A C++ software environment for the development of embedded signal processing systems

Winograd, Joseph M.; Nawab, S. Hamid

doi:10.1109/icassp.1995.480122

Cited by 16 publications

(5 citation statements)

References 3 publications

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“…We have previously utilized the PD approach in conjunction with the Integrated Processing and Understanding (IPUS) concept (Lesser et al 1995) from artificial intelligence to develop a decomposition algorithm for indwelling EMG signals (Nawab et al 2002, Nawab et al 2004, and Nawab et al 2006). The IPUS framework basically allows rules to be conveniently encoded (see Winograd and Nawab (1995) for details) in support of the mathematical structure of an algorithm in order to permit run-time modification of its behavior in response to different conditions found in the input signal. For example, in the context of the PD approach, the IPUS rules help to decide on a signal-by-signal basis what amplitude threshold to use in detecting action potentials so that their shapes can be resolved sufficiently for distinguishing them from action potentials of other MUAPTs.…”

Section: Methodsmentioning

confidence: 99%

High-yield decomposition of surface EMG signals

Nawab

Chang

Luca

2010

Clinical Neurophysiology

304

355

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Objective-Automatic decomposition of surface Electromyographic (sEMG) signals into their constituent motor unit action potential trains (MUAPTs).Methods-A small five-pin sensor provides four channels of sEMG signals that are in turn processed by an enhanced artificial intelligence algorithm evolved from a previous proof-ofprinciple. We tested the technology on sEMG signals from five muscles contracting isometrically at force levels ranging up to 100% of their maximal level, including those that were covered with more than 1.5 cm of adipose tissue. Decomposition accuracy was measured by a new method wherein a signal is first decomposed and then reconstructed and the accuracy is measured by comparison. Results were confirmed by the more established two-source method.Results-The number of MUAPTs decomposed varied among muscles and force levels and mostly ranged from 20 to 30, with a maximum of 40. The accuracy of all the firings of the MUAPTs was on average 92.5%, at times reaching 97%.Conclusion-Reported technology can reliably perform high-yield decomposition of sEMG signals for isometric contractions up to maximal force levels.Significance-The small sensor size and the high yield and accuracy of the decomposition should render this technology useful for motor control studies and clinical investigations.

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Section: Methodsmentioning

confidence: 99%

High-yield decomposition of surface EMG signals

Nawab

Chang

Luca

2010

Clinical Neurophysiology

304

355

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“…The new algorithms are based on our own Artificial Intelligence knowledge-based approach specifically designed to manage signalprocessing algorithms that perform two main categories of functions: 1) they identify differences in shapes and track changes in the shapes of the action potentials under a variety of conditions and 2) they resolve complex superpositions. The algorithms have been described in publications by Nawab and Lesser (1992), Winograd and Nawab (1995), Hochstein et al (2002), and Nawab et al (2004a,b). We succeeded in automatically decomposing iEMG signals from the quadrifilar needle sensor (De Luca et al 1982a) and later with quadrifilar wire sensors (De Luca and Adam 1999) with typically 10 MU action potential trains with an accuracy of typically 85% with a processing time eightfold that of the acquisition time.…”

Section: B a C K G R O U N Dmentioning

confidence: 97%

“…The algorithms in both PD II and PD III are designed to address the difficulties inherent in trying to separate overlapping action potentials at various points in the EMG signal by using an Artificial Intelligence framework called IPUS ("Integrated Processing and Understanding of Signals") Lesser et al 1995;Nawab and Lesser 1992;Winograd and Nawab 1995). Although other signal-processing approaches have also been investigated for performing EMG decompositions, the IPUS approach has provided significantly more accurate results on intramuscular EMG data (Nawab et al 2004a,b).…”

Section: Algorithms For Decomposing Semg Signalmentioning

confidence: 99%

Decomposition of Surface EMG Signals

Luca¹,

Adam²,

Wotiz³

et al. 2006

Journal of Neurophysiology

405

340

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This report describes an early version of a technique for decomposing surface electromyographic (sEMG) signals into the constituent motor unit (MU) action potential trains. A surface sensor array is used to collect four channels of differentially amplified EMG signals. The decomposition is achieved by a set of algorithms that uses a specially developed knowledge-based Artificial Intelligence framework. In the automatic mode the accuracy ranges from 75 to 91%. An Interactive Editor is used to increase the accuracy to > 97% in signal epochs of about 30-s duration. The accuracy was verified by comparing the firings of action potentials from the EMG signals detected simultaneously by the surface sensor array and by a needle sensor. We have decomposed up to six MU action potential trains from the sEMG signal detected from the orbicularis oculi, platysma, and tibialis anterior muscles. However, the yield is generally low, with typically < or = 5 MUs per contraction. Both the accuracy and the yield should increase as the algorithms are developed further. With this technique it is possible to investigate the behavior of MUs in muscles that are not easily studied by needle sensors. We found that the inverse relationship between the recruitment threshold and the firing rate previously reported for muscles innervated by spinal nerves is also present in the orbicularis oculi and the platysma, which are innervated by cranial nerves. However, these two muscles were found to have greater and more widespread values of firing rates than those of large limb muscles.

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“…This software, based on an original design [5], offers a unique combination of features with respect to currently available design tools such as Matlab [6]. The environment presents a unified platform in which embedded signal processing applications which require sophisticated rule-based control can be designed, prototyped, tested, and implemented.…”

Section: The Softwarementioning

confidence: 99%

What is IPUS and how does it help resolve biosignal complexity?

Nawab

Cole

2011

2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Integrated Processing and Understanding of Signals (IPUS) combines signal processing and artificial intelligence approaches to develop algorithms for resolving signal complexity. It has also led to development over the last decade and a half of software tools for supporting the algorithm design process. The signals to be analyzed are the superposition of temporally localized and temporally overlapping signal components from broadly defined signal classes pertinent to the given application. Resolving a signal's complexity thus amounts to "decoding" it to reveal details of the specific signal components that are present at each point of a dense temporal grid defined on the signal. IPUS uses artificial intelligence techniques such as rule-based inference in conjunction with parameterized signal processing transformations to combat the combinatorial explosion encountered in any exhaustive search among the possible decoding answers for a given signal. Originally developed in the mid 1990's for auditory scene analysis, the IPUS approach has since been refined and extended in the context of various applications. In this paper, we present an overview of IPUS and discuss why its latest developments significantly impact biosignal analysis in diverse rehabilitation applications.

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A C++ software environment for the development of embedded signal processing systems

Cited by 16 publications

References 3 publications

High-yield decomposition of surface EMG signals

High-yield decomposition of surface EMG signals

Decomposition of Surface EMG Signals

What is IPUS and how does it help resolve biosignal complexity?

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