Comparative study of local and Karhunen-Loève-based ST-T indexes in recordings from human subjects with induced myocardial ischemia

Time-Warping Analysis of the T-Wave Peak-to-End Interval to Quantify Ventricular Repolarization Dispersion During Ischemia

Variations in the dispersion of ventricular repolarization can be quantified by T-wave time-warping based index, dw. However, the early phase of the T-wave can be affected by ST-segment changes during ischemia. We hypothesized that restricting dw to the T-wave peak-to-end ( Tpe) would circumvent this limitation while still quantifying variations in repolarization dispersion. A total of 101 ECG recordings from patients undergoing coronary occlusion, together with their control recordings, were analyzed. A series of dw values was calculated by quantifying the Tpe morphological variations between the T-waves at different occlusion stages and a baseline T-wave. We introduced a normalized version of dw, Rd, reflecting variations of dw during occlusion relative to control recordings ( Rd = 1 corresponds to the same level of variation). The dw series followed a gradually increasing trend with occlusion time, reaching median [range] Rd values of 9.44 [1.01, 80.74] at the occlusion end. Rd at occlusion end was significantly higher than threshold values of 1, 2, 5, and 10 in 94.1%, 85.11%, 64.4% and 48.5% of patients, respectively. The spatial lead-wise analysis of dw showed distinct distributions depending on the occluded artery, suggesting a relation with the ischemia location. The relative variation R with ischemia of index dw (9.4) is greater than that of the T-wave amplitude (7.7), Tpe interval (2.7) and T-wave width (3.0). In conclusion, dw tracks ischemic-induced variations in repolarization dispersion in a more robust manner than classical indexes, avoiding the impact of ST segment elevation/depression or early T-wave distortions, thus warranting further clinical studies.

Quantification of Spatial Heterogeneity of Ventricular Repolarization During Early-Stage Cardiac Ischemia Induced by Coronary Angioplasty

Coronary angioplasty (CA) is a surgical procedure meant to break the plaque and restore the blood flow in obstructed coronary arteries. It is based on inserting an inflatable balloon with a catheter in the clogged artery. When the balloon inflation is prolonged, it also provides an excellent model to investigate the electrophysiological changes due to early ischemia. In this work, we tested whether early cardiac ischemia induced by prolonged balloon inflations might lead to changes in spatial heterogeneity of ventricular repolarization (SHVR), as measured by the V-index on the 12-lead ECG. The metric was recently shown to significantly improve the ECG sensitivity for the diagnosis of non-ST elevation myocardial infarction, in patients presenting to the emergency department. The analysis was retrospectively performed on the data of 104 patients who underwent prolonged CA (STAFF III dataset). The V-index was estimated before, during and post-occlusion (limiting the analysis to the first inflation). Successively, it was quantified on short 90 s overlapping windows, during occlusion, to assess the time evolution of SHVR. V-index values estimated during occlusion were significantly larger (median: 6.2 ms, p <; 0.05) than baseline room values. Also, pre- and post-occlusion values did not differ (p > 0.05), suggesting a complete recovery after CA. SHVR progressively increased during the occlusion with respect to baseline (median reaching 55.6 ms vs 34.2 ms). In conclusion, the V-index detected changes in SHVR due to early-stage cardiac ischemia.

A Review of Automated Methods for Detection of Myocardial Ischemia and Infarction Using Electrocardiogram and Electronic Health Records

There is a growing body of research focusing on automatic detection of ischemia and myocardial infarction (MI) using computer algorithms. In clinical settings, ischemia and MI are diagnosed using electrocardiogram (ECG) recordings as well as medical context including patient symptoms, medical history, and risk factors-information that is often stored in the electronic health records. The ECG signal is inspected to identify changes in the morphology such as ST-segment deviation and T-wave changes. Some of the proposed methods compute similar features automatically while others use nonconventional features such as wavelet coefficients. This review provides an overview of the methods that have been proposed in this area, focusing on their historical evolution, the publicly available datasets that they have used to evaluate their performance, and the details of their algorithms for ECG and EHR analysis. The validation strategies that have been used to evaluate the performance of the proposed methods are also presented. Finally, the paper provides recommendations for future research to address the shortcomings of the currently existing methods and practical considerations to make the proposed technical solutions applicable in clinical practice.

Electrocardiogram ST-Segment Morphology Delineation Method Using Orthogonal Transformations

Differentiation between ischaemic and non-ischaemic transient ST segment events of long term ambulatory electrocardiograms is a persisting weakness in present ischaemia detection systems. Traditional ST segment level measuring is not a sufficiently precise technique due to the single point of measurement and severe noise which is often present. We developed a robust noise resistant orthogonal-transformation based delineation method, which allows tracing the shape of transient ST segment morphology changes from the entire ST segment in terms of diagnostic and morphologic feature-vector time series, and also allows further analysis. For these purposes, we developed a new Legendre Polynomials based Transformation (LPT) of ST segment. Its basis functions have similar shapes to typical transient changes of ST segment morphology categories during myocardial ischaemia (level, slope and scooping), thus providing direct insight into the types of time domain morphology changes through the LPT feature-vector space. We also generated new Karhunen and Lo ève Transformation (KLT) ST segment basis functions using a robust covariance matrix constructed from the ST segment pattern vectors derived from the Long Term ST Database (LTST DB). As for the delineation of significant transient ischaemic and non-ischaemic ST segment episodes, we present a study on the representation of transient ST segment morphology categories, and an evaluation study on the classification power of the KLT- and LPT-based feature vectors to classify between ischaemic and non-ischaemic ST segment episodes of the LTST DB. Classification accuracy using the KLT and LPT feature vectors was 90% and 82%, respectively, when using the k-Nearest Neighbors (k = 3) classifier and 10-fold cross-validation. New sets of feature-vector time series for both transformations were derived for the records of the LTST DB which is freely available on the PhysioNet website and were contributed to the LTST DB. The KLT and LPT present new possibilities for human-expert diagnostics, and for automated ischaemia detection.

Evaluation of ventricular repolarization dispersion during acute myocardial ischemia: spatial and temporal ECG indices

In this work, we studied the evolution of different electrocardiogram (ECG) indices of ventricular repolarization dispersion (VRD) during acute transmural myocardial ischemia in 95 patients undergoing percutaneous coronary intervention (PCI). We studied both temporal indices of VRD (T-VRD), based on the time intervals of the ECG wave, and spatial indices of VRD (S-VRD), based on the eigenvalues of the spatial correlation matrix of the ECG. The T-wave peak-to-end interval I(TPE) index showed statistically significant differences during left anterior descending artery and right coronary artery (RCA) occlusion for almost the complete time course of the PCI procedure with respect to the control recording. Regarding S-VRD indices, we observed statistically significant increases in the ratio of second to the first eigenvalue I(T21), the ratio of the third to the first eigenvalue I(T31) and the T-wave residuum I(TWR) during RCA occlusions. We also found a statistically significant increase in the I(T31) during left circumflex artery occlusions. To evaluate the evolution of VRD indices during acute ischemia, we calculated the relative change parameter R(I) for each index I. Maximal relative changes (R(I)) during acute ischemia were found for the S-VRD indices I(T21), the first eigenvalue I(λ1) and the second eigenvalue I(λ2), with changes 64, 57 and 52 times their baseline range of variation during the control recording, respectively. Also, we found that relative changes with respect to the baseline were higher in patients with T-wave alternans (TWA) than in those without TWA. In conclusion, results suggest that I(TPE) as well as I(T21), I(T31) and I(TWR) are very responsive to dispersion changes induced by ischemia, but with a behavior which very much depends on the occluded artery.

Novel set of vectorcardiographic parameters for the identification of ischemic patients

New signal processing techniques have enabled the use of the vectorcardiogram (VCG) for the detection of cardiac ischemia. Thus, we studied this signal during ventricular depolarization in 80 ischemic patients, before undergoing angioplasty, and 52 healthy subjects with the objective of evaluating the vectorcardiographic difference between both groups so leading to their subsequent classification. For that matter, seven QRS-loop parameters were analyzed, i.e.: (a) Maximum Vector Magnitude; (b) Volume; (c) Planar Area; (d) Maximum Distance between Centroid and Loop; (e) Angle between XY and Optimum Plane; (f) Perimeter and, (g) Area-Perimeter Ratio. For comparison, the conventional ST-Vector Magnitude (ST(VM)) was also calculated. Results indicate that several vectorcardiographic parameters show significant differences between healthy and ischemic subjects. The identification of ischemic patients via discriminant analysis using ST(VM) produced 73.2% Sensitivity (Sens) and 73.9% Specificity (Spec). In our study, the QRS-loop parameter with the best global performance was Volume, which achieved Sens=64.5% and Spec=74.6%. However, when all QRS-loop parameters and ST(VM) were combined, we obtained Sens=88.5% and Spec=92.1%. In conclusion, QRS loop parameters can be accepted as a complement to conventional ST(VM) analysis in the identification of ischemic patients.

ECG-based detection of body position changes using a Laplacian noise model

Body position changes (BPC), which are often manifested in the ECG as shifts in the electrical axis of the heart, result in ST changes, and thus, may be misclassified as ischemic events during ambulatory monitoring. We have developed a BPC detector by modeling shifts as changes in the Karhunen-Loève transform coefficients of the QRS complex and the ST-T waveform. The noise is assumed to have a Laplacian distribution. A generalized likelihood ratio test has been chosen as the strategy to detect BPCs. Two different databases have been used to assess detection performance. The obtained results were 93%/99% in terms of sensitivity/positive predictivity value (S/+PV) and a false alarm rate of 2 events/hour. The results clearly outperform current techniques (S/+PV: 85%/99%) based on the Gaussian noise assumption.

An ECG ambulatory system with mobile embedded architecture for ST-segment analysis

A prototype of a ECG ambulatory system for long term monitoring of ST segment of 3 leads, low power, portability and data storage in solid state memory cards has been developed. The solution presented is based in a mobile embedded architecture of a portable entertainment device used as a tool for storage and processing of bioelectric signals, and a mid-range RISC microcontroller, PIC 16F877, which performs the digitalization and transmission of ECG. The ECG amplifier stage is a low power, unipolar voltage and presents minimal distortion of the phase response of high pass filter in the ST segment. We developed an algorithm that manages access to files through an implementation for FAT32, and the ECG display on the device screen. The records are stored in TXT format for further processing. After the acquisition, the system implemented works as a standard USB mass storage device.

Karhunen-Loève representation distinguishes ST-T wave morphology differences in emergency department chest pain patients with non-ST-elevation myocardial infarction versus nonacute coronary syndrome

Patients presenting to the emergency department with chest pain are triaged to early reperfusion therapies based on their initial 12-lead electrocardiogram (ECG). The standard 12-lead ECG lacks sensitivity to detect acute myocardial infarction (AMI). Electrocardiographic diagnosis of non-ST-elevation myocardial infarction (non-STEMI) is especially difficult and is delayed until cardiac biomarkers turn positive, indicating onset of myocardial necrosis.

STUDY AIMS

The purpose of this analysis was to extract global ST-T waveform features from patients with chest pain, compare these features in patients with and without AMI, and then identify features that distinguish diagnostic categories.

METHODS

This is a secondary analysis of data from the Ischemia Monitoring and Mapping in the Emergency Department in Appropriate Triage and Evaluation of Acute Ischemic Myocardium study, a prospective clinical trial in which patients were attached to Holter monitor devices to obtain 24 hours of continuous ECG data. Digital recordings from 176 patients were analyzed: 88 with AMI (STEMI and non-STEMI) and 88 without AMI or unstable angina. The non-acute coronary syndrome (ACS) group was further subdivided into those with non-ACS cardiac conditions such as heart failure and those without cardiac disease who had noncardiac chest pain. For each patient, 10 consecutive waveforms were obtained within the first 120 minutes of emergency department presentation. The waveforms were time-aligned to the QRS, signal-averaged, baseline-adjusted. ST-T waveforms were complied according to diagnostic category and pooled for further analysis. Eigenvector-lead feature coefficients (Karhunen-Loève [K-L] coefficients) were obtained for each patient by taking the dot product of the ST-T wave (ST segment or entire waveform) and the first 3 common eigenvectors, producing 24 K-L coefficients. Cumulative probability distribution function curves were plotted for each diagnostic category. Statistical significance of category coefficient distribution differences was determined. Multinomial regression was used to assess accuracy of feature coefficients to predict diagnostic category.

RESULTS

Non-STEMI and non-ACS cardiac category K-L coefficient curves were statistically different in 11 of 24 feature curves (P < .001-.047). ST-segment (50 samples) coefficients predicted non-ACS cardiac patients 11.5% more often (P = .02) than those derived from the entire ST-T wave.

CONCLUSION

Patients diagnosed with non-STEMI have distinct distribution of K-L coefficients compared with non-ACS cardiac patients. Coefficients from the first 50 samples of the ST-T wave (ST segment) better predict diagnostic category than do coefficients derived from the entire ST-T wave. Karhunen-Loève coefficient feature analysis may provide early diagnostic information to distinguish patients with non-STEMI vs non-ACS cardiac patients.

The study on separability criteria suitable for cardiac data

There is a need to develop a criterion to measure the class separability of cardiac data before the decision-making logic is applied. This article explores the use of separability criteria to measure the separability of cardiac data. Then a new separability criterion was presented, which was constructed by combining standard deviation with Euclidean center distance. The criterion was applied to extract features of cardiac arrhythmias, measure the performance of the features, and build the decision tree for multiclass classification. The data in this study were obtained from MIT-BIH database. The experimental results show that it is an effective and practical separability measurement criterion.

A pilot study examining the performance of polynomial-modeled ventricular shock electrograms for rhythm discrimination in implantable devices

BACKGROUND

Inappropriate shocks continue to be a problem for patients with implantable defibrillators (ICD). We evaluated the performance of polynomial-modeled ventricular electrograms (EGM) to discriminate between supraventricular tachycardia (SVT) and ventricular tachycardia (VT).

METHODS

Seven sets of EGM from patients having both SVT and VT documented during a single ICD interrogation were included. The cardiac cycle was analyzed off-line in two parts, QR and RQ segments, which were modeled separately using third-order and sixth-order polynomial equations, respectively. These segments were then analyzed to determine which polynomial coefficients were most significant for rhythm discrimination.

RESULTS

When analyzing the QR segment during arrhythmia, there were statistically significant (P<0.05) correlations in 4 of 4 (100%) of the QR coefficients when comparing normal sinus rhythm (NSR) to SVT and 2 of 4 (50%) when comparing NSR to VT or SVT to VT. When analyzing the RQ segment during arrhythmia, there were statistically significant (P<0.05) correlations in 4 of 7 (57%) of the RQ coefficients when comparing NSR to SVT, 5 of 7 (71%) when comparing NSR to VT, and 3 of 7 (43%) when comparing SVT to VT. Using a cutoff value of 50% change from NSR, the ratio of first-order to zero-order QR coefficient was able to completely separate VT from SVT (P=0.03) in this series of patients.

CONCLUSION

Our data demonstrate the feasibility of simple polynomial equations that reproduce the depolarization and repolarization phases of human ventricular shock EGM. The ratio of first-order to zero-order QR coefficient was able to reliably discriminate between SVT and VT while reducing the polynomial model to a first-order system. The results of this pilot trial may serve as the basis for a larger prospective trial implementing a discrimination algorithm for use in low computational power implantable devices.

A segmental polynomial model of ventricular electrograms as a simple and efficient morphology discriminator for implantable devices

BACKGROUND

The goal of this study is to construct a polynomial model of the ventricular electrogram (EGM) that faithfully reproduces the EGM and can be implemented in current, low computational power implantable devices. Such a model of ventricular EGMs is still lacking.

METHODS

New Zealand White rabbits underwent chronic implantation of pacemakers through a left thoracotomy approach. Unipolar ventricular EGMs sampled at a frequency of 1 kHz were stored digitally in 1-minute segments before and after intravenous injection of isoproterenol or procainamide. Each cardiac cycle was divided into a QR and an RQ segment which were modeled separately using a 6th order polynomial equation.

RESULTS

The 14 coefficients of each cardiac cycle were reproducible throughout the baseline recordings (r > or = 0.94, P < 0.002). Isoproterenol caused no changes in the coefficients of the QR segment but significantly altered all but one of the seven coefficients of the RQ segment (p(6)= 0.0039, p(5)= 0.017, p(4)= 0.00007, p(3)= 0.112, p(2)= 0.00016, p(1)= 0.0086, p(a)= 0.00003). Procainamide caused statistically significant changes in both QR segment (p(6)= 0.018, p(5)= 0.287, p(4)= 0.019, p(3)= 0.176, p(2)= 0.016, p(1)= 0.362, p(a)= 0.000044) and RQ segment (p(6)= 0.0028, p(5)= 0.036, p(4)= 0.002, p(3)= 0.058, p(2)= 0.022, p(1)= 0.718, p(a)= 0.0018) coefficients.

CONCLUSION

Our data demonstrate the feasibility of a segmental polynomial equation that reproduces the phases of depolarization and repolarization of the rabbit EGM. This model is reproducible and demonstrates the expected changes with antiarrhythmic drug administration. If reproduced in humans, these findings can have wide applications in patients with implantable devices, ranging from morphologic discrimination of arrhythmias to early detection of metabolic derangements or drug effects.

Non-invasive computerised detection of acute coronary occlusion

The goal of this study was to evaluate the role of a computerised, non-invasive ECG method for detecting acute coronary occlusion (ACO). Ninety-five standard ECG leads were recorded, before and during ACO, from 18 patients undergoing balloon angioplasty. ECG amplitude and derivative parameters were calculated for the ORS, ST and T components of the ECG signal, before and during ACO. Results were obtained for each lead. Sensitivity of the standard visual ECG analysis for detecting ACO was 48%, whereas the percentage of conventional ECG changes during baseline was 14%. For the best ECG parameter, the amplitude parameter of the ORS component, sensitivity was 82%, and the percentage of parameter changes during baseline was 20%. The sensitivity for detecting ACO with five of the six ECG parameters studied was greater than that of the standard visual analysis. Ischaemic changes were detected in 4.3 +/- 1.6 leads per patient using the amplitude parameter of the ORS component, whereas, with the standard visual analysis, 2.5 +/- 2.1 leads demonstrated such changes (p<0.001). Results were then summarized per patient. The standard visual ECG analysis detected ACO in 15 of 18 patients (83%), if at least one lead showed ischaemic changes. The computerised analysis detected ACO in all 18 patients using the same criterion. The sensitivity of the computerised method for detecting ACO in the clinical setting of angioplasty was greater than that of the standard visual analysis. It is suggested that the computerised method may be useful for detecting myocardial ischaemia in other clinical settings of acute myocardial ischaemia.

ECG-based detection of body position changes in ischemia monitoring

The purpose of this paper is to analyze and detect changes in body position (BPC) during electrocardiogram (ECG) recording. These changes are often manifested as shifts in the electrical axis and may be misclassified as ischemic changes during ambulatory monitoring. We investigate two ECG signal processing methods for detecting BPCs. Different schemes for feature extraction are used (spatial and scalar), while preprocessing, trend postprocessing and detection are identical. The spatial approach is based on VCG loop rotation angles and the scalar approach is based on the Karhunen-Loève transform (KLT) coefficients. The methods are evaluated on two different databases: a database with annotated BPCs and the STAFF III database with recordings from rest and during angioplasty-induced ischemia but not including BPCs. The angle-based detector results in performance values of detection probability PD = 95%, false alarm probability PF = 3% in the BPC database and false alarm rate in the STAFF III database in control ECGs during rest RF(c) = 2 h(-1) (episodes per hour) and in ischemia recordings during angioplasty RF(a) = 7 h(-1), whereas the KLT-based detector produces values of PD = 89%, PF = 3%, RF(c) = 4 h(-1), and RF(a) = 11 h(-1), respectively. Including information on noise level in the detection process to reduce the number of false alarms, performance values of PD approximately equal to 90%, PF approximately equal to 1%, RF(c) approximately equal to 1 h(-1) and RF(a) approximately equal to 2 h(-1) are obtained with both methods. It is concluded that reliable detection of BPCs may be achieved using the ECG signal and should work in parallel to ischemia detectors.

Analysis of heart rate variability in the presence of ectopic beats using the heart timing signal

The time-domain signals representing the heart rate variability (HRV) in the presence of an ectopic beat exhibit a sharp transient at the position of the ectopic beat, which corrupts the signal, particularly the power spectral density (PSD) of the HRV. Consequently, there is a need for correction of this type of beat prior to any HRV analysis. This paper deals with the PSD estimation of the HRV by means of the heart timing (HT) signal when ectopic beats are present. These beat occurrence times are modeled from a generalized, continuous time integral pulse frequency modulation model and, from this point of view, a specific method for minimizing the effect of the presence of ectopic beats is presented to work together with the HT signal. By using both, a white noise driven autoregressive model of the HRV signal with artificially introduced ectopic beats and actual heart rate series including ectopic beats, the more usual methods of HRV spectral estimation are compared. Results of the PSD estimation error function of the number of ectopic beats are presented. These results demonstrate that the proposed method has one order of magnitude lower error than usual ectopic beats removal strategies in preserving PSD, thus, this strategy better recovers the original clinical indexes of interest.

Characterisation of acute myocardial ischaemia in a canine model based on principal component analysis of unipolar endocardial electrograms

The study presents a method for identifying endocardial electrical features relevant to local ischaemia detection at rest. The method consists of, first, normalisation of electrograms to a uniform representation; secondly, the use of principal component analysis to reduce the dimensionality of the electrogram vector space; and, thirdly, a search for a classification axis that matches the degree of ischaemia present in the tissue. Left ventricular myocardial states were assessed by echocardiography and NOGA mapping in eight dogs at baseline and then immediately after, 5h after and 3 days after occlusion of the left anterior descending coronary artery. Five principal components were required to approximate electrograms with an average error of less than 10% of the peak-to-peak amplitude. Correlations of 0.77, 0.80 and 0.84 were obtained between the principal component-based parameters and the echocardiography scores at the three ischaemic stages, respectively. Expression of these parameters in the time domain showed that the major changes occurred in the depolarisation segment of the endocardial electrogram as well as in the ST-segment. In conclusion, the proposed method provides a suitable alternative co-ordinate system for the classification of ischaemic regions and highlights signal segments that change as a result of pathology.

Automatic detection of ST-T complex changes on the ECG using filtered RMS difference series: application to ambulatory ischemia monitoring

A new detector is presented which finds changes in the repolarization phase (ST-T complex) of the cardiac cycle. It operates by applying a detection algorithm to the filtered root mean square (rms) series of differences between the beat segment (ST segment or ST-T complex) and an average pattern segment. The detector has been validated using the European ST-T database, which contains ST-T complex episodes manually annotated by cardiologists, resulting in sensitivity/positive predictivity of 85/86%, and 85/76%, for ST segment deviations and ST-T complex changes, respectively. The proposed detector has a performance similar to those which have a more complicated structure. The detector has the advantage of finding both ST segment deviations and entire ST-T complex changes thereby providing a wider characterization of the potential ischemic events. A post-processing stage, based on a cross-correlation analysis for the episodes in the rms series, is presented. With this stage subclinical events with repetitive pattern were found in around 20% of the recordings and improved the performance to 90/85%, and 89/76%, for ST segment and ST-T complex changes, respectively.

Hierarchical state space partitioning with a network self-organising map for the recognition of ST-T segment changes

The problem of maximising the performance of ST-T segment automatic recognition for ischaemia detection is a difficult pattern classification problem. The paper proposes the network self-organising map (NetSOM) model as an enhancement to the Kohonen self-organised map (SOM) model. This model is capable of effectively decomposing complex large-scale pattern classification problems into a number of partitions, each of which is more manageable with a local classification device. The NetSOM attempts to generalize the regularization and ordering potential of the basic SOM from the space of vectors to the space of approximating functions. It becomes a device for the ordering of local experts (i.e. independent neural networks) over its lattice of neurons and for their selection and co-ordination. Each local expert is an independent neural network that is trained and activated under the control of the NetSOM. This method is evaluated with examples from the European ST-T database. The first results obtained after the application of NetSOM to ST-T segment change recognition show a significant improvement in the performance compared with that obtained with monolithic approaches, i.e. with single network types. The basic SOM model has attained an average ischaemic beat sensitivity of 73.6% and an average ischaemic beat predictivity of 68.3%. The work reports and discusses the improvements that have been obtained from the implementation of a NetSOM classification system with both multilayer perceptrons and radial basis function (RBF) networks as local experts for the ST-T segment change problem. Specifically, the NetSOM with multilayer perceptrons (radial basis functions) as local experts has improved the results over the basic SOM to an average ischaemic beat sensitivity of 75.9% (77.7%) and an average ischaemic beat predictivity of 72.5% (74.1%).

Temporal evolution of traditional versus transformed ECG-based indexes in patients with induced myocardial ischemia

The time course of changes in the electrocardiogram as a result of myocardial ischemia induced during prolonged coronary angioplasty has been studied. We have analyzed the electrocardiogram evolution during the occlusion in terms of the Ischemic Changes Sensor, which is a parameter that describes the capacity of different indexes to detect induced changes. Traditional indexes at specific time locations (ST level, T wave amplitude and position, and durations of QT interval and QRS complex) and global indexes (based on the Karhunen-Loève transform as applied to the QRS complex, ST-T complex, ST segment and T wave) have been considered. The global indexes better detected ischemic changes than the traditional indexes. The most sensitive were the index for the ST-T complex (89%) in the Karhunen-Loève transform-derived group and for the ST level (61%) in the traditional group. Changes in the ventricular repolarization period usually appeared earlier (77% of patients) than changes in the depolarization period (23% of patients). A similar percentage of patients exhibited the earliest ischemic changes in the T wave (41%) and in the ST segment (36%). The evolution of the Ischemic Changes Sensor parameters showed that the majority (60%) of the total changes occurred during the first minute of occlusion. The results suggest that the use of global electrocardiogram indexes better reflect ischemic changes than do traditional indexes, such as the ST segment deviation.

Identification of the occluded artery in patients with myocardial ischemia induced by prolonged percutaneous transluminal coronary angioplasty using traditional vs transformed ECG-based indexes

We have studied the spatial properties of ischemic changes as induced by prolonged angioplasty and how the changes are related to different ECG indexes. Indexes based on measurements at specific points in time (ST level at J + 60 ms point, maximal T wave amplitude and position, QT interval, and QRS duration) and global indexes (based on the Karhunen-Loève transform and applied to the QRS complex, ST-T complex, ST segment, and T wave), considering both repolarization and depolarization information, were analyzed. The changes during the occlusion period of the different indexes were used as variables in a multivariate discriminant analysis to determine which indexes showed the best discrimination of the three major occlusion sites (corresponding to LAD, RCA, and LCX coronary arteries). Occlusions in LCX artery were the most difficult to classify. With three local indexes (ST60 level measured in lead V3, T wave amplitude in I, and ST60 in III) it was possible to correctly classify 76% of patients by the occlusion site, and with three KLT-derived indexes (first-order KLT index for ST-T complex in I and for QRS in leads V3 and I) 83% of correct classification was obtained. Using six indexes for local and KLT-derived indexes the correct classification was increased to 85 and 90% of patients, respectively. The use of different ECG indexes (from different intervals) on quasiorthogonal leads permitted the identification of the occluded artery in patients undergoing PTCA and may be extended to more general use.