A. Taddei
G. B. Moody
M. Emdin G. Antolic
A. Smrdel
C. Marchesi
and R. G. Mark
Faculty of Computer & Information Science, University of Ljubljana, Ljubljana, Slovenia
Harvard-MIT Division of Health Sciences & Technology, Cambridge, MA, USA
National Research Council (CNR) Institute of Clinical Physiology, Pisa, Italy
Cardiology Division, Beth Israel Deaconess Medical Center, Boston, MA, USA
Department of Cardiology, University Medical Center, Ljubljana, Slovenia
Department of Systems & Informatics, University of Firenze, Italy
AMBULATORY ELECTROCARDIOGRAPHIC (AECG) and intensive care unit (ICU) monitoring are widely used diagnostic approaches in clinical practice for evaluating patients with suspected or known coronary artery disease. Owing to the long duration of these electrocardiogram (ECG) records, automated detection techniques are required to help in interpretation of relevant clinical events.
Standardised reference ECG databases are important research resources that permit developers of automated detectors and ECG analysers to assess the quality of their instrumentation on the same reference database. Thus the performance of different analysers can be compared. In the early 1980s, the MIT-BIH arrhythmia database [MARK et al., 1982] and the American Heart Association database [HERMES et al., 1981] were released. They made it possible to develop, evaluate and compare reproducibly the quantitative performance of automated arrhythmia detectors.
Another important task during AECG and ICU monitoring is the analysis of transient ST segment and T-wave changes due to myocardial ischaemia. Improvements in recording technology since the early 1980s made it possible to begin analysis of transient ST changes during AECG. Standardising the approach to the detection and interpretation of ST segment and T-wave changes was initiated by a `concerted action' on ambulatory monitoring set up by the European Community in 1985 [MARCHESI, 1986]. The goal was to develop an ECG database as a reference for assessing the quality of AECG analysis systems. Funding from the European Community supported development of an annotation protocol and of a small prototype database.
Development of the database was continued by the joint efforts of the Institute of
Clinical Physiology of the National Research Council (CNR) in Pisa and of the Thoraxcenter
of Erasmus University, in Rotterdam, with the voluntary participation of 13 research groups
from eight countries that provided ECG recordings and contributed to the demanding work
of annotating them. The European Society of Cardiology provided both financial and
scientific backing, so as to enable completion of the European Society of Cardiology
ST-T database (ESC DB) [TADDEI et al., 1992b], which was first released in 1990. It was the
first standard, generally available set of AECG records with documented `significant'
(
100 
V) transient ST segment episodes of depression or elevation and significant
transient T-wave episodes ( 200 V) of depression or elevation.
The ESC DB contains 90 2 h, well-characterised, representative records, with manually annotated transient ST segment (368) and T-wave (401) episodes compatible with myocardial ischaemia. Episodes are annotated in each lead separately, and each heart-beat is also annotated manually in terms of QRS complex onset, beat type, rhythm change and signal quality. The ESC DB promoted further investigations in the analysis of ST-T changes in the ECG and has proven to be an invaluable resource for the development and evaluation of ECG analysers.
During the past few years, it has been a reference for companies developing biomedical
equipment and has stimulated extensive research and publications
[CERUTTI et al., 1992,LAGUNA et al., 1996,PRESEDO et al., 1996a,EMDIN et al., 1997,TADDEI et al., 1997]
[LAGUNA et al., 1997].
Techniques to classify QRS complex morphology were developed and evaluated
[MORABITO et al., 1992,SILIPO et al., 1993,SILIPO et al., 1995a],
and a number of recognition techniques to detect transient ischaemic events automatically
were introduced, including: time-domain analysis
[JAGER et al., 1991,TADDEI et al., 1995,GARCIA et al., 2000],
the Karhunen-Loève Transform (KLT) approach
[JAGER et al., 1992,LAGUNA et al., 1995,GARCIA et al., 1996,JAGER et al., 1998a,SMRDEL and JAGER, 1998],
non-linear principal components [DIAMANTARAS et al., 1996], a variety of neural network
techniques [SILIPO et al., 1995b,SILIPO and MARCHESI, 1996,STAMKOPOULOS et al., 1998,MAGLAVERAS et al., 1998]
and a fuzzy-logic approach [PRESEDO et al., 1996b]. A bilateral project supported by the
CNR, involving the Institute of Clinical Physiology in Pisa, and the Massachusetts
Institute of Technology, in Cambridge, was conducted between 1995 and 1996 to address
standardisation of the analysis of ST-T changes during myocardial ischaemia.
Although the ESC DB represented a major contribution to the research community, the relatively short record lengths presented significant limitations. For example, careful analysis of the ESC DB had revealed intriguing temporal dynamics of transient ischaemic episodes [JAGER et al., 1996a], but their full exploration was prevented by the short record lengths. The ESC DB was found to contain a number of non-ischaemic ST segment changes due to postural changes or slow drift of the ST deviation level [JAGER et al., 1995]. Such non-ischaemic ST segment episodes are quite common in real-world ECG monitoring. They complicate automated analysis of transient ST events and account for many false positive ischaemia detections. The ESC DB does not include a sufficient number of non-ischaemic episodes adequately to test the specificity of automated ischaemia detectors.
The objective of the present study was to create an annotated database of long-term ECG records that would more completely represent the spectrum of real-world ST events. Development of the long-term ST database (LTST DB) began in 1995 with the joint research project `Detection of transient ST segment changes during ambulatory monitoring' v[JAGER et al., 1998b], conducted between the Faculty of Computer & Information Science, University of Ljubljana, Slovenia, and the Massachusetts Institute of Technology, Cambridge, USA. The project was sponsored by the US-Slovenian Science & Technology Joint Fund Secretariat. The project produced an initial LTST DB of 11 annotated two-lead 24 h AECG records. The aim of this database was to support the development and evaluation of ST segment change detectors capable of differentiating between ST episodes compatible with ischaemia and non-ischaemic ST events.
In 1997, Medtronic, Inc. (Minneapolis, USA), agreed to sponsor further development of the database. At that time, research groups from the Institute of Clinical Physiology, in Pisa, the Beth Israel Deaconess Medical Center, in Boston, and University Medical Center, in Ljubljana, joined the project. In 1999, Zymed, Inc. (Camarrilo, USA), became an additional sponsor of the project with a special interest in adding a set of three-lead AECG records to the database. It is important to observe that the LTST DB was not intended as a replacement of the ESC DB, but as a complement. The ESC DB was fully annotated on a beat-by-beat basis, thus supporting evaluation of algorithms for QRS detection in the presence of ST-T abnormalities, in addition to detectors of ST segment and T-wave episodes. On the other hand, the LTST DB is of far greater size, and the annotation methodology was different. Owing to the enormous number of data, it was not practical to annotate the ST segment changes beat-by-beat. The ST segment annotations are based on average waveforms. The goals of the LTST DB are
In previous papers on the LTST DB, we reported our initial approach to the development of
the database [JAGER et al., 1996b], the newly established and continuously updated annotation
protocols, the newly developed annotating tool SEMIA and the status of the database
at that time [JAGER et al., 1998c,JAGER et al., 2000]. In this paper, we present the final design and
construction of the LTST DB. We present sources of AECG records, the selection procedure
and selection criteria for records, the automated preprocessing procedure, the methodology
to determine heart-beat fiducial points, the annotation protocols with definitions of
significant transient ST events, the annotating tools, the annotating procedure using
human expert annotators, the database annotations and the content of the records of the
database.
The records of the LTST DB were selected from Holter recordings obtained in routine clinical practice settings, in the United States and Europe, between 1994 and 2000. The candidate AECG records were chosen from collections of two- and three-lead AECG records at four different sites
The records of the database were selected to model real-world clinical conditions as far as possible and to document significant numbers of ischaemic and non-ischaemic ST events. The selection procedure for the records consisted of the following steps:
![[*]](cross_ref.png)
) by expert Holter
technicians and cardiologists using standard Holter scanners; the digitised data from the
scanner were saved; trend plots of heart rate and ST segment level, together with detailed
hard-copy rhythm strips, were used to select records with ST episodes meeting one or
more of the goals of the project; those records showing episodes of significant ST
deviations were selected for further processing
Each selected record contained one or more of the following features: transient ischaemic ST episodes, transient non-ischaemic ST episodes due to heart rate changes, slow ST level drifts and non-ischaemic ST shifts due to axis shifts or changes in ventricular excitation. Records containing combinations of these features were preferred. Some of the selected records contain atrio-ventricular and intraventricular conduction defects and/or arrhythmias such as atrial and ventricular ectopy, and atrial fibrillation. Other records were selected to include examples of baseline ST displacement resulting from conditions such as hypertension, ventricular dyskinesia and the effects of medications. The cardiologists also selected a number of records from patients with proven transient myocardial ischaemia, such as effort, resting, unstable, mixed and Prinzmetal's angina.
Leads that were felt to be most likely to reveal ST segment changes were generally chosen
at the time of the original Holter recording. Not surprisingly therefore a variety of lead
combinations were used. The leads used in the two-channel records included: precordial leads
V
, V
, V
or V
, together with modified limb lead III (MLIII); or
lead V and lead V; or modified limb lead L2 (ML2) and modified lead V
(MV2). The leads used in the three-channel records included: a combination from leads V,
V, V, V
, II and aVF, or Zymed's EASI lead system with the leads E-S,
A-S and A-I.
Analogue records were made using standard AECG recorders. The analogue output of the playback units was passed through anti-aliasing filters and digitised. The records were digitised at the same site as where they were obtained. As analogue AECG recorders typically preserve frequency content in the signals, typically from close to 0.05 Hz up to 30 Hz (or to 45 Hz in best cases) [BRUEGGEMANN et al., 1991], the records were digitised at 128 or 250 samples per second per channel, depending on the scanning system, and the resolution was 12 bits. There is no significant information to be gained from using a higher sampling frequency for these records. The low frequency cutoff met the AHA [KNOEBEL et al., 1989] and AAMI [AAMI, 1994] recommendations. The scanning systems available and used at the sites were Marquette, ICR, Del Mar Avionics, Oxford Medilog, Remco Italia Cardioline and Zymed.
During the preprocessing phase, which was performed at the central computer facility
site at the Faculty of Computer & Information Science, in Ljubljana, time series of
diagnostic and morphologic features were derived from ECG samples. The signal processing
methodology is summarised in Fig. . The time series were needed later during
the annotation phases and to derive trend plots for selecting the final records of the
database. Initially, the selected records were resampled to a uniform sampling frequency of
250 samples per second per channel, and the amplitude scale was adjusted to 200 ADC units
mV
. To derive morphologic features, we used the KLT, which has been proven to be
useful for shape representation of the ECG morphology [MOODY and MARK, 1990,JAGER et al., 1992,TADDEI et al., 1992a].
Stable fiducial points for heart-beats were generated using the ARISTOTLE arrhythmia detector [MOODY and MARK, 1982] for QRS complex detection and classification. ARISTOTLE places its fiducial point (FP) within the QRS complex region in the `centre of mass' of deflections. In the case of biphasic QRS complex, it is placed close to more significant deflection, whereas, in the case of monophasic QRS complex, it is placed close to a peak of the QRS complex. A stable fiducial point in each heart-beat was a prerequisite for automatic identification of the iso-electric level, calculation of KLT-based ST segment and QRS complex feature vectors, and time-averaging of heart-beats. ARISTOTLE's fiducial point is stable and suitable for our further analysis.
Removal of baseline wander using a cubic spline approximation and subtraction technique
and low-pass filtering by a six-pole Butterworth filter (with a cutoff frequency of
55 Hz) followed. After that, instantaneous heart rate was calculated. Next, the position
of the iso-electric level in each heart-beat and in each ECG lead was defined as the
centre of the `most flat' region in the PQ interval prior to the ARISTOTLE's
fiducial point [JAGER et al., 1991,JAGER, 1994]. After that, the ST level was measured with
respect to the defined iso-electric level at the point FP + 120 ms, if the heart rate
(HR) was less than 100 beats min (or FP + 112 ms if 100 
HR 
110, or
FP + 104 ms if 110 HR 120, or FP + 100 ms if HR 
120) [JAGER, 1994].
Next, abnormal beats and their neighbours were rejected, the KLT-based ST segment and QRS complex morphology feature-vector time series were derived, and noisy beats were rejected. Heart-beats were judged `noisy' if the ST segment or QRS complex KLT feature vector differed sufficiently (mean + 1 SD) from those of the past few (15) normal heart-beats, or if the normalised residual error for the ST segment or for the QRS complex exceeded a certain percentage (25%) when the ST segment or QRS complex was approximated using the first five KLT eigenvectors [JAGER et al., 1992,JAGER, 1994]. The noisy beat detection procedure in the KLT space appeared to be robust and accurate. The percentage of rejected heart-beats was less than approximately 10% in almost all records.
The resulting time series were finally smoothed, resampled and further smoothed. Finally, trend plots of the time series were derived to aid in selecting the final records of the database. Morphologic KLT feature-vector time series for QRS complexes and ST segments allowed accurate visual detection of important, as well as subtle, events in the time series.
The automatically generated iso-electric points and J points during the preprocessing
phase required human editing to improve their accuracy. This was particularly true of
the J points that were estimated using the ARISTOTLE's QRS fiducial points, i.e.
simply 120 ms (or less, depending on heart rate) after the fiducial point. The physician
annotators used SEMIA editing tools (see section 2.8) to interact with the data
at a number of points in the 24 h records and manually to adjust the positions of the
iso-electric level and the J point at the selected times. The flow of data through the
annotation phases is shown in Fig. . The editing points were chosen by
the annotators and were set roughly prior to, at the extrema and at the end of ST episodes;
or, otherwise, approximately every 20 min. Manual adjustment of the positions of the
iso-electric level and the J point was done simultaneously for all ECG leads, using
average heart-beats computed over a 16 s window surrounding the points chosen for
editing.
An automatic post-processing procedure estimated the positions of the iso-electric
level and the J point for the remainder of the clean heart-beats by linearly interpolating
between points of editing. Next, time-averaged heart-beats over 16 s intervals surrounding
each clean heart-beat were computed. The ST level function was then constructed
in each lead using the adjusted iso-electric and J points. ST amplitudes were measured at
J + 80 ms, if HR was less than 100 beats min (or J + 72 ms if 100 HR 110,
or J + 64 ms if 110 HR 120, or J + 60 ms if HR 120). The ST level
functions were then resampled (0.5 samples s), and smoothed (7-point moving
average). Finally, these new ST level functions replaced those derived during the
preprocessing phase and formed the basis for annotating ST events.
The annotation protocol is compatible with that developed for the AHA, MIT-BIH arrhythmia
and ESC databases, but we have extended it to permit more detailed descriptions of
non-ischaemic ST events. The ST events were defined and annotated independently in each
ECG lead to support analysis of each ECG lead independently and also to enable evaluation
of single-lead ischaemia detection algorithms. Electrocardiogram waveform analysis alone
is often inadequate to make an unambiguous diagnosis of myocardial ischaemia and should
not exclusively be relied upon for annotating transient ischaemic ST change episodes.
Therefore our gold standard for annotating transient ischaemic and heart-rate related
ST segment episodes was the expert cardiologists' opinion, based on: their knowledge and
experience, type of change of ST segment waveforms, 24 h context of diagnostic and
morphology parameters, and detailed clinical information from the subjects, including
other clinical investigations and clinical history. The basis for annotating ST events
in each ECG lead was the ST level function (see Fig. ). The ST level
function typically varies widely and significantly in amplitude, owing to drifts, position
changes, changes in conduction, heart-rate related changes and ischaemia.
The annotators defined several classes of ST segment changes
Record annotation began with the establishment of the global-reference annotation in
each ECG lead (refer to Fig. ). It was chosen to be near the beginning of
the record, at a time when the ST level was stable for at least 5 min. All subsequent ST
annotations were referenced to the global reference level. The next step in the annotation
process was manually to track the time-varying ST level, except for deviations due to ischaemia,
non-ischaemic heart-rate related changes in ST morphology and noisy ST events. The tracking
process permitted the human experts to remove from consideration variations in ST level
that could also be significant (50V) but were clinically not important. Annotations
(known as local references) were placed at intervals in the non-ischaemic data and
were connected with straight-line segments to produce the ST reference function.
The algebraic difference between the ST level function and the ST reference function
was the ST deviation function, which clearly identified transient ST deviations from
the local ST reference level, as defined by the annotators. Ischaemic and non-ischaemic
heart-rate related ST episodes were then identified and annotated in the ST deviation
function. To be annotated, a transient ST episode had to be significant,
satisfying the following criteria:
V
or more throughout a continuous
interval of at least 
s
V, provided that
it does not exceed 50V in the following 30 s.
and were used, yielding three different ST
annotation protocols. Protocol A included: = 75 V, and = 30 s;
protocol B: = 100 V, and = 30 s; and protocol C:
= 100 V, and = 60 s. Thus three sets of ST episode annotations
were provided, as differing criteria may be appropriate, depending on the application.
To annotate ST events successfully, the annotators considered ST level and ST deviation
functions, the original ECG signals, the time series of QRS complex and ST segment KLT
coefficients and clinical information about the patients (final diagnosis, other investigations,
patient history). The annotators used SEMIA editing tools to support their analysis.
An example of annotating is shown in Fig. . For the annotation phases, refer
also to Fig. . The ST segment level was tracked in the cases of slow drift
or in the cases of other non-ischaemic changes in ST segment morphology, which had to be
evident by simultaneous change in QRS complex morphology and also evident in the time shape
within a longer period, and may or may not be accompanied by a change in the course of the
QRS complex KLT coefficients. Any significant, sudden step-change of the ST level function
that was accompanied by a simultaneous sudden step-change in QRS complex morphology was
bounded by a local reference before and after the step change and was annotated as
significant axis shift or significant conduction change, according to its
nature.
Significant ST episodes associated with non-ischaemic heart-rate related changes in ST segment morphology (defined above) were annotated as significant heart-rate related ST episodes. Episodes associated with ischaemic changes in ST segment morphology (defined above) were labelled as significant ischaemic ST episodes. Sometimes, significant axis shifts or conduction changes appeared within significant ST episodes. In these cases, they were not tracked out, but were annotated within the episodes. Sometimes, significant ST episodes were caused by noisy ST intervals. Short, noisy episodes were annotated as noisy events at their extrema, and longer noisy periods were annotated as unreadable intervals. Longer intervals with all heart-beats rejected during preprocessing because of noise were also annotated as unreadable intervals.
SEMIA (semi-automatic) is a special-purpose graphic event-driven user interface
and signal-processing tool designed especially for this project [JAGER et al., 1998c,JAGER et al., 2000].
The system is a powerful graphical editing system and was critical to the success of the
annotation process. An abbreviated display of SEMIA's windows is shown in
Fig. . SEMIA's display provides the annotator with a global view
(at different resolutions) of the ST deviation function and heart rate, a close-up view
of individual heart-beat waveforms and a view of the temporal course of KLT coefficient
representations of the QRS complexes and ST segments. SEMIA supports: manual
adjustment of heart-beat fiducial points, manual tracking of the ST reference level,
annotation of significant ST shifts, and manual or automatic annotation of ST episodes
according to selected criteria. SEMIA supported database annotation at different
geographical sites interacting via the Internet and without paper tracings.
After setting local references and annotations indicating significant ST shifts (see
Fig.
True QRS annotations for the selected records of the database were obtained as follows:
Records were rescanned once again (see Fig.
The LTST DB record files are in the WFDB format [MOODY and MARK, 1991]. They contain
detailed clinical information for the patients, waveform data, true QRS annotations
and ST annotations that are easily accessible by the WFDB software. Record files are
summarised in Table
The header file (.hea) describes the format of the signal files (.dat) and contains
technical information about the record (recorder, date and starting time of recording,
leads), comments of expert annotators and a detailed and compact clinical summary for
the patient. The clinical summary includes age, sex, the Holter report on symptoms
during recording, final diagnosis, previous coronary angioplasty or by-pass, and current
medications. Factors that could affect ST-T morphology were also documented, including
known heart disease (coronary heart disease, angina, previous myocardial infarction,
valvular heart disease, left ventricular hypertrophy, cardiomyopathy, AV nodal or
intraventricular conduction delay or block etc.), hypertension, electrolyte abnormalities,
hypercapnoea, hyperventilation, hypotension or anemia. The clinical summary also includes
reports of previous clinical investigations that have been performed (baseline ECG,
stress ECG, thallium positron emission tomography or scintigraphy, stress echo, left
ventricular function echocardiography and coronary arteriography).
ARISTOTLE's QRS annotation file (.ari) contains automatically derived QRS
annotations and QRS complex fiducial points. The true QRS annotation file (.atr) contains
human QRS annotations. The annotation codes used for these two QRS annotation files are
the same as those in the MIT-BIH database [MOODY and MARK, 1991]. The ST segment annotation
files (.sta, .stb, .stc) contain ST segment annotations (see Table
The LTST DB contains 86 AECG records from 80 patients with significant transient ST
events annotated by human experts. There are 68 two-channel recordings and 18 three-channel
recordings. The records vary in duration from approximately 19 h to 26 h. Table
Table
Samples of the LTST DB are available
1,
and the entire database has been published on DVD-ROMs and CD-ROMs
2.
The database also includes a subset of utility files containing diagnostic and morphology
feature-vector time series used during annotating. These files are in text format and
include ST level, ST reference and ST deviation functions of the records, and time series
of ST segment and QRS complex KLT feature vectors. The SEMIA annotation tool,
version 3.0.1, that permits the viewing and examination of feature-vector time series
and database annotations is a part of the database and is available
3.
Transient myocardial ischaemia is an important clinical problem, and it has been
demonstrated that much of it may be asymptomatic, but detectable using the ECG.
AECG recording therefore has a role in the diagnosis and follow-up of at-risk
patients. Automated systems are needed accurately to quantitate ischaemia in AECG
recordings. However, such systems are difficult to design, because of the many
non-ischaemic ST events and artifacts that are present in real-world ECG data.
There is also a need for tools to evaluate the performance of devices that claim
to detect transient ischaemia. The long-term ST database described in this paper
will provide a critically important research resource for algorithm developers
and will also make it possible to evaluate detector performance in a reproducible
manner.
The development of this database was complex, resource intensive, time consuming
and painstaking. The project benefited from the expertise, resources and experience
of the research groups and drew upon experiences obtained during the development of
the previous MIT-BIH, AHA and ESC databases. The semi-automatic interactive graphic
tools were critical to the success of the project. They supported paperless work and
facilitated international co-operation via the Internet. Reviewing and correcting the
annotations after their automatic derivation, instead of fully manually annotating,
proved to be much faster and more convenient for human experts.
It is important to emphasize that the LTST DB is not intended as a replacement for
the ESC database
4,
or MIT-BIH
5or AHA databases
6.
Its goals are different.
The LTST DB fills a gap in the scope of previously published databases. The MIT-BIH
7and AHA
8databases are intended for evaluating arrhythmia and ventricular arrhythmia detectors.
The ESC DB
9contains 2 h ambulatory records and is annotated beat-by-beat in terms of QRS onset,
beat type, arrhythmias and ST segment and T-wave changes. It is intended for evaluating
detectors of transient ST segment and T-wave changes, as well as for testing QRS detectors
in the presence of ST-T abnormalities.
The LTST DB contains long-term ambulatory records and ST segment measurements obtained
on average waveforms. What we hoped to accomplish was to represent better the wide variety
of real-world data, including many examples of ischaemic and mixtures of non-ischaemic ST
events. The LTST DB will support the development and evaluation of the performance of
algorithms to detect transient ischaemic and non-ischaemic ST segment changes. It will
also support researchers studying lengthy examples of quasi-periodic and other temporal
patterns of ST change and enable basic studies in the dynamics of mechanisms responsible
for ischaemia.
Acknowledgements - The authors wish to thank Robert Stadler, PhD, Shannon
Nelson, BSc, and Lee Stylos, PhD, from Medtronic, Inc., in Mineapolis, and Dirk Feild,
PhD, from Zymed, Inc., in Camarrilo (now at the Philips Medical Systems in Oxnard), for
their sincere interest in this project and financial support. They wish to thank Dirk Feild,
PhD, also for contributing the three-channel records. They are particularly indebted to
Peter Stone, MD, and Gail McCallum for their assistance in accessing a number of Holter
recordings from the ACIP Core Laboratory at the Brigham and Womens Hospital in Boston.
The authors thank Diane Perry at the Beth Israel Deaconess Medical Center in Boston for
digitising the records and editing individual QRS annotations, and Sharon Stevens at the
Philips Medical Systems in Oxnard for editing individual QRS annotations as well. They
thank Isaac Henry, MSc, from the Beth Israel Deaconess Medical Center in Boston, for
managing QRS annotation files, and further thank many of those who contributed to the
project: Wei Zong, PhD, and Ramakrishna Mukamala, PhD, from the Massachusetts Institute
of Technology, in Cambridge; Maurizio Varanini, BSc, and Simone Bordigiago, BSc, from the
Institute of Clinical Physiology, in Pisa; and Boris Glavic, BSc, Mitja Zabukovec, BSc,
and Maja Škrjanc, BSc, from the Faculty of Computer and Information Science in Ljubljana.
), the expert cardiologists of the team R.G. MARK, M. EMDIN,
and G. ANTOLIŠ
2.10 True QRS annotations
) by two independent Holter
technicians, one using a Marquette Holter scanner and the other using a Zymed Holter scanner.
Each of the Holter technicians identified all QRS complexes in each record during scanning
and manually corrected the type of those QRS complexes that were falsely classified by the
scanner. The output of the scanners was QRS annotation streams containing fiducial points
of QRS complexes and QRS annotations according to their beat types. The two QRS annotation
streams for each selected record were then merged together beat-by-beat into one annotation
stream using the BXB program of the WFDB utility software [MOODY and MARK, 1991]. The program
keeps both QRS annotations for an individual QRS complex, if the QRS annotations from the
two annotation streams for this QRS complex differ. Discrepancies in the individual QRS
annotations were then adjudicated manually by an expert cardiologist using the WAVE
tool of the WFDB.
3 Results
3.1 Database annotations
.
)
according to annotation protocols A, B and C. The numbers of ST episodes, as determined
by each of the three sets of criteria, and the number of significant ST shifts are summarised
in the (.cnt) text file. The ST segment measurements file (.16a) contains measurements
obtained on average heart-beats comprising clean heart-beats (those that passed the
preprocessing phase) in 16 s averaging windows. An annotation contains: the value of the
ST level function for that average heart-beat; ST segment amplitude measurements at the
points: J + 0 ms, J + 20 ms, J + 40 ms, J + 60 ms, J + 80 ms, J + 100 ms and J + 120 ms;
positions of the iso-electric level and J point relative to the QRS fiducial point for this
average beat; and the number of heart-beats left and right of the centre heart-beat included
in the corresponding average beat.
3.2 Database records
summarises the content of the records with diagnoses and numbers of annotated ST events
according to protocol A. The subjects were 46 men, aged from 44 to 85 years, and 29 women,
aged from 23 to 87 years, for five subjects, sex and age data are not available. Each record
contains significant ST events of some type. Transient ST segment episodes were counted in
each ECG lead separately, as annotated, and in the sense of combined ST annotation streams.
Column ST/I in Table summarises the numbers of combined ST change episodes
and combined ischaemic ST episodes. Combined ST change episodes are those obtained by
merging the ST episode annotations of ischaemic ST episodes and of heart-rate related ST
episodes from the simultaneous leads into one single ST annotation stream, regardless of the
type of ST episode, i.e. a combined ST change episode occurs if an episode of any type occurs
in any lead. Combined ischaemic ST episodes are those obtained by merging the ST episode
annotations of ischaemic ST episodes only into one single ST annotation stream, i.e. a
combined ischaemic ST episode occurs if an ischaemic ST episode occurs in any lead.
summarises the overall numbers of annotated ST segment episodes and
their durations for the annotation protocols A, B and C. The total gross database duration
is 1991:50:49 (1991 h, 50 min and 49 s), and the average duration of records is 23:09:40.
According to protocol A, the LTST DB contains 856 true ischaemic ST episodes in lead 0,
786 episodes in lead 1 and 153 episodes in lead 2. Combining the ischaemic and heart-rate
related ST episode annotation streams from the simultaneous leads yields 1490 combined ST
change episodes of total duration of 200:22:42 (average episode duration 0:08:04), whereas
combining ischaemic ST episode annotation streams yields 1155 combined ischaemic ST
episodes of total duration of 151:40:12 (average episode duration 0:07:53).
4 Discussion
References
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