Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “HANDWRITING”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,585 records · Page 88Linked to original sources
PubMed1989

Treatment of focal dystonias of the hand with botulinum toxin injections.

The effects of botulinum toxin injections have been studied on 19 patients with hand dystonia. The dystonic muscles were identified by clinical examination and EMG findings of localised bursts of muscle activation with fine wire electrodes during the tasks that precipitated the dystonia. Injections into the most active muscles were given to each patient every 2 weeks in increasing doses (up to 20 U the first week, up to 40 U the second week, and up to 80 U the third week) until performance improvement was achieved. Subjective improvement of cramping, pain and/or tension was associated with temporary weakness in injected muscles. Benefit was seen in 16 patients, lasted between 1 and 6 months, and was reproducible.

Adult↗
PubMed1992

Hypergraphia associated with a brain tumour of the right cerebral hemisphere.

Two different neurobehavioural abnormalities have been reported under the term hypergraphia. One has been described in temporal lobe epilepsies and the other in the acute stage of strokes of the right cerebral hemisphere. The latter type of hypergraphia in a patient with a metastatic brain tumour confined to the right hemisphere is reported. Such hypergraphia is a general right hemisphere sign that is not peculiar to strokes in the acute stage and the writing behaviour is inattentive.

Adenocarcinoma↗
PubMed1996

Increased writing activity in neurological conditions: a review and clinical study.

Increased writing activity in a 70 year old, right handed man presenting with a history of alcohol misuse and maturity onset diabetes is reported. Brain CT disclosed corticosubcortical atrophy and 99mTc-HMPAO SPECT disclosed severe bilateral frontal hypoperfusion more prominent on the right. The patient's neuropsychological symptomatology consisted of severe (verbal) aspontaneity, intermittent utilisation behaviour, and pronounced increased writing activity, which mainly consisted of a perseverative, micrographic written reproduction of visually or verbally perceived language fragments. Several neurological causes of increased writing activity and the equivocal terminology met in the medical literature are reviewed. A distinction between hypergraphia and automatic writing behaviour is proposed. It is concluded that our patient's increased writing activity may be characterised as automatic writing behaviour.

Aged↗
PubMed2006

Effect of computerisation on the quality and safety of chemotherapy prescription.

BACKGROUND: Chemotherapy is prescribed according to protocols of several cycles. These protocols include not only therapeutic agents but also adjuvant solvents and inherent supportive care measures. Multiple errors can occur during the prescription, the transmission of documents and the drug delivery processes, and lead to potentially serious consequences. OBJECTIVE: To assess the effect of a computerised physician order entry (CPOE) system on the number of errors in prescription recorded by the centralised chemotherapy unit of a pharmacy service in a university hospital. PATIENTS AND METHODS: Existing chemotherapy protocols were standardised by a multidisciplinary team (composed of a doctor, a pharmacist and a nurse) and a CPOE system was developed from a File Maker Pro database. Chemotherapy protocols were progressively introduced into the CPOE system. The effect of the system on prescribing errors was measured over 15 months before and 21 months after starting computerised protocol prescription. Errors were classified as major (dosage and drug name) and minor (volume or type of infusion solution). RESULTS: Before computerisation, 141 errors were recorded for 940 prescribed chemotherapy regimens (15%). After introduction of the CPOE system, 75 errors were recorded for 1505 prescribed chemotherapy regimens (5%). Of these errors, 69 (92%) were recorded in prescriptions that did not use a computerised protocol. A dramatic decrease in the number of errors was noticeable when 50% of the chemotherapy protocols were prescribed through the CPOE system. CONCLUSION: Errors in chemotherapy prescription nearly disappeared after implementation of CPOE. The safety of chemotherapy prescription was markedly improved.

Adult↗
PubMed2001

A recurrent neural network for partitioning of hand drawn characters into strokes of different orientations.

A neural network is described which is intended to extract orientation features that should be used for recognition of hand drawn characters. The network partitions the input hand drawn characters into separate line segments (strokes) according to their orientations. The network consists of several neural layers; each layer serves for extracting strokes of a certain orientation. Every neural layer has one-to-one correspondence with an input screen. The network uses an iterative update procedure which includes interactions of neurons inside each layer through oriented excitatory connections and inhibitory interrelations between the corresponding neurons of different layers. Computer simulation of the network was performed. Experiments showed that the network efficiently classifies all pixels of any hand drawn characters according to the orientations of the strokes constituting these characters and performs, as a result of that, a reasonable segmentation of characters.

Algorithms↗
PubMed1994

Handwritten digit recognition using two-layer self-organizing maps.

In this paper, we present a two-layer self-organizing neural network based method for handwritten digit recognition. The network consists of a base layer self-organizing map and a set of corresponding maps in the second layer. The input patterns are partitioned into subspace in the first layer. Patterns in a subspace are led to the second layer and a corresponding map is built according to the first layer performance. In the classification process, each pattern searches for several closest nodes from the base map and then it is classified into a specified class by determining the nearest model of the corresponding maps in the second layer. The new method yielded higher accuracy and faster performance than the ordinary self-organizing neural network.

Handwriting↗
PubMed1995

Recognition of Telugu characters using neural networks.

The aim of the present work is to recognize printed and handwritten Telugu characters using artificial neural networks (ANNs). Earlier work on recognition of Telugu characters has been done using conventional pattern recognition techniques. We make an initial attempt here of using neural networks for recognition with the aim of improving upon earlier methods which do not perform effectively in the presence of noise and distortion in the characters. The Hopfield model of neural network working as an associative memory is chosen for recognition purposes initially. Due to limitation in the capacity of the Hopfield neural network, we propose a new scheme named here as the Multiple Neural Network Associative Memory (MNNAM). The limitation in storage capacity has been overcome by combining multiple neural networks which work in parallel. It is also demonstrated that the Hopfield network is suitable for recognizing noisy printed characters as well as handwritten characters written by different "hands" in a variety of styles. Detailed experiments have been carried out using several learning strategies and results are reported. It is shown here that satisfactory recognition is possible using the proposed strategy. A detailed preprocessing scheme of the Telugu characters from digitized documents is also described.

Algorithms↗
PubMed1995

Comparison of multilayer neural network and Nearest Neighbor Classifiers for handwritten digit recognition.

The basic Nearest Neighbor Classifier (NNC) is often inefficient for classification in terms of memory space and computing time needed if all training samples are used as prototypes. These problems can be solved by reducing the number of prototypes using clustering algorithms and optimizing the prototypes using a special neural network model. In this paper, we compare the performance of the multilayer neural network and an Optimized Nearest Neighbor Classifier (ONNC) for handwritten digit recognition applications. We show that an ONNC can have the same recognition performance as an equivalent neural network classifier. The ONNC can be efficiently implemented using prototype and variable ranking, partial summation and distance triangular inequality based strategies. It requires the same memory space as, but less, training time and classification time than the neural network.

Computers↗
PubMed1996

Probabilistic interpretation of feedforward network outputs, with relationships to statistical prediction of ordinal quantities.

Several problems require the estimation of discrete random variables whose values can be put in a one-to-one ordered correspondence with a finite subset of the natural numbers. This happens whenever quantities are involved that represent integer items, or have been quantized on a fixed number of levels, or correspond to "graded" linguistic values. Here we propose a correct probabilistic approach to such kind of problems that fully exploits all the available prior knowledge about their own structure. In spite of the very stringent constraints induced in output space, the method can be directly applied to standard feed-forward networks while keeping local computation of both outputs and error signals. According to these guidelines, we devised a neural implementation of a complex image pre-processing algorithm by using very poor resolution on the computing elements in the network.

Algorithms↗
PubMed1997

Classification of handwritten digits using a RAM neural net architecture.

Results are reported on the task of recognizing handwritten digits without any advanced pre-processing. The result are obtained using a RAM-based neural network, making use of small receptive fields. Furthermore, a technique that introduces negative weights into the RAM net is reported. The results obtained on the task of recognizing handwritten digits is comparable with the best performances reported in the literature.

Classification↗
PubMed1988

Writing tremor myoclonus.

A patient with a 9-year history of primary writing tremor is described. Spontaneous postural tonic abduction and tremor of the right index finger was also present. Atypical features included unidirectionality of the tremor, writing-associated and independent, bilateral proximal upper limb myoclonus. No clinical response was obtained after intravenous benztropine and subtotal lidocaine infiltration of the right first dorsal interosseous muscle motor point. There was no dystonia present. This case illustrates the sporadic occurrence of writing tremor of the primary type in the absence of dystonia, otherwise a common feature of writer's cramp.

Dystonia↗
PubMed1991

Tiapride as treatment for certain patients with idiopathic torsion dystonia.

Five female patients with idiopathic torsion dystonia (ITD) responded to treatment with tiapride, a selective D-2 dopamine antagonist. Previous treatments with various drugs, including anticholinergics, were ineffective. It is suggested that the previously reported genetic subgroups of ITD respond to different drug regimens. The autosomal dominant group responded to anticholinergics, while the autosomal recessive group responded best to a selective D-2 dopamine antagonist.

Adult↗