Biomedical subjects

Lars Edenbrandt

Publications and source records attributed to Lars Edenbrandt.

22 records · Page 2Linked to original sources

PubMed2004

Detecting acute myocardial infarction in the 12-lead ECG using Hermite expansions and neural networks.

We use artificial neural networks (ANNs) to detect signs of acute myocardial infarction (AMI) in ECGs. The 12-lead ECG is decomposed into Hermite basis functions, and the resulting coefficients are used as inputs to the ANNs. Furthermore, we present a case-based method that qualitatively explains the operation of the ANNs, by showing regions of each ECG critical for ANN response. Key ingredients in this method are: (i) a cost function used to find local ECG perturbations leading to the largest possible change in ANN output and (ii) a minimization scheme for this cost function using mean field annealing. Our approach was tested on 2238 ECGs recorded at an emergency department. The obtained ROC areas for ANNs trained with the Hermite representation and standard ECG measurements were 83.4 and 84.3% (P=0.4), respectively. We believe that the proposed method has potential as a decision support system that can provide good advice for diagnosis, as well as providing the physician with insight into the reason underlying the advice.

Acute Disease↗

PubMed2003

Role of ventilation scintigraphy in diagnosis of acute pulmonary embolism: an evaluation using artificial neural networks.

The purpose of this study was to assess the value of the ventilation study in the diagnosis of acute pulmonary embolism using a new automated method. Either perfusion scintigrams alone or two different combinations of ventilation/perfusion scintigrams were used as the only source of information regarding pulmonary embolism. A completely automated method based on computerised image processing and artificial neural networks was used for the interpretation. Three artificial neural networks were trained for the diagnosis of pulmonary embolism. Each network was trained with 18 automatically obtained features. Three different sets of features originating from three sets of scintigrams were used. One network was trained using features obtained from each set of perfusion scintigrams, including six projections. The second network was trained using features from each set of (joint) ventilation and perfusion studies in six projections. A third network was trained using features from the perfusion study in six projections combined with a single ventilation image from the posterior view. A total of 1,087 scintigrams from patients with suspected pulmonary embolism were used for network training. The test group consisted of 102 patients who had undergone both scintigraphy and pulmonary angiography. Performances in the test group were measured as area under the receiver operation characteristic curve. The performance of the neural network in interpreting perfusion scintigrams alone was 0.79 (95% confidence limits 0.71-0.86). When one ventilation image (posterior view) was added to the perfusion study, the performance was 0.84 (0.77-0.90). This increase was statistically significant ( P=0.022). The performance increased to 0.87 (0.81-0.93) when all perfusion and ventilation images were used, and the increase in performance from 0.79 to 0.87 was also statistically significant ( P=0.016). The automated method presented here for the interpretation of lung scintigrams shows a significant increase in performance when one or all ventilation images are added to the six perfusion images. Thus, the ventilation study has a significant role in the diagnosis of acute lung embolism.

Adolescent↗

PubMed2002

Neural networks--a diagnostic tool in acute myocardial infarction with concomitant left bundle branch block.

The prognosis of acute myocardial infarction (AMI) improves by early revascularization. However the presence of left bundle branch block (LBBB) in the electrocardiogram (ECG) increases the difficulty in recognizing an AMI and different ECG criteria for the diagnosis of AMI have proved to be of limited value. The purpose of this study was to detect AMI in ECGs with LBBB using artificial neural networks and to compare the performance of the networks to that of six sets of conventional ECG criteria and two experienced cardiologists. A total of 518 ECGs, recorded at an emergency department, with a QRS duration > 120 ms and an LBBB configuration, were selected from the clinical ECG database. Of this sample 120 ECGs were recorded on patients with AMI, the remaining 398 ECGs being used as a control group. Artificial neural networks of feed-forward type were trained to classify the ECGs as AMI or not AMI. The neural network showed higher sensitivities than both the cardiologists and the criteria when compared at the same levels of specificity. The sensitivity of the neural network was 12% (P = 0.02) and 19% (P = 0.001) higher than that of the cardiologists. Artificial neural networks can be trained to detect AMI in ECGs with concomitant LBBB more effectively than conventional ECG criteria or experienced cardiologists.

Acute Disease↗

PubMedDate not supplied

Value of exercise data for the interpretation of myocardial perfusion SPECT.

BACKGROUND: Artificial neural networks have successfully been applied for automated interpretation of myocardial perfusion images. So far the networks have used data from the myocardial perfusion images only. The purpose of this study was to investigate whether the automated interpretation of myocardial perfusion images with the use of artificial neural networks was improved if clinical data were assessed in addition to the perfusion images. METHODS AND RESULTS: A population of 229 patients who had undergone both rest-stress myocardial perfusion scintigraphy in conjunction with an exercise test and coronary angiography, with no more than 3 months elapsing between the 2 examinations, were studied. The networks were trained to detect coronary artery disease or myocardial ischemia with the use of 2 different gold standards. The first was based on coronary angiography, and the second was based on all data available (including perfusion scintigrams, coronary angiography, exercise test, resting electrocardiography, patient history, etc). The performance of the neural networks was quantified as areas under the receiver operating characteristic curves. The results showed that the neural networks trained with perfusion images performed better than those trained with exercise data (0.78 vs 0.55, P <.0001), with coronary angiography used as the gold standard. Furthermore, the networks did not improve when data from the exercise test were used as input in addition to the perfusion images (0.78 vs 0.77, P =.6). CONCLUSIONS: The results show that the clinically important information in combined exercise test and myocardial scintigraphy could be found in the perfusion images. Exercise test information did not improve upon the accuracy of automated neural network interpretation of myocardial perfusion images in a receiver operator characteristic analysis of test accuracy.

Adult↗