Multiresolution segmentation of respiratory electromyographic signals

Choi, Haan-Go; Prı́ncipe, José C.; Hutchison, Alastair A.; Wozniak, J. A.

doi:10.1109/10.284944

Cited by 10 publications

(3 citation statements)

References 10 publications

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“…This method was first used in the context of EMG onset detection by Maple-Horvart and Gilbey in 1992 [ 27 ]. After that, MA was applied to calculate EMG envelopes for EMG onset detection in several other papers: [ 13 , 28 – 41 ].…”

Section: Literature Review Resultsmentioning

confidence: 99%

“…The MA is calculated with a series of averages from successive segments, with or without overlapping windows. The consequence of its use is the attenuation of rapid variations through local averaging, but retention of slow variations [ 28 ], smoothing the signal and acquiring its envelope.…”

Section: Literature Review Resultsmentioning

confidence: 99%

“…Root-mean-square (RMS) on raw EMG signal This approach ([ 28 , 33 , 42 – 51 ]) computes the RMS value of the signal within a window that “moves” across the raw EMG signal.…”

Section: Literature Review Resultsmentioning

confidence: 99%

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Review of electromyography onset detection methods for real-time control of robotic exoskeletons

Carvalho,

Fernández,

del-Ama

et al. 2023

J NeuroEngineering Rehabil

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Background Electromyography (EMG) is a classical technique used to record electrical activity associated with muscle contraction and is widely applied in Biomechanics, Biomedical Engineering, Neuroscience and Rehabilitation Robotics. Determining muscle activation onset timing, which can be used to infer movement intention and trigger prostheses and robotic exoskeletons, is still a big challenge. The main goal of this paper was to perform a review of the state-of-the-art of EMG onset detection methods. Moreover, we compared the performance of the most commonly used methods on experimental EMG data. Methods A total of 156 papers published until March 2022 were included in the review. The papers were analyzed in terms of application domain, pre-processing method and EMG onset detection method. The three most commonly used methods [Single (ST), Double (DT) and Adaptive Threshold (AT)] were applied offline on experimental intramuscular and surface EMG signals obtained during contractions of ankle and knee joint muscles. Results Threshold-based methods are still the most commonly used to detect EMG onset. Compared to ST and AT, DT required more processing time and, therefore, increased onset timing detection, when applied on experimental data. The accuracy of these three methods was high (maximum error detection rate of 7.3%), demonstrating their ability to automatically detect the onset of muscle activity. Recently, other studies have tested different methods (especially Machine Learning based) to determine muscle activation onset offline, reporting promising results. Conclusions This study organized and classified the existing EMG onset detection methods to create consensus towards a possible standardized method for EMG onset detection, which would also allow more reproducibility across studies. The three most commonly used methods (ST, DT and AT) proved to be accurate, while ST and AT were faster in terms of EMG onset detection time, especially when applied on intramuscular EMG data. These are important features towards movement intention identification, especially in real-time applications. Machine Learning methods have received increased attention as an alternative to detect muscle activation onset. However, although several methods have shown their capability offline, more research is required to address their full potential towards real-time applications, namely to infer movement intention.

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Section: Literature Review Resultsmentioning

confidence: 99%

Section: Literature Review Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Review of electromyography onset detection methods for real-time control of robotic exoskeletons

Carvalho,

Fernández,

del-Ama

et al. 2023

J NeuroEngineering Rehabil

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Detection and analysis of gastrointestinal sounds in normal and small bowel obstructed rats

Mansy

Sandler²

2000

Med. Biol. Eng. Comput.

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This study is aimed at detecting gastrointestinal sounds (GIS) and correlating their characteristics with gastrointestinal (GI) conditions. The central hypotheses are that GIS generation depends on the motility patterns and the mechanical properties of the gut, and that changes in those result in measurable differences in GIS. An animal model which included both healthy rats and those with small bowel obstruction (SBO) was developed. The acoustic bursts of GIS were detected by amplitude thresholding the signal envelope. Three methods of envelope estimation were proposed and evaluated. Envelope estimation using a Hilbert transform was found to produce the best results in the current application. The duration and dominant frequency of each detected GIS event was estimated and clear differences between healthy and diseased rats were discovered. In the control state, GIS events were found to consistently be of relatively short duration (3-65 ms). Although the majority of events in the SBO state had similar short duration, infrequent longer events were also detected and appeared to be pathognomonic. Long duration events (> 100 ms) occurred in each of seven obstructed, but in none of 14 non-obstructed, cases (p < 0.001). It is concluded that GIS analysis may prove useful in the non-invasive, rapid, and accurate diagnosis of SBO.

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A randomisation method for discriminating between signal and noise in recordings of rhythmic electromyographic activity

Thexton

1996

Journal of Neuroscience Methods

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Multiresolution segmentation of respiratory electromyographic signals

Cited by 10 publications

References 10 publications

Review of electromyography onset detection methods for real-time control of robotic exoskeletons

Review of electromyography onset detection methods for real-time control of robotic exoskeletons

Detection and analysis of gastrointestinal sounds in normal and small bowel obstructed rats

A randomisation method for discriminating between signal and noise in recordings of rhythmic electromyographic activity

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