Franc Jager

Long-term ST database: a reference for the development and evaluation of automated ischaemia detectors and for the study of the dynamics of myocardial ischaemia

The long-term ST database is the result of a multinational research effort. The goal was to develop a challenging and realistic research resource for development and evaluation of automated systems to detect transient ST segment changes in electrocardiograms and for supporting basic research into the mechanisms and dynamics of transient myocardial ischaemia. Twenty-four hour ambulatory ECG records were selected from routine clinical practice settings in the USA and Europe, between 1994 and 2000, on the basic of occurrence of ischaemic and non-ischaemic ST segment changes. Human expert annotators used newly developed annotation protocols and a specially developed interactive graphic editor tool (Semia) that supported paperless editing of annotations and facilitated international co-operation via the Internet. The database contains 86 two- and three-channel 24h annotated ambulatory records from 80 patients and is stored on DVD-ROMs. The database annotation files contain ST segment annotations of transient ischaemic (1155) and heart-rate related ST episodes and annotations of non-ischaemic ST segment events related to postural changes and conduction abnormalities. The database is intended to complement the European Society of Cardiology ST-T database and the MIT-BIH and AHA arrhythmia databases. It provides a comprehensive representation of ‘real-world’ data, with numerous examples of transient ischaemic and non-ischaemic ST segment changes, arrhythmias, conduction abnormalities, axis shifts, noise and artifacts.

Analysis of transient ST segment changes during ambulatory monitoring using the Karhunen-Loeave transform

The authors present an algorithm based on the Karhunen-Loeave transform (KLT) for robust automated detection of ischemic ST segment episodes and measurement of the duration of ischemia in two-channel ambulatory electrocardiographic data. The algorithm operates as a postprocessor to an existing arrhythmia detector. The episode detector incorporates a single-scan trajectory recognition technique in the KLT feature space. The algorithm differentiates between true ischemic ST segment changes and non-ischemic ST deviations caused by axis shifts. In evaluations using the European Society of Cardiology ST-T database, the algorithm achieved a gross ST episode sensitivity of 85.2%, with a positive predictivity of 86.2%. The gross ischemic ST duration sensitivity was 75.8%, with a positive predictivity of 78.0%.<<ETX>>

Performance measures for algorithms to detect transient ischemic ST segment changes

The availability of the European ST-T database makes it possible to perform quantitative, reproducible performance tests of methods for detecting and measuring transient ischemic ST changes in the electrocardiogram (ECG). The authors propose an evaluation protocol and performance measurements for use with the database. They describe methods for evaluating the accuracy of ST episode detection, measurement of ischemia duration, and measurements of ST deviation. Bootstrap estimation is used to derive expected lower bounds on performance measures and to assess their utility as predictors of performance. These methods are illustrated by a case study in which an evaluation is presented of a two-channel algorithm for automated detection of ischemic ST episodes.<<ETX>>

Automated detection of transient ST-segment episodes in 24 h electrocardiograms

A novel automated system is presented for improved detection of transient ischaemic and heart rate-related ST-segment episodes in ‘real-world’ 24 h ambulatory ECG data. Using a combination of traditional time-domain and Karhunen-Loève transform-based approaches, the detector derives QRS complex and ST-segment morphology feature vectors and, by mimicking human examination of feature-vector time series and their trends, tracks the time-varying ST-segment reference level owing to clinically unimportant, non-ischaemic causes, such as slow drifts, axis shifts and conduction changes. The detector estimates the slowly varying ST-segment level trend, identifies step changes in the time series and subtracts the ST-segment reference level thus obtained from the ST-segment level to obtain the ST-segment deviation time series, which are suitable for detection of ST-segment episodes. The detector was developed using the Long-term ST database containing 24h ambulatory ECG records with human-expert annotated transient ischaemic and heart rate-related ST-segment episodes. The average ST episode detection sensitivity/positive predictivity obtained when using the annotations of the annotation protocol B of the database were 78.9%/80.7%. Evaluation of the detector using the European Society of Cardiology ST-T database as a test database showed average ST episode detection sensitivity/positive predictivity of 81.3%/89.2%, which are better performances, comparable with those of the systems being developed using the European database.

Analysis of transient ST segment changes during ambulatory monitoring

The authors describe a two-channel algorithm for robust automated detection of transient ischemic ST changes. The algorithm operates as a post-processor to the ARISTOTLE arrhythmia detector. An ST segment deviation detection function is calculated as the magnitude of the ST segment vector determined from both leads. Using a variety of auxiliary functions, the algorithm distinguishes between transient ischemic ST changes and non-ischemic ST deviations caused by position-related changes in the electrical axis of the heart.<<ETX>>

Characterization of transient ischemic and non-ischemic ST segment changes

Using trend plots of heart rate, Karhunen-Loeve coefficient representations of ST segments and QRS complexes, and time-domain measures of ST level and QRS morphology, the authors have studied temporal patterns of ST change episodes and their associations with heart rate changes and arrhythmias in the European Society of Cardiology ST-T Database. The authors describe methods for discriminating between ischemic and non-ischemic ST episodes, and for interpreting mixed ischemic and non-ischemic ST changes as well as long-term drift of ST level. Further, the authors describe distinctly different temporal patterns in ischemic ST episodes, and the association of heart rate changes and arrhythmias with ischemic ST episodes.

Hardware implementation of a modified delay-coordinate mapping-based QRS complex detection algorithm

We present a modified delay-coordinate mapping-based QRS complex detection algorithm, suitable for hardware implementation. In the original algorithm, the phase-space portrait of an electrocardiogram signal is reconstructed in a two-dimensional plane using the method of delays. Geometrical properties of the obtained phase-space portrait are exploited for QRS complex detection. In our solution, a bandpass filter is used for ECG signal prefiltering and an improved method for detection threshold-level calculation is utilized. We developed the algorithm on the MIT-BIH Arrhythmia Database (sensitivity of 99.82 and positive predictivity of 99.82 ) and tested it on the long-term ST database (sensitivity of 99.72 and positive predictivity of 99.37 ). Our algorithm outperforms several well-known QRS complex detection algorithms, including the original algorithm.

An algorithm for synchronization of in vivo electroporation with ECG

The combined treatment of tumours in which delivery of a chemotherapeutic agent is followed by high voltage electroporation pulses has been termed electrochemotherapy. The electrochemotherapy of tumours located relatively close to the heart muscle can lead to fibrillation of the heart, especially if electroporation pulses are delivered in the vulnerable period of the heart or in coincidence with heart arrhythmias. We built an electroporation pulse delivery algorithm that enables safer use of electrochemotherapy. The algorithm is designed to deliver pulses outside the vulnerable period and to prevent pulses from being generated in the presence of heart arrhythmias. We evaluated the algorithms performance using records of the Long-Term ST Database, thus simulating real-world conditions. The results of the evaluation, a sensitivity of 91.751%, a positive predictivity of 100.000% and a delivery error rate of 8.268% for electroporation pulse delivery (medians), suggest that the algorithm is accurate and appropriate for application in electrochemotherapy of tumours regardless of tumour location.

The Long-Term ST database: a research resource for algorithm development and physiologic studies of transient myocardial ischemia

Presents the Long Term ST Database, a collection of eighty 24-hour two and three lead ECG records from ambulatory subjects with transient ST segment abnormalities. The database provides a comprehensive standard research resource for quantitatively assessing the performance of automated detectors of transient ischemia, and for supporting basic research into the mechanisms and dynamics of transient ischemia. Records of the database contain annotated significant transient ischemic ST episodes, non-ischemic ST episodes caused by heart rate related changes, non-ischemic ST events due to axis shifts or QRS conduction changes, and individual QRS and rhythm annotations, all made by human experts.

A long-term ST database for development and evaluation of ischemia detectors

Reports the status of an ongoing international collaborative research effort to produce a new long term ST database (LTST DB), a collection of seventy annotated ambulatory records containing transient ischemic and non-ischemic ST changes. The authors present the selection criteria for records, an annotation protocol with definitions of transient ST events, interactive graphic tools for manual and automatic annotating, and the annotation procedure.