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Biomedical subjects

B Devine

Publications and source records attributed to B Devine.

14 recordsLinked to original sources
PubMed1994

Artificial neural networks for the diagnosis of atrial fibrillation.

Different forms of artificial intelligence have been applied to pattern recognition in medicine. Recently, however, a relatively new technique involving software-based neural networks has become more readily available. Deterministic logic is currently applied to rhythm analysis in computer-assisted ECG interpretation methods developed in the University of Glasgow. The aim of the present study is to compare an artificial neural network with deterministic logic for separating sinus rhythm (SR) with supraventricular extrasystoles (SVEs) and/or ventricular extra-systoles (VEs) from atrial fibrillation (AF) at a particular point in the diagnostic logic of the Glasgow Program. A total of 2363 ECGs with 1495 AF and 868 SR + (SVEs and/or VEs) are used for training and testing a variety of neural networks, and the optimum design is selected. Methods for combining the results of the neural-network classification and the deterministic interpretation are also developed. A further 717 ECGs are used to test the selected network. The results show that the use of an artificial neural network can improve the sensitivity of reporting AF from 88.5% using the deterministic approach to 92%, without sacrificing specificity (92.3%).

Atrial Fibrillation↗
PubMed1994

Effects of age, sex, and race on ECG interval measurements.

The effects of age, sex, and race on the electrocardiogram (ECG) were studied using three separate populations: a pediatric group of 1,782 neonates, infants, and children, and adult white group of 1,555 individuals, and an adult Chinese cohort of 503 individuals. All ECGs were processed using the same computer program, and various interval measurements were derived, including QRS duration, heart rate, QT dispersion, and selected Q-wave durations. Also, a small subgroup of 195 white subjects had a signal-averaged ECG recorded. In the pediatric group, there was a clear link between age and QRS duration, which increased linearly from about 1 year of age to adolescence. In the adults, the principal differences were an increased QRS duration in men compared with women both in the standard and signal-averaged ECG. Upper limits of normal heart rate also tended to be higher in women than in men in the two adult populations. Small racial differences could be seen in some measurements, but were not thought to be of clinical significance.

Adolescent↗
PubMed1994

Use of artificial neural networks within deterministic logic for the computer ECG diagnosis of inferior myocardial infarction.

An investigation into the use of software-based artificial neural networks for the electrocardiographic (ECG) detection of inferior myocardial infarction was made. A total of 592 clinically validated subjects, including 208 with inferior myocardial infarction, 300 normal subjects, and 84 left ventricular hypertrophy cases, were used in this study. A total of 200 ECGs (100 from patients with inferior myocardial infarction and 100 from normal subjects) were fed to 66 supervised feedforward neural networks for training using a back-propagation algorithm. QRS and ST-T wave measurements were used as the input parameters for the neural networks. The best performing network using QRS measurements only and the best using QRS and ST-T data were selected by assessing a test set of 292 ECGs (108 from patients with inferior myocardial infarction, 84 from patients with left ventricular hypertrophy, and 100 from normal subjects). These two networks were then implanted separately into the deterministic Glasgow program for further study. After the implementation, it was found necessary to include a small inferior Q criterion to improve the specificity of reporting inferior myocardial infarction, thereby producing a small loss of sensitivity as compared with use of the network alone. The use of an artificial neural network within the deterministic logic performed better than either alone in the diagnosis of inferior myocardial infarction, producing a 20% gain in sensitivity with 2% loss in overall specificity compared with the original deterministic logic.

Diagnosis, Computer-Assisted↗
PubMed1993

Detection of electrocardiographic 'left ventricular strain' using neural nets.

The use of artificial neural networks for classification of ST-T abnormalities of the electrocardiogram (ECG) was investigated. A training set of 356 lateral leads selected from 105 ECGs was visually classified as exhibiting one particular ST-T morphology (left ventricular (LV) strain) or not. Selected measurements, together with the classification, were fed as input to a three-layer software-based network during the learning process. The performance of the network was evaluated by comparing the results obtained from the network with conventional criteria, using two test sets. Set 1 comprised 63 lateral leads from 32 ECGs with ST-T changes showing atypical forms of LV strain. Set 2 consisted of 80 lateral leads from 20 ECGs containing normal and abnormal T-waves. For set 1, the network outperformed conventional criteria, having a higher sensitivity (96 per cent against 85 per cent) and specificity (67 per cent against 50 per cent). With test set 2, both network and conventional criteria were 100 per cent sensitive and 100 per cent specific. For sets 1 and 2 combined, the network had a higher overall sensitivity (97 per cent against 89 per cent) and specificity (88 per cent against 82 per cent). The results suggest that neural networks may be useful in selected areas of electrocardiography, but care is required when selecting patterns for use in the training process.

Diagnosis, Computer-Assisted↗
PubMed1993

Classification of electrocardiographic ST-T segments--human expert vs artificial neural network.

Artificial neural networks, which can be used for pattern recognition, have recently become more readily available for application in different research fields. In the present study, the use of neural networks was assessed for a selected aspect of electrocardiographic (ECG) waveform classification. Two experienced electrocardiographers classified 1000 ECG complexes singly on the basis of the configuration of the ST-T segments into eight different classes. ECG data from 500 of these ST-T segments together with the corresponding classifications were used for training a variety of neural networks. After this training process, the optimum network correctly classified 399/500 (79.8%) ST-T segments in the separate test set. This compared with a repeatability of 428/500 (85.6%) for one electrocardiographer. Conventional criteria for the classification of one type of ST-T abnormality had a much worse performance than the neural network. It is concluded that neural networks, if carefully incorporated into selected areas of ECG interpretation programs, could be of value in the near future.

Electrocardiography↗
PubMed1993

Deterministic logic versus software-based artificial neural networks in the diagnosis of atrial fibrillation.

An investigation into the use of software-based neural networks for the detection of atrial fibrillation was made. At a specific point in the Glasgow 12-lead electrocardiographic interpretation program, a decision has to be made as to whether atrial fibrillation or sinus rhythm with supraventricular or ventricular extrasystoles is present. The same input parameters used for the deterministic logic at that point were also utilized to train a variety of neural networks. Results from a separate test set showed that the sensitivity of detecting atrial fibrillation could be improved using the best of the neural networks. On the other hand, it was felt that the original deterministic logic could be improved by considering adjustments in order that the presence of certain combinations of findings not previously regarded as representing atrial fibrillation would now do so. When the deterministic logic was upgraded in this way, it was found, again using a separate test set, that the revised logic was improved compared to the original, and also gave a performance similar to that of the neural network. It is concluded that the use of a neural network at a specific diagnostic decision point in a rhythm analysis program can be as effective as deterministic logic, which may take several years to perfect.

Atrial Fibrillation↗
PubMed1992

Neural networks for classification of ECG ST-T segments.

The usefulness of neural networks for pattern recognition in electrocardiographic (ECG) ST-T segments was assessed. Two thousand ST-T segments from the 12-lead ECG were visually classified singly into 7 different groups. The material was divided into a training set and a test set. Computer-measured ST-T data for each element in the training set, paired with the corresponding classification, was input to various configurations of software-based neural networks during a learning process. Thereafter, the networks correctly classified 90-95% of the individual ST-T segments in the test set. The importance of the size and composition of the training set in determining the performance of a network was clearly demonstrated. In conclusion, neural networks can be used for classification of ST-T segments. If carefully incorporated into a conventional ECG interpretation program, neural networks may well be of value for automated ECG interpretation in the near future.

Electrocardiography↗
PubMed1990

Methodology of ECG interpretation in the Glasgow program.

This paper describes the methods currently used in Glasgow Royal Infirmary for computer analysis of electrocardiograms. The software is designed to analyse from 3 to 15 simultaneously recorded leads, with facilities for analysis of rhythm and serial changes. Options for Minnesota Code (with serial comparison) and XYZ lead interpretation are available.

Diagnosis, Computer-Assisted↗
PubMed1989

The assessment of chronic pancreatitis.

The stimulated pancreatic polypeptide (PP) response was compared to standard tests of pancreatic function in patients with proven chronic pancreatitis and control subjects. Although the median values of the PP test in patients were statistically significantly different from those of controls, much variability was seen in both groups. The PP response seems to correlate with pancreatic exocrine function and shows good agreement with the results of other tests of exocrine pancreatic function while avoiding many of the difficulties inherent in performing other tests. Although more studies are needed, the PP response provides a valuable test of pancreatic function.

Adult↗
PubMedDate not supplied

Longitudinal versus traditional residencies: a study of continuity of care.

BACKGROUND AND OBJECTIVES: Continuity of care is one of the presumed advantages of longitudinal residencies. However, it is not clear how well such residencies provide continuity of care, and, further, there is no recognized acceptable rate of good continuity. We compared traditional and longitudinal residencies to determine the extent to which the residents provided their patients with continuity of care. METHODS: We conducted a systematic chart review at three longitudinal and three matched traditional block-rotation programs. In total, 628 charts were reviewed, and 6,256 visits were evaluated. Continuity with a primary resident was evaluated over a 2-year period, with continuity defined as the percentage of visits for which the patient saw the same resident. RESULTS: There was no significant difference in overall rates of continuity between longitudinal and traditional programs (59.6% versus 57.8%). One longitudinal program, however, had a 74.8% rate of continuity, which was significantly higher than the rates in the otherfive programs. CONCLUSIONS: There was no significant difference found in continuity of care provided by residents at longitudinal programs, compared with those at traditional programs. Our results do not support the hypothesis that longitudinal residency programs achieve superior rates of continuity of care. Further comparison studies of longitudinal and traditional programs would be useful.

Analysis of Variance↗