Lars Edenbrandt

Clustering ECG complexes using Hermite functions and self-organizing maps

An integrated method for clustering of QRS complexes is presented which includes basis function representation and self-organizing neural networks (NNs). Each QRS complex is decomposed into Hermite basis functions and the resulting coefficients and width parameter are used to represent the complex. By means of this representation, unsupervised self-organizing NNs are employed to cluster the data into 25 groups. Using the MIT-BIH arrhythmia database, the resulting clusters are found to exhibit a very low degree of misclassification (1.5%). The integrated method outperforms, on the MIT-BIH database, both a published supervised learning method as well as a conventional template cross-correlation clustering method.

EANM/ESC procedural guidelines for myocardial perfusion imaging in nuclear cardiology

The European procedural guidelines for radionuclide imaging of myocardial perfusion and viability are presented in 13 sections covering patient information, radiopharmaceuticals, injected activities and dosimetry, stress tests, imaging protocols and acquisition, quality control and reconstruction methods, gated studies and attenuation-scatter compensation, data analysis, reports and image display, and positron emission tomography. If the specific recommendations given could not be based on evidence from original, scientific studies, we tried to express this state-of-art. The guidelines are designed to assist in the practice of performing, interpreting and reporting myocardial perfusion SPET. The guidelines do not discuss clinical indications, benefits or drawbacks of radionuclide myocardial imaging compared to non-nuclear techniques, nor do they cover cost benefit or cost effectiveness.

Vectorcardiogram synthesized from a 12-lead ECG: Superiority of the inverse Dower matrix

Vectorcardiographic (VCG) criteria for the diagnosis of, for example, myocardial infarction and right ventricular hypertrophy, are superior to the corresponding 12-lead ECG criteria. Contour and rotation of the QRS loops are important parts of these VCG criteria that have no direct counterpart in the 12-lead ECG. Therefore, attempts have been made to synthesize VCGs from 12-lead ECGs for diagnostic purposes. Visual comparison of QRS loops from the Frank VCG and three different synthesized VCGs was made by three independent observers to determine which method produces the most Frank-like QRS loops. The inverse transformation matrix of Dower proved to be the best method of synthesis. Normal limits for some clinically important measurements in VCG interpretation were calculated for this synthesis method and the Frank VCG.

Changes in high-frequency QRS components are more sensitive than ST-segment deviation for detecting acute coronary artery occlusion☆

OBJECTIVES This study describes changes in high-frequency QRS components (HF-QRS) during percutaneous transluminal coronary angioplasty (PTCA) and compares the ability of these changes in HF-QRS and ST-segment deviation in the standard 12-lead electrocardiogram (ECG) to detect acute coronary artery occlusion. BACKGROUND Previous studies have shown decreased HF-QRS in the frequency range of 150-250 Hz during acute myocardial ischemia. It would be important to know whether the high-frequency analysis could add information to that available from the ST segments in the standard ECG. METHODS The study population consisted of 52 patients undergoing prolonged balloon occlusion during PTCA. Signal-averaged electrocardiograms (SAECG) were recorded prior to and during the balloon inflation. The HF-QRS were determined within a bandwidth of 150-250 Hz in the preinflation and inflation SAECGs. The ST-segment deviation during inflation was determined in the standard frequency range. RESULTS The sensitivity for detecting acute coronary artery occlusion was 88% using the high-frequency method. In 71% of the patients there was ST elevation during inflation. If both ST elevation and depression were considered, the sensitivity was 79%. The sensitivity was significantly higher using the high-frequency method, p<0.002, compared with the assessment of ST elevation. CONCLUSIONS Acute coronary artery occlusion is detected with higher sensitivity using high-frequency QRS analysis compared with conventional assessment of ST segments. This result suggests that analysis of HF-QRS could provide an adjunctive tool with high sensitivity for detecting acute myocardial ischemia.

Acute Myocardial Infarction Detected in the 12-Lead ECG by Artificial Neural Networks

BACKGROUND The 12-lead ECG, together with patient history and clinical findings, remains the most important method for early diagnosis of acute myocardial infarction. Automated interpretation of ECG is widely used as decision support for less experienced physicians. Recent reports have demonstrated that artificial neural networks can be used to improve selected aspects of conventional rule-based interpretation programs. The purpose of this study was to detect acute myocardial infarction in the 12-lead ECG with artificial neural networks. METHODS AND RESULTS A total of 1120 ECGs from patients with acute myocardial infarction and 10,452 control ECGs, recorded at an emergency department with computerized ECGs, were studied. Artificial neural networks were trained to detect acute myocardial infarction by use of measurements from the 12 ST-T segments of each ECG, together with the correct diagnosis. After this training process, the performance of the neural networks was compared with that of a widely used ECG interpretation program and the classification of an experienced cardiologist. The neural networks showed higher sensitivities and discriminant power than both the interpretation program and cardiologist. The sensitivity of the neural networks was 15.5% (95% confidence interval [CI], 12.4 to 18.6) higher than that of the interpretation program compared at a specificity of 95.4% (P<.00001) and 10.5% (95% CI, 7.2 to 13.6) higher than the cardiologist at a specificity of 86.3% (P<.00001). CONCLUSIONS Artificial neural networks can be used to improve automated ECG interpretation for acute myocardial infarction. The networks may be useful as decision support even for the experienced ECG readers.

Direct hospital costs of chest pain patients attending the emergency department: a retrospective study

BackgroundChest pain is one of the most common complaints in the Emergency Department (ED), but the cost of ED chest pain patients is unclear. The aim of this study was to describe the direct hospital costs for unselected chest pain patients attending the emergency department (ED).Methods1,000 consecutive ED visits of patients with chest pain were retrospectively included. Costs directly following the ED visit were retrieved from the hospital economy system.ResultsThe mean cost per patient visit was 26.8 thousand Swedish kronar (kSEK) (median 7.2 kSEK), with admission time accounting for 73% of all costs. Mean cost for patients discharged from the ED was 1.4 kSEK (median 1.3 kSEK), and for patients without ACS admitted 1 day or less 7.6 kSEK (median 6.9 kSEK). The practice in the present study to admit 67% of the patients, of whom only 31% proved to have ACS, was estimated to give a cost per additional life-year saved by hospital admission, compared to theoretical strategy of discharging all patients home, of about 350 kSEK (39 kEUR or 42 kUSD).ConclusionCosts for chest pain patients are large and primarily due to admission time. The present admission practice seems to be cost-effective, but the substantial overadmission indicates that better ED diagnostics and triage could decrease costs considerably.

Evaluation of changes in standard electrocardiographic QRS waveforms recorded from activity-compatible proximal limb lead positions

Proximal limb lead positions are currently used for activity-compatible electrocardiographic monitoring of myocardial ischemia. Two previously described systems for alternate limb lead placement were studied in patients with and without QRS evidence of healed anterior or inferior myocardial infarction. An innovative method was used to simultaneously record 6 standard and 6 modified limb leads, and 3 standard and 3 modified precordial leads on a standard digital electrocardiograph. Both alternate lead placement systems showed rightward frontal plane axis shift and diminished Q-wave durations in lead aVF compared with those of their simultaneous standard controls. Furthermore, potential differences between the standard distal limb lead sites and 5 more proximal sites were explored along each limb. Differences along the left arm were accentuated relative to those along the right arm owing to differences in proximity of the arms to the myocardium. Along the lower limb, and anterior site showed less deviation from standard than did a more lateral site. It is imperative that recordings from alternate sites be labeled accordingly so that their output cannot be confused with that obtained from standard sites.

Quality of planar whole-body bone scan interpretations - a nationwide survey

PurposeThe purpose of this study was to investigate, in a nationwide study, the inter-observer variation and performance in interpretations of bone scans regarding the presence or absence of bone metastases.MethodsBone scan images from 59 patients with breast or prostate cancer, who had undergone scintigraphy due to suspected bone metastatic disease, were studied. The patients were selected to reflect the spectrum of pathology found in everyday clinical work. Whole body images, anterior and posterior views, were sent to all 30 hospitals in Sweden that perform bone scans. Thirty-seven observers from 18 hospitals agreed to participate in the study. They were asked to classify each of the patient studies regarding the presence of bone metastasis, using a four-point scale. Each observer’s classifications were pairwise compared with the classifications made by all the other observers, resulting in 666 pairs of comparisons. The interpretations of the 37 observers were also compared with the final clinical assessment, which was based on follow-up scans and other clinical data.ResultsOn average, two observers agreed on 64% of the bone scan classifications. Kappa values ranged between 0.16 and 0.82, with a mean of 0.48. Sensitivity and specificity for the observers compared with the final clinical assessment were 77% and 96%, respectively, for detecting bone metastases in planar whole-body bone scanning.ConclusionModerate inter-observer agreement was found when observers were compared pairwise. False-negative errors seem to be the major problem in the interpretations of bone scan images, whilst the specificities for the observers were high.

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.

Artificial neural networks for recognition of electrocardiographic lead reversal.

Misplacement of electrodes during the recording of an electrocardiogram (ECG) can cause an incorrect interpretation, misdiagnosis, and subsequent lack of proper treatment. The purpose of this study was twofold: (1) to develop artificial neural networks that yield peak sensitivity for the recognition of right/left arm lead reversal at a very high specificity; and (2) to compare the performances of the networks with those of 2 widely used rule-based interpretation programs. The study was based on 11,009 ECGs recorded in patients at an emergency department using computerized electrocardiographs. Each of the ECGs was used to computationally generate an ECG with right/left arm lead reversal. Neural networks were trained to detect ECGs with right/left arm lead reversal. Different networks and rule-based criteria were used depending on the presence or absence of P waves. The networks and the criteria all showed a very high specificity (99.87% to 100%). The neural networks performed better than the rule-based criteria, both when P waves were present (sensitivity 99.1%) or absent (sensitivity 94.5%). The corresponding sensitivities for the best criteria were 93.9% and 39.3%, respectively. An estimated 300 million ECGs are recorded annually in the world. The majority of these recordings are performed using computerized electrocardiographs, which include algorithms for detection of right/left arm lead reversals. In this study, neural networks performed better than conventional algorithms and the differences in sensitivity could result in 100,000 to 400,000 right/left arm lead reversals being detected by networks but not by conventional interpretation programs.