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At least 235 records · Page 13Linked to original sources
PubMed1999

The influence of ionizing radiation on the CLARION 1.2 cochlear implant during radiation therapy.

OBJECTIVE: This study aimed to determine the maximum dose of radiation the CLARION 1.2 cochlear implant can withstand safely. INTRODUCTION: Cochlear implants restore functional hearing to patients with sensorineural deafness. Because some patients may need radiation therapy, it is important to investigate the influence of ionizing radiation on cochlear implant function. METHODS: This study tested the function of four CLARION 1.2 implants (Advanced Bionics, Sylmar, CA, U.S.A.) after varying radiation treatments with gamma rays. The first implant received a cumulative dosage of 69 Gy over nine treatments (single doses between 0.1-30 Gy). The second was irradiated with a total of 90 Gy, receiving three treatments of 30 Gy each. The third and fourth received doses more typical of patient therapy (i.e., 2 Gy) approximately 30 times, for a cumulative dosage of approximately 60 Gy. Implant function was tested after every treatment; the CLARION implant incorporates a back-telemetry system, allowing impedance and current output testing. RESULTS: Despite the type of treatment, the results were quite consistent: difficulties in function occurred when the cumulative dosage inside the implant was approximately 60 Gy. The first implant recovered completely and the second recovered partially. DISCUSSION: The CLARION 1.2 cochlear implant seems to safely withstand approximately 60 Gy of radiation before experiencing functional difficulties. In a clinical situation, the implant would not likely be in the target volume irradiated, and thus the patient's therapeutic cumulative dosage might be higher.

Cochlear Implants↗
PubMed1993

Reconstructing muscle activation during normal walking: a comparison of symbolic and connectionist machine learning techniques.

One symbolic (rule-based inductive learning) and one connectionist (neural network) machine learning technique were used to reconstruct muscle activation patterns from kinematic data measured during normal human walking at several speeds. The activation patterns (or desired outputs) consisted of surface electromyographic (EMG) signals from the semitendinosus and vastus medialis muscles. The inputs consisted of flexion and extension angles measured at the hip and knee of the ipsilateral leg, their first and second derivatives, and bilateral foot contact information. The training set consisted of data from six trials, at two different speeds. The testing set consisted of data from two additional trials (one at each speed), which were not in the training set. It was possible to reconstruct the muscular activation at both speeds using both techniques. Timing of the reconstructed signals was accurate. The integrated value of the activation bursts was less accurate. The neural network gave a continuous output, whereas the rule-based inductive learning rule tree gave a quantised activation level. The advantage of rule-based inductive learning was that the rules used were both explicit and comprehensible, whilst the rules used by the neural network were implicit within its structure and not easily comprehended. The neural network was able to reconstruct the activation patterns of both muscles from one network, whereas two separate rule sets were needed for the rule-based technique. It is concluded that machine learning techniques, in comparison to explicit inverse muscular skeletal models, show good promise in modelling nearly cyclic movements such as locomotion at varying walking speeds.(ABSTRACT TRUNCATED AT 250 WORDS)

Algorithms↗