Miha Amon (2017) . PhD thesis.

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Abstract

The unreliable delineation of transient morphologic changes of ST segments in long-term ambulatory electrocardiogram (ECG) records and the unreliable differentiation between transient ischaemic and non-ischaemic ST segment morphologies are still the major weakness of modern systems for the visual or automatic detection of transient ischaemic episodes. The traditional method for assessing transient changes of ST segment morphologies, which is based on the ST segment level measurement in a single static reference point, is not a sufficiently precise technique, not only due to frequent noises present in the ECG signals, but also because this method does not acquire the morphology of an entire ST segment. A particular problem that has not been suitably resolved as of yet is the variable length of individual segments and waves of individual heartbeats due to variable heart rates. Clinically important segments and waves of the ECG signal can therefore appear in significantly modified time lengths due to changed heart rates.What occurs is that the observed characteristics of segments and waves are clinically equivalent, but the assessment of their morphology is significantly different due to a changed heart rate.The existing methods for ST segment morphology feature extraction are not adaptive with regard to variable heart rates and consequently to variable ST segment lengths. We have developed a new, robust, noise-resistant method for the delineation of transientST segment morphology changes, and a new method for the classification of transient ischaemic and non-ischaemic ST segment episodes, which are based on a foundation of orthogonal transformations that dynamically adapt to the variable heart rate.For this purpose, we have developed a new algorithm which dynamically by resampling and the linear interpolation of the input pattern vectors (ST segments) adapts their length with regard to the value of instantaneous heart rate, and transforms them into pattern vectors with a constant length.The algorithm is based on Bazett’s formula, which is used for estimating the length of the interval of depolarization and repolarization of heart ventricles according to instantaneous heart rate, and on a few expertly determined positions of adaptive reference point of measurement of the ST segment level with regard to several selected values of heart rate.With the aim of establishing an adaptive and noise-resistant extraction and estimation of morphologicfeatures of long-term ECG records, we have developed new basis functions of the Karhunen-Loève orthogonal Transformation (KLT) for the ST segment using a robust covariancematrix construction procedure, which also dynamically adapts to the variable lengthof the input pattern vectors, and a new orthogonal transformation for the ST segment on the basis of the Legendre polynomials (LPT), which also dynamically adapts to the variable length of the input pattern vectors due to variable heart rates.The KLT and LPT transformations yield consistent mutually comparable estimations of the ST segment morphology changes regardless of the changes in heart rate. The obtained morphology feature-vector time series of both transformations further enable expert analysis and the automatic detection and classification of transient ST segment episodes. The developed delineation method enables a long-term representation and characterization of transient ST segment morphology changes on the basis of diagnostic and morphologic feature-vector time series. We present a performance study of the new method for the representation and characterization of transient ischaemic and non-ischaemic ST segment morphology changes using the KLT and LPT transformations.The first three basis functions of the LPT transformation (constant, linear function, and quadratic function) correspond to clinically important morphology categories thatoccur during transient ST segment morphology changes of heart ischaemia (elevation ordepression, slope, and scooping). The LPT transformation thus enables a unique direct insight into the individual time domain categories of the ST segment morphology changes via monitoring only the morphologic feature-vector time series.We have published the new derived KLT and LPT transformation-based morphologic feature-vector time series in the scope of the international reference database LTST DB (Long-Term ST Database) of long-term ECG records, which is freely available on the Physionet website. The method developed for the classification of transient ischaemic and non-ischaemic ST segment episodes is based on the use of the KLT or LPT transformation morphology feature vectors only and does not use other diagnostic feature vectors of the ECG signal.The other advantages of the developed method over the other existing methods are:lower sensitivity to noise, only a stable fiducial point is needed for each heartbeat, there is no need to detect the precise beginning of ST segment for each heartbeat, while the information on the start time of a transient episode is also not needed.In our work we present a study on the performance evaluation of classification of expert-annotated transient ischaemic and non-ischaemic episodes from LTST DB database recordsusing new morphologic feature vectors of the KLT and LPT transformation, and using a few selected standard classifiers.The highest classification accuracy achieved, using the k Nearest Neighbors classifier, kNN (k = 3), obtained by ten-fold cross-validation and ten repetitions, was 91% based on the KLT and 90% based on the LPT, which is comparable and better than the published performances from related studies. The adaptive KLT and LPT orthogonal transformations open new possibilities for a more efficient expert diagnosis and automated analysis. The KLT transformation yields higher results in classification performance between transient ischaemic and non-ischaemic ST segment episodes and indicates the development of new and more powerful automated systems.The LPT transformation yields better results in the delineation of transient ST segment morphology changes and indicates opportunities for the development of new clinical diagnostic criteria for diagnosing transient ischaemia.

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