Biomedical subjects

E L Hines

Publications and source records attributed to E L Hines.

4 recordsLinked to original sources

PubMed2002

Classifying coronary dysfunction using neural networks through cardiovascular auscultation.

The paper applies artificial neural networks (ANNs) to the analysis of heart sound abnormalities through auscultation. Audio auscultation samples of 16 different coronary abnormalities were collected. Data pre-processing included down-sampling of the auscultated data and use of the fast Fourier transform (FFT) and the Levinson-Durbin autoregression algorithms for feature extraction and efficient data encoding. These data were used in the training of a multi-layer perceptron (MLP) and radial basis function (RBF) neural network to develop a classification mechanism capable of distinguishing between different heart sound abnormalities. The MLP and RBF networks attained classification accuracies of 84% and 88%, respectively. The application of ANNs to the analysis of respiratory auscultation and consequently the development of a combined cardio-respiratory analysis system using auscultated data could lead to faster and more efficient treatment.

Adult↗

PubMed1999

Neural network correction of ultrasonic C-scan images.

A neural network-based approach to the correction of C-scan images is presented. This allows the effects of a finite ultrasonic beam diameter in an immersion experiment to be considered, by training the network on a series of defects of known characteristics. The result is an image which is a better representation of the actual defect.

Humans↗

PubMed1996

Neural network based on adaptive resonance theory as compared to experts in suggesting treatment for schizophrenic and unipolar depressed in-patients.

A modified neural network based on adaptive resonance theory (ART) was trained with the records of 211 psychiatric inpatients (74 schizophrenic, 50 unipolar depressed, 34 bipolar depressed, 20 bipolar manic, 33 other) who improved by at least 40 points on the GAFS during 8 weeks of treatment. Thereafter, a comparison was made between the clinical response of another 26 schizophrenic patients and 28 unipolar depressed inpatients, to treatment suggested by the trained ART (N = 21) and by the consensus of two senior psychiatrists (N = 33). The patients were allocated blindly and randomly to the two treatment groups. The BPRS (for the schizophrenic patients) or the HDRS (for the unipolar depressed patients) was completed weekly for 5 weeks. Results showed no difference between decisions regarding treatment by the ART network and by the experts. Length of hospital stay was also similar. All ART suggestions included supportive psychotherapy. High potency antipsychotics were suggested for 7 schizophrenic inpatients, clozapine for one and the addition of community therapy for another. Depressed patients got a variety of treatment suggestions. No contraindicated treatment was suggested by ART; however, two incomplete treatment suggestions were dropped from the study. In conclusion, in a prospective study ART was successful in learning treatment strategies and performed under supervision similar to experts.

Adolescent↗

PubMedDate not supplied

Electronic nose based tea quality standardization.

In this paper we have used a metal oxide sensor (MOS) based electronic nose (EN) to analyze five tea samples with different qualities, namely, drier month, drier month again over-fired, well fermented normal fired in oven, well fermented overfired in oven, and under fermented normal fired in oven. The flavour of tea is determined mainly by its taste and smell, which is generated by hundreds of Volatile Organic Compounds (VOCs) and Non-Volatile Organic Compounds present in tea. These VOCs are present in different ratios and determine the quality of the tea. For example Assamica (Sri Lanka and Assam Tea) and Assamica Sinesis (Darjeeling and Japanese Tea) are two different species of tea giving different flavour notes. Tea flavour is traditionally measured through the use of a combination of conventional analytical instrumentation and human or ganoleptic profiling panels. These methods are expensive in terms of time and labour and also inaccurate because of a lack of either sensitivity or quantitative information. In this paper an investigation has been made to determine the flavours of different tea samples using an EN and to explore the possibility of replacing existing analytical and profiling panel methods. The technique uses an array of 4 MOSs, each of, which has an electrical resistance that has partial sensitivity to the headspace of tea. The signals from the sensor array are then conditioned by suitable interface circuitry. The data were processed using Principal Components Analysis (PCA), Fuzzy C Means algorithm (FCM). We also explored the use of a Self-Organizing Map (SOM) method along with a Radial Basis Function network (RBF) and a Probabilistic Neural Network classifier. Using FCM and SOM feature extraction techniques along with RBF neural network we achieved 100% correct classification for the five different tea samples with different qualities. These results prove that our EN is capable of discriminating between the flavours of teas manufactured under different processing conditions, viz. over-fermented, over-fired, under fermented, etc.

Chemoreceptor Cells↗