Real-time digital signal processing of electroneurographic signals

Upshaw, B.; Sinkjær, Thomas

doi:10.1109/iembs.1994.415465

Cited by 3 publications

(2 citation statements)

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“…The amplifier consistedof a standard audio transformer, which providedimpedance matching and an initial passive signal gain(4), coupled to a low‐noise instrumentation amplifierpowered by two 9 V batteries. The output of thisamplifier was then connected to a portablebattery‐operated digital signal processing (DSP) system (5). A ground electrode, connected to the amplifiercommon, was placed 2 cm proximal to the nerve cuff.Two self‐adhering surface stimulation electrodeswere placed over the peroneal nerve, 2 cm and 6 cmdistal to the knee joint, on the lateral side of the leftleg.…”

Section: Methodsmentioning

confidence: 99%

Natural Versus Artificial Sensors Applied in Peroneal Nerve Stimulation

Upshaw

Sinkjær

1997

Artificial Organs

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We have attempted to quantify the performance of natural versus artificial sensors when used in a closed-loop functional electrical stimulation system. Peroneal nerve stimulation was applied during gait to a multiple sclerosis subject with a drop foot. Stimulation was applied only during the swing phase to provide artificially induced dorsiflexion of the foot. Detection of the onset of the stance phase was accomplished using a standard heel contact switch mounted inside the subject's shoe (the artificial sensor) and using processed nerve signals derived from an implanted nerve-cuff electrode (the natural sensor). A detection percentage of at least 85% was achieved using the afferent nerve signal information only. When muscle activity (also recorded in the cuff) and additional information about the gait cycle were incorporated, functional detection ratios approaching 100% were achieved.

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Section: Methodsmentioning

confidence: 99%

Natural Versus Artificial Sensors Applied in Peroneal Nerve Stimulation

Upshaw

Sinkjær

1997

Artificial Organs

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“…This nerve contains strictly sensory afferent fibers, primarily innervating the heel area of the foot. Using local anesthesia, a nerve-cuff electrode, constructed from a 3-cm long section of biocompatible silicone tubing, was implanted approximately 5 cm proximal and 3 cm posterior to the medial malleolus of the left ankle joint, [13]. The chronic implantation of this electrode for the purpose of recording nerve signals was approved by the local medical ethics committee.…”

Section: A Nerve Signal Recordingsmentioning

confidence: 99%

Digital signal processing algorithms for the detection of afferent nerve activity recorded from cuff electrodes

Upshaw

Sinkjær

1998

IEEE Trans. Rehab. Eng.

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Due to the very poor signal-to-noise ratios (SNR's) usually encountered with whole nerve-cuff signals, the processing method typically applied, rectification and windowed (bin)integration (RBI), can have serious shortcomings in extracting reliable information. In order to improve detection accuracy, these signals were further analyzed using statistical signal detection algorithms based on their second and higher order spectra (HOS). A comparison with both analog and digital RBI processing suggests that the statistical methods, due to their ability to separate the signal and noise subspaces, are superior. It was determined that the noise typically encountered with nerve-cuff electrode signals is normally (Gaussian) distributed. Therefore, third-order statistics can be applied to, ideally, completely reject the noise component. When cutaneous nerve recordings from the calcaneal nerve (innervating the heel area) were used in a dropfoot correction neural prosthesis, the detection percentage and the insensitivity to algorithm parameters were increased through the use of these statistical methods as to warrant their real-time implementation, and the inherent additional processing hardware that entails.

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Tripolar nerve cuff recording: stimulus artifact, EMG and the recorded nerve signal

Struijk

Thomsen²

Proceedings of 17th International Conference of the Engineering in Medicine and Biology Society

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Real-time digital signal processing of electroneurographic signals

Cited by 3 publications

References 1 publication

Natural Versus Artificial Sensors Applied in Peroneal Nerve Stimulation

Natural Versus Artificial Sensors Applied in Peroneal Nerve Stimulation

Digital signal processing algorithms for the detection of afferent nerve activity recorded from cuff electrodes

Tripolar nerve cuff recording: stimulus artifact, EMG and the recorded nerve signal

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