites, has been investigated for the treatment of conditions [1][2][3][4][5][6][7][8] such as Parkinson's disease [9,10], dystonia, [11,12], tremor [13], psychiatric conditions [14], and pain disorders [15][16][17][18]. Most neural recording and stimulating electrodes are metallic [19].…”

Section: Introductionmentioning

confidence: 99%

Development and Characterization of Novel Conductive Sensing Fibers for In Vivo Nerve Stimulation

Richter

Mace

Hays

et al. 2021

Sensors

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Advancements in electrode technologies to both stimulate and record the central nervous system’s electrical activities are enabling significant improvements in both the understanding and treatment of different neurological diseases. However, the current neural recording and stimulating electrodes are metallic, requiring invasive and damaging methods to interface with neural tissue. These electrodes may also degrade, resulting in additional invasive procedures. Furthermore, metal electrodes may cause nerve damage due to their inherent rigidity. This paper demonstrates that novel electrically conductive organic fibers (ECFs) can be used for direct nerve stimulation. The ECFs were prepared using a standard polyester material as the structural base, with a carbon nanotube ink applied to the surface as the electrical conductor. We report on three experiments: the first one to characterize the conductive properties of the ECFs; the second one to investigate the fiber cytotoxic properties in vitro; and the third one to demonstrate the utility of the ECF for direct nerve stimulation in an in vivo rodent model.

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Fast automatic detection of amyotrophic lateral sclerosis disease based on Euclidean distance metric

Achmamad

Belkhou

Jbari

2020

2020 International Conference on Electrical and Information Technologies (ICEIT)

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Electrodiagnostic Testing for the Diagnosis and Management of Amyotrophic Lateral Sclerosis

Cited by 5 publications

References 40 publications

A comparative study of wavelet families for electromyography signal classification based on discrete wavelet transform

A comparative study of wavelet families for electromyography signal classification based on discrete wavelet transform

Development and Characterization of Novel Conductive Sensing Fibers for In Vivo Nerve Stimulation

Fast automatic detection of amyotrophic lateral sclerosis disease based on Euclidean distance metric

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