Biomedical subjects

B Hudgins

Publications and source records attributed to B Hudgins.

7 recordsLinked to original sources

PubMed2001

Myo-electric signals to augment speech recognition.

It is proposed that myo-electric signals can be used to augment conventional speech-recognition systems to improve their performance under acoustically noisy conditions (e.g. in an aircraft cockpit). A preliminary study is performed to ascertain the presence of speech information within myo-electric signals from facial muscles. Five surface myo-electric signals are recorded during speech, using Ag-AgCl button electrodes embedded in a pilot oxygen mask. An acoustic channel is also recorded to enable segmentation of the recorded myo-electric signal. These segments are processed off-line, using a wavelet transform feature set, and classified with linear discriminant analysis. Two experiments are performed, using a ten-word vocabulary consisting of the numbers 'zero' to 'nine'. Five subjects are tested in the first experiment, where the vocabulary is not randomised. Subjects repeat each word continuously for 1 min; classification errors range from 0.0% to 6.1%. Two of the subjects perform the second experiment, saying words from the vocabulary randomly; classification errors are 2.7% and 10.4%. The results demonstrate that there is excellent potential for using surface myo-electric signals to enhance the performance of a conventional speech-recognition system.

Adult↗

PubMed2001

A wavelet-based continuous classification scheme for multifunction myoelectric control.

This work represents an ongoing investigation of dexterous and natural control of powered upper limbs using the myoelectric signal. When approached as a pattern recognition problem, the success of a myoelectric control scheme depends largely on the classification accuracy. A novel approach is described that demonstrates greater accuracy than in previous work. Fundamental to the success of this method is the use of a wavelet-based feature set, reduced in dimension by principal components analysis. Further, it is shown that four channels of myoelectric data greatly improve the classification accuracy, as compared to one or two channels. It is demonstrated that exceptionally accurate performance is possible using the steady-state myoelectric signal. Exploiting these successes, a robust online classifier is constructed, which produces class decisions on a continuous stream of data. Although in its preliminary stages of development, this scheme promises a more natural and efficient means of myoelectric control than one based on discrete, transient bursts of activity.

Algorithms↗

PubMed2000

Nonlinear adaptive filtering of stimulus artifact.

Noninvasive measurements of somatosensory evoked potentials have both clinical and research applications. The electrical artifact which results from the stimulus is an interference which can distort the evoked signal, and introduce errors in response onset timing estimation. Given that this interference is synchronous with the evoked signal, it cannot be reduced by the conventional technique of ensemble averaging. The technique of adaptive noise cancelling has potential in this regard however, and has been used effectively in other similar problems. An adaptive noise cancelling filter which uses a neural network as the adaptive element is investigated in this application. The filter is implemented and performance determined in the cancelling of artifact for in vivo measurements on the median nerve. A technique of segmented neural network training is proposed in which the network is trained on that segment of the record time window which does not contain the evoked signal. The neural network is found to generalize well from this training to include the segment of the window containing the evoked signal. Both quantitative and qualitative measures show that significant stimulus artifact reduction is achieved.

Artifacts↗

PubMed1998

Errors associated with the use of adaptive differential pulse code modulation in the compression of isometric and dynamic myo-electric signals.

Muscle activity produces an electrical signal termed the myo-electric signal (MES). The MES is a useful clinical tool, used in diagnostics and rehabilitation. This signal is typically stored in 2 bytes as 12-bit data, sampled at 3 kHz, resulting in a 6 kbyte s-1 storage requirement. Processing MES data requires large bit manipulations and heavy memory storage requirements. Adaptive differential pulse code modulation (ADPCM) is a popular and successful compression technique for speech. Its application to MES would reduce 12-bit data to a 4-bit representation, providing a 3:1 compression. As, in most practical applications, memory is organised in bytes, the realisable compression is 4:1, as pairs of data can be stored in a single byte. The performance of the ADPCM compression technique, using a real-time system at 1 kHz, 2 kHz and 4 kHz sampling rates, is evaluated. The data used include MES from both isometric and dynamic contractions. The percent residual difference (PRD) between an unprocessed and processed MES is used as a performance measure. Errors in computed parameters, such as median frequency and variance, which are used in clinical diagnostics, and waveform features employed in prosthetic control are also used to evaluate the system. The results of the study demonstrate that the ADPCM compression technique is an excellent solution for relieving the data storage requirements of MES both in isometric and dynamic situations.

Electromyography↗

PubMed1995

Two-channel enhancement of a multifunction control system.

The enhancement of an existing myoelectric control system has been investigated. The original one-channel system used an artificial neural network to classify myoelectric patterns. This research shows that a two-channel control system can improve the classification accuracy of the pattern classifier significantly, thus improving the reliability of the prosthesis.

Adult↗

PubMed1993

A new strategy for multifunction myoelectric control.

This paper describes a novel approach to the control of a multifunction prosthesis based on the classification of myoelectric patterns. It is shown that the myoelectric signal exhibits a deterministic structure during the initial phase of a muscle contraction. Features are extracted from several time segments of the myoelectric signal to preserve pattern structure. These features are then classified using an artificial neural network. The control signals are derived from natural contraction patterns which can be produced reliably with little subject training. The new control scheme increases the number of functions which can be controlled by a single channel of myoelectric signal but does so in a way which does not increase the effort required by the amputee. Results are presented to support this approach.

Amputation, Surgical↗

PubMedDate not supplied

Classification of the myoelectric signal using time-frequency based representations.

An accurate and computationally efficient means of classifying surface myoelectric signal patterns has been the subject of considerable research effort in recent years. Effective feature extraction is crucial to reliable classification and, in the quest to improve the accuracy of transient myoelectric signal pattern classification, an ensemble of time-frequency based representations are proposed. It is shown that feature sets based upon the short-time Fourier transform, the wavelet transform, and the wavelet packet transform provide an effective representation for classification, provided that they are subject to an appropriate form of dimensionality reduction.

Action Potentials↗