STAR algorithm.pdf

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ST/AR Algorithm

ST/AR ST MonitoringApplication Note

This appli cati on note explains the applicati on of ST

segment monitoring. I t also provides a detailed

descripti on of the ST segment monitoring algori thm

implemented in the multi -lead ST/AR (ST andArrhythmi a) algori thm, and an assessment of the ST

segment monitoring algori thms performance.

IntroductionRoutine electrocardiographic monitoring is standardpractice in coronary and intensive care units,emergency rooms, ambulatory monitoring settings,and operating rooms. Until recently, the focus ofECG monitoring has been directed at the

measurement of heart rate and the detection ofarrhythmia. Now, with the development ofcontinuous ST segment monitoring, part of this

routine ECG monitoring can be used to detect STchanges which may be ischemic episodes.

Ischemia detection has always been an importantcomponent in identifying and managing patientswith coronary artery disease. Several recent advancesin cardiac patient management, such as earlyreperfusion with thrombolytic therapy andrevascularization with PTCA procedure, have madecontinuous non-invasive detection of ischemia evenmore important. The emphasis has shifted fromdetecting and diagnosing coronary artery disease tothe on-line monitoring and treatment of evolving

ischemia.

Although monitoring ECG for ST segmentdeviation is not the most sensitive and specifictechnique for myocardial ischemia detection, itremains the only practical technique for continuousnon-invasive monitoring of ischemic episodes.

Myocardial Ischemia

The term ischemia refers to a reduction in thesupply of oxygenated blood to the cells of an organ.

Ischemia occurs when the arterial conduit becomeslimited in its abil ity to feed tissues with sufficientoxygen to meet the metabolic requirements. Theimpact of ischemia depends upon the duration andlocation of the tissue cells.

Organs such as the heart and brain are mostvulnerable to reduced oxygenation or ischemia due


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to their high extraction of oxygen. The heart isvulnerable to infarction as it has few collateral bloodvessels to oxygenate tissue if the primary arterialconduits are narrowed or blocked for an extendedtime.

The main cause of myocardial ischemia is coronaryartery disease. More specifically, the principalcontributors to reduced myocardial oxygenation are

thought to be: Reduced vessel size (narrowing) due to increased

calcification of plaques within the artery.

Spasm of the muscular-walled artery.

Situations that cause ischemia, and that withoutintervention are likely to lead to myocardialinfarction are:

Migration of a blood clot into the coronary artery.

Rupture of an arterial plaque which releasesthrombogenic fluid directly into the coronary

artery.

Decreased oxygen supply, either as decreased bloodflow or lack of oxygen in the blood, results indelayed or abnormal electrical characteristics duringthe repolarization phase of the myocardial cells. Thisis displayed as changes in the level of the STsegment.

The effects of ischemia are reversible if the episode islimited in time. Many ischemic events are self-limiting and are caused by an increased demand forblood from an artery that is unable to respond. For

example, if an episode is caused by increased physicalor emotional stress, relieving that stress limits itsduration. When an episode remains unrelieved,tissue cells begin to die and a myocardial infarctionresults.

Silent Ischemia

In many cases, myocardial ischemia episodes areaccompanied by chest pain (angina). However,ischemia may also be present in many situations in

which chest pain is absent (silent ischemia). It hasbeen shown that silent ischemia has the samecharacteristic changes as those of symptomaticepisodes, except that chest pain is not present. Manyrecent studies have shown that the majority ofischemic episodes in patients with symptomaticcoronary artery disease are actually not associatedwith angina.

Although several mechanisms have been proposed,the link between pain and transient myocardialischemia is still not well understood. Data areemerging that show that both painful or silent

ischemic episodes in patients, whether with stableangina, unstable angina, or after myocardial

infarction, are a major independent sign of increasedrisk for subsequent major cardiac events.

Therefore, myocardial ischemia is now considered asan entity composed of both painful and silentepisodes (total ischemic burden). It has beenadvocated that therapy for the patient with coronaryartery disease should be the total eradication of allischemic episodes whether symptomatic (painful) or

asymptomatic (painless). Because angina can nolonger be considered a reliable indicator of ischemia,noninvasive techniques assume a pivotal role in thedetection of ischemic episodes.

Detection of Myocardial Ischemia

Technologic advances during the past decade havepresented clinicians with a diversity of techniques forthe detection of myocardial ischemia in both clinicalpractice and research. Each technique measuressome aspect of abnormal function seen during the

development of ischemia.

Inadequate perfusion can be detected by myocardialredistribution scintigraphy. The presence ofanaerobic metabolism can be detected usingpositron emission tomography by detecting amismatch between myocardial perfusion andmetabolism function. Diminished myocardial wallcontraction can be detected by either radionuclideventriculography or two-dimensionalechocardiography. The electrophysiologic changesduring ischemia can be detected as ST segment

changes in the ECG signal.Continuous ST SegmentMonitoringAlthough monitoring ECG for ST segmentdeviation is not the most sensitive and specifictechnique for myocardial ischemia detection, it hasbeen the most utilized technique for ischemiadetection in the operating room and ICU/CCUbecause of its availability, low cost, and ease of use. Itis also the only detection technique that enables thedetection and documentation of all episodes of

ischemia as reflected by ST segment changes,whether painful or silent.

When monitoring of the ST segment indicatesischemic activity, a 12-lead ECG is commonlyperformed to confirm the episode.

The ST Segment

After ventricular depolarization, normal myocardialcells are at nearly the same potential. Therefore inthe absence of any cardiac pathology, the end of

depolarization and the beginning of repolarizationare normally isoelectric; this region is called the STsegment. On the ECG signal, the ST segment is


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defined as the region between the end of the S-wave,also called the J-point, and the beginning of the T-wave.

Ischemic and damaged tissue causes the cells of themyocardium to become either more or less excitable.This change is most apparent in the ECG during therepolarization phase. The ST segment of the ECGprimarily reflects ventricular repolarization, and

therefore ST depression or elevation reflects thisischemia or damage.

Figure 1 The ST Segment

The location of the monitoring electrodes and thedirection and magnitude of the ST change indicatethe area of the heart at risk, and the possible extentof the damage. The probability of detecting ischemicepisodes, and locating them, increases with thenumber of leads employed, the appropriate choice of

ECG leads, and correct lead placement.

Measuring ST Segment

The current standard of determining the STsegment measurement is by measuring the voltagedifference between the value at a point 60 or 80milliseconds after the J-point and the isoelectricbaseline. The isoelectric baseline is either betweenthe P- and Q-waves (the P-R interval) or in front ofthe P-wave (the T-P interval).

Figure 2 ST Segment Measurement

ST segment measurement values are reported ineither millivolts, microvolts or millimeters, becausethe standard diagnostic ECG strips are plotted at ascale of ten millimeters per millivolt. Thus onemillimeter represents 0.1 millivolt. A positive valuerepresents an ST elevation (Figure 3), and a negativevalue represents an ST depression (Figure 4).

Figure 3 ST Elevation

Figure 4 ST Depression

Although there is no standard for ECG diagnosis ofmyocardial ischemia, ST segment changes of greaterthan one millimeter are generally consideredsignificant.

When the ST segment is measured at or near the J-point, it is important to differentiate betweenchanges that are significant and those that may be

affected by the heart rate. It is therefore necessary togive a description of the ST segment slope.

The slope of the ST segment is often described ashorizontal, or downsloping, both of which aresignificant, or upsloping which is not significantand is possibly influenced by heart rate.(Figure 5)

Figure 5 ST Segment Slope

P

Q

R-WAVE PEAK

S

T

ST

SEGMENT

J-POINT

R

BEGINNING

OF T

ST Value

P

Q

R-WAVE PEAK

S

T

ISOELECTRIC

POINT

ST

MEASUREMENT

J-POINT

ST VALUE

POINT

Abnormal

Abnormal Normal

Worse


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Indications of ST Segment Monitoring

Although further clinical experience is still necessaryto identify areas of potential application, there areseveral areas where continuous ST segmentmonitoring is clearly indicated, including:

Ruling out myocardial infarction.

Evaluating patients with known or suspected

coronary artery disease. Evaluating reperfusion after thrombolytic therapy.

Evaluating post-MI ischemia.

Evaluating reocclusion post-angioplasty oratherectomy.

Evaluating myocardial ischemia as the cause ofrespiratory failure.

Evaluating intra- and post-operative ischemia forcardiac and high risk surgical procedures.

ST Segment Changes That Are Not IschemicRelated

Low specificity is a major limitation of ECG as anischemia monitor. Multiple non-ischemic causes ofECG changes can mimic ischemia, such as:

Body position changes

Drug effects (such as digitalis and diuretics)

Electrolyte imbalances (such as hypokalemia)

Conduction disturbances (including LBBB and

WPW syndrome) Hypothermia

Left ventricular hypertrophy

Ventricular pacing

Old infarcts

Because ST segments may be altered by manydifferent conditions, it is imperative that cliniciansinterpret the ST information in the context of otherclinical information.

The ST Segment MonitoringAlgorithm

ST segment monitoring algorithm is designed toprovide continuous ST measurement, whileproviding continuous patient surveillance and alarmgeneration for both selected and derived leads. Thetotal number of ST measurements generated by theST/AR algorithm depends on the ECG lead-set andthe type of monitoring device used for monitoring.Please refer to the Instructions for Use for themonitoring device for the specific number of STmeasurements supported by the monitor and thelead set.

When a 5-wire lead set is used in standard electrodeplacement, up to three user- selected ECG leads areprocessed for direct ST segment measurement. Forlimb leads that are not measured directly, ST valuesare derived if possible. In addition, if either the V-lead or the MCL lead is included for direct STmeasurement, the ST measurement for other leadswill be derived if possible. Thus, using a 5-wire leadset in standard lead placement, a maximum of 8 ST

values including 6 limb leads, 1 unipolar chest lead(V) and 1 bipolar chest lead (M CL) can begenerated.

When EASITM lead placement is used, the direct STmeasurements are performed first on the directlyacquired EASITM leads (see application note: 12-Lead monitoring using EASITM Lead System Pub#5980-1198E). Subsequently, ST values aredetermined for all derived 12 leads.

Note: EASITM derived 12-Lead ECGs and theirmeasurements are approximations to conventional

12-Lead ECGs, and should not be used fordiagnostic interpretations.

For conventional 12-lead ECG, direct STmeasurements are performed on two limb leads andall six chest leads first. Other limb leads that are notdirectly measured are derived.

The ST algorithm uses the beat detection andclassification information provided by thearrhythmia algorithm when arrhythmia analysis ison, and performs several additional actions on theECG waveforms, including filtering the signals,measuring ST values, producing a representativeQRS complex, and generating ST alarms ifnecessary.

Basic Algorithm Concept

The ST segment is a signal of low amplitude and lowfrequency content. It is a parameter that is difficultto measure accurately due to distortion introducedby muscle noise and baseline wander. However, sinceST segment values do not change very rapidly, notevery single beat needs to be measured. Therefore,

reliable ST measurements can be extracted usingsignal averaging techniques.

One common approach is to derive a singlerepresentative beat through waveform averaging.The ST segment value is then measured from thisrepresentative beat. One potential problem with thistechnique is that noisy or abnormal beats, if notrejected completely from the averaging process, cancorrupt the final averaged waveform and thus causeinaccurate ST measurement.

In order to reduce the impact of an undesired beat,

some products use an averaging technique calledincremental updating to compute the averaged beat.Only a pre-defined fixed amount of changes to the


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averaged beat is allowed for every new beat to beaveraged. One disadvantage of this approach is thatthe resulting averaged waveform may not resembleany of the original QRS complexes.

ST/AR ST Algorithm Approach

In order to overcome these problems, ST/AR takes adifferent approach. Rather than trying to derive an

averaged beat with each new beat for measuring STvalues, all complexes detected in a discrete timeperiod, 15 seconds, are saved. A representative beatis then derived from these beats using a statisticaltechnique. The ST value associated with therepresentative complex is used as the final ST value.

The 15-second time interval used for ST segmentmeasurement has been selected to ensure that it islong enough to include a sufficient number of beatsat various heart rates to produce a stable STmeasurement, yet short enough so that ST segment

changes of short duration will not be missed.

ST Segment Analysis

Step 1: ECG Signal Filtering

The patient's incoming ECG waveforms are digitallysampled at 500 samples/second. For ST segmentanalysis where a high sampling rate is not needed, alower sampling rate of 250 (125 for M300A andM3001A Measurement Server) samples/second isused.

To ensure that the ST segment can be measuredaccurately, the incoming ECG signals must have alow-end bandwidth of 0.05 Hz. This low bandwidthis necessary to ensure that no signal distortion isintroduced to the ST segment. ST segmentmeasurement will not be performed if this conditionis not met.

An additional ST filter with a higher low-endbandwidth of 0.67 H z is used to further removeunwanted baseline noise. Although this filter has abandwidth higher than 0.05 Hz, it is designed not tointroduce any distortion to the ST segment.

Step 2: Saving QRS Complexes

Next, all QRS complexes detected by the beatdetection algorithm within a discrete 15-secondperiod are saved for subsequent ST segment analysis.

QRS complexes that are classified by the arrhythmiaalgorithm as PVC, questionable, ventricularly paced,or AV sequentially paced are excluded from the STsegment analysis. If arrhythmia analysis is notselected, beats that are premature or display aventricular pacer spike are excluded.

In addition, for each lead saved, checks are made tomake sure that any measurement points, lead, and

bandwidth changes did not occur during this15-second period. If any of these conditions ispresent, ST measurement for that lead will not beperformed.

Step 3: Pre-processing of MeasurementPoints

The ST segment value is measured based on two

user-adjustable measurement points: the isoelectricpoint and the J-point. The ST segment is measuredat either 60 or 80 milliseconds after the J-point,depending on the user configuration and/orselection.

Rather than using the same isoelectric and STmeasurement points to perform ST measurementfor each beat, the points are shifted left or right asnecessary to avoid noise in the measurement regions.The maximum amount of shifting allowed is limitedto 20 milliseconds.

Step 4: Rejecting Beats with UnstableIsoelectric Baseline

In order for accurate ST values to be derived, beats

with unstable isoelectric baseline are also excluded

from subsequent ST segment analysis.

For each lead, the median isoelectric value for all the

beats in the 15-second period is derived. The median

value for a group of measurements is defined as the

value selected from the group such that half of the

measurements are larger than this value and half of

the measurements are smaller.

The isoelectric value for each beat is then compared

to the median value. Any beat with an isoelectric

value that deviates more than 2.5 millimeters from

the median value is excluded from further ST

analysis.

This additional step further eliminates beats with

noise that could corrupt the final ST measurement.

Step 5: Statistical Analysis of STMeasurement

The ST algorithm then performs a statistical analysisof the distribution of all the individual ST segmentvalues from the remaining beats. If the ECG signalbeing monitored is stable and ST can be reliablymeasured, these individual ST segmentmeasurements will form a bell-shaped normaldistribution and an ST value and a representativecomplex will be generated.

The advantage of this technique is that noisy beatsand incorrectly classified PVCs will not contaminatethe representative beat for ST measurement. Thus,the algorithm discards statistically irrelevant data

while focusing on the beat and associated ST valuewhich is clinically relevant.


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The median value for all the individual ST segmentmeasurements is determined. In order to make surethat the selected median value is not just an isolatedpoint in the distribution, the algorithm furtherexamines a group of five beats. These five beatsinclude the beat with the median value, two beatswith the smallest ST measurements greater than themedian value, and two beats with the largest STmeasurements less than the median value. In order

to produce a valid ST value, the difference betweenthe highest and the lowest ST values within thegroup must be less than or equal to 0.5 millimeters,and at least three out of these five beats must havevery similar morphology.

Finally, all beats with similar morphology from thisgroup are averaged to produce a representative STwaveform. The ST segment measurement associatedwith this representative beat is used as the ST valuefor this 15-second time period.

Step 6: Computing Derived ST Values andWaveforms

When a 5-wire lead set in standard placement orconventional 12 lead placement is used, ST valuesand waveforms for limb leads which are notincluded in the original user-selected leads arederived if possible. A total of four additional limbleads can be derived if ST values and waveformsfrom any two limb leads are available. In addition,for a 5-wire lead set in standard position, if either theV-lead or the MCL lead is directly measured, theother lead will be derived if possible.

When monitoring with EASITM lead placement, all12 leads are derived.

In addition to the ST measurements, the ST Index(STindex) is calculated when possible. The ST indexis a summation of the absolute values of three STsegment measurements, using leads that can bestindicate ST changes in the different locations of theheart.

- Anterior lead - V2- Lateral lead - V5

- Inferior lead - aVFSince the ST index is based on absolute vales, it isalways a positive number.

Step 7: Generating One-Minute ST Valuesand Waveforms

In addition to the 15-second ST values andassociated waveforms for real-time display, the STalgorithm also provides ST values and associated STwaveforms at one-minute intervals for databasestorage and recall. The one-minute ST value and

waveform are selected from the four 15-second STvalues and waveforms within that minute. The value

which shows the largest deviation from the previousone-minute ST values is selected.

Alarm DetectionThe ST monitoring algorithm supports thedetection of ST alarm conditions for each leadseparately. However the generation of ST segmentalarms can vary between monitoring product. Pleaserefer to the Instructions For Use manual for specific

information about how ST alarms are generated onyour system.

For individual ST lead alarms, the ST high and lowalarm limits can also be set independently for eachlead.

For ST multi-lead alarms, a multi-lead ST alarm isannounced when two or more ST leads exceed adefined set of lead limits.

For automatic alarm limits, the high low limits canbe applied to all three leads used for ST segment

monitoring and are set around the patients currentST value. See application note: Automatic AlarmLimits Pub# 5968-6215E.

For smart alarms, the high and low limits are setaround the patients current ST value. They can beapplied when ST monitoring is started and reappliedas necessary.

ST values must exceed the alarm limit for fourconsecutive 15-second time periods (1 minute)before an alarm is declared.

Special Concerns forComputerized ST SegmentMonitoringIt is impossible to design a computerized STsegment monitoring algorithm that accuratelyanalyzes 100% of all patients. In the followingsections, several conditions that can cause difficultyfor the ST segment monitoring algorithm aredescribed.

ECG Signal Quality

A clean signal is integral to accurate ST segmentmonitoring. It is important to minimize or eliminatefactors that create electrical noise baseline wander,muscle art ifact, or 60 Hz interference.

Lead Selection

It is mandatory to standardize lead location.Seemingly insignificant errors in lead location canhave a profound effect on the resulting ECG whichcan impact the final interpretation.

The benefit of ST monitoring is the ability of thesystem to detect episodes of myocardial ischemiaindependent of patient symptoms. Therefore, it is


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important to select the leads most likely to detect STsegment changes during the ischemic episode.

Arrhythmia Algorithm Accuracy

When arrhythmia analysis is on, beat labeling fromthe arrhythmia algorithm is used in the STalgorithm to reject beats that should not bemeasured. Incorrect beat labeling by the arrhythmiaalgorithm can influence the accuracy of the STmeasurement algorithm. Some of the conditionsthat can cause the arrhythmia algorithm to performpoorly include: large P- and T-waves, changing QRSmorphology, and noisy signal. In order to obtainaccurate ST measurement, factors that affect thearrhythmia monitoring performance should becorrected.

Even when arrhythmia analysis is off, the STalgorithm is able to maintain its measurementaccuracy by rejecting short runs of ventricular beats

and isolated PVCs. However, the presence ofsustained runs of ventricular beats will influence theaccuracy of the ST measurement algorithm. This iseasily identified in the ST trends as a step change asopposed to a gradual change for a true ischemicevent

Atrial Fibrillation and Flutter

In some cases of atrial dysrhythmias, the erraticbaseline caused by fibrillations and flutters makes itdifficult to achieve reliable ST measurement. In suchcases, leads with the least amount of erratic baselineshould be selected. However, this may not eliminatethe problem completely. These types of waveformsremain a major challenge for the ST algorithm.

Incorrect Measurement Points

The isoelectric point establishes the baseline againstwhich the ST measurement point is compared. Forproper operation, the isoelectric point should be setwithin the P-R or T-P interval, and the ST

measurement point should be set at 60 or 80milliseconds after the J-point.

Artifactual ST segment depression or elevation

occurs when the isoelectric point or the ST

measurement point is set incorrectly. It is important

that these measurement points be set correctly when

monitoring is initiated. In addition, they should also

be reassessed periodically and readjusted if

necessary.

Heart Rate Changes

While nominal variation of the heart rate should notaffect the ST measurement, errors can occur if thereis a significant increase in the heart rate. When the

heart rate increases, ventricular repolarization, asmeasured by the QT interval, becomes shorter.Because the algorithm measures the ST segment at afixed interval from the J-point, QT shortening cancause the ST measurement point to fall within theT-wave. When this occurs, the location of themeasurement point should be checked andreadjusted if necessary.

Assessing ST AlgorithmPerformanceThe performance of the algorithm used to detect STsegment deviation is fundamental to theeffectiveness of computerized ST segmentmonitoring. It is important that the algorithmenable the system to alert the clinical staff to true STsegment episodes without unnecessarily distractingthem from their duties. Such an algorithm will allowthe monitoring system to generate accuratecumulative data that will be useful in supporting

therapeutic decisions.

Reference Database

The European Society of Cardiology (ESC ST-T)database is available to the public for the testing ofST segment monitoring algorithms. This databaseconsists of records of patient ECG waveforms,together with a set of annotation files in which STsegment changes have been identified by expertcardiologists.

The ESC ST-T database has been used to assess theperformance of the ST segment monitoringalgorithm. The database consists of data from 90patients and was developed for the purpose ofevaluating ST segment analysis algorithms.

Using the ESC ST-T Database

It should be noted that the ESC ST-T databaseprovides only a general idea of the performance of anST segment monitoring algorithm. The accuracy ofthe performance measures is limited due to the

following characteristics of the database: The database annotates only the peak deviation of

each ST episode.

The peak ST value annotations are measured asthe difference between the actual value and aninitial value selected from the beginning of therecord.

The measurements in the database have aresolution of 0.5 millimeters for values less than5.0 millimeters, and a resolution of 1.0millimeters for values greater than 5.0 millimeters.


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ST Segment Performance ValidationTest

The ST Segment Performance Validation Testquantifies the performance of the ST segmentmonitoring algorithm.

Performance Measures

The performance measures quantify the extent towhich the algorithm results agree with the expertannotation of a given patient record. Themeasurement of each ST segment in the referencedatabase is called thetrue measurement of that ST.

The performance measures used to measure STsegment measurement accuracy include thefollowing:

TheMeasurement Correlation Coefficient is astatistical measurement of how well the algorithmsgenerated values and the true measurement values

correlate. A high Measurement CorrelationCoefficient indicates that the two sets of values aresimilar a high percentage of the time.

Relationships between two sets of data can be viewedgraphically via scatter plots. TheLinear RegressionLine is the equation of a straight line that best fitsthe plotted data. When the two sets of data aresimilar, the Linear Regression Line approaches Y =X.

TheMean of Absolute Difference is the averagedifference between the measurements generated by

the algorithm and the true measurements. A lowMean of Absolute Difference indicates that the twosets of values are similar a high percentage of thetime.

Results Summary

The ST Segment Measurement Accuracy results aresummarized below in two scatter plots, one for eachECG channel in the database.

The horizontal axis in each of the plots represents

the range of ST measurement values, in millimeters,generated by the algorithm. The vertical axisrepresents the ST measurement values, inmillimeters, from the ESC ST-T reference database.The middle of the three diagonal lines represents thedesired goal - the point where the algorithmsmeasurement value is equal to the databases value.The other two diagonal lines represent an errorrange of +/- 1.0 millimeter.

For each ST segment episode, one point is plottedusing the algorithms measurement as the horizontalcoordinate and the databases measurement as thevertical coordinate.

Algorithm ST Segment MeasurementAccuracy

Figure 6 ESC STT Database, Channel 1

Figure 7 ESC STT Database, Channel 2

As shown in the plots, only 10% of themeasurements generated by the algorithm exceed the

error range of 1.0 millimeter.The following table summarizes the ST segment

measurement accuracy performance measures:

The Measurement Correlation Coefficient of 0.96indicates that the algorithms value and the ESCST-T database value are highly correlated. TheLinear Regression Line also indicates that the twosets of data are highly correlated because theequation is close to Y= X.

The Mean of Absolute Difference value indicatesthat the average difference between each of thealgorithms values and the ESC ST-T database valuesis 0.52 millimeters. This value is very low whencompared with the resolution of the database values

(a resolution of 0.5 millimeters for values less than5.0 millimeters, and a resolution of 1.0 millimetersfor values greater than 5.0 millimeters).

Performance Measure Result

Measurement CorrelationCoefficient

0.96

Linear Regression Line Y = 0.94X-0.06

Mean of Absolute Difference 0.52 mm


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ConclusionComputerized ST segment monitoring is a tool theclinician can use to continuously monitor andevaluate the progress of patients. In order to fullymake use of this tool, it is important to understandthe algorithm's capabilities and limitations.

If an alarm is triggered due to an ST segmentchange, only a clinician, not the monitor, can

determine the seriousness of the event. To ensurepeak performance, the staff should be aware of theinterventions and adjustments they can implementto enhance the ST algorithm's performance andaccuracy.

From the results presented in the application note,the performance of the ST segment monitoringalgorithm is evident. This ability enhances theeffective monitoring of ST segment events in theclinical setting.

References

1.\Drew B, et. al.: Bedside ECG Monitoring, Heartand Lung, 20(6), November 1991, 597-623.

2.\Drew B and Tisdale L: ST Segment Monitoringfor Coronary Artery Reocclusion FollowingThrombolytic Therapy and CoronaryAngioplasty: Identification of Optimal Bedside

Monitoring Leads, Ameri can Journal of Criti calCare, 2(4), 1993, 280-292.

3.\Fox JT: Continuous ST Segment Monitoring - ANew Way to Detect Myocardial Ischemia,Nursing, 1994, Vol 24, 32EE-32KK.

4.\Fu GY, Joseph AJ, and Antalis G: Application ofContinuous ST Segment Monitoring in theDetection of Silent Myocardial Ischemia, Annalsof Emergency Medicine, 1994, Vol 23, 1113-1115.

5.\Tisdale L and Drew B: ST Segment Monitoringfor Myocardial Ischemia, AACN Clini cal Issues,4(1), February 1993, 34-43.

6.\Willerson JT and Cohn JN, ed.: Cardi ovascularMedicine, Churchil l Livingston, New York, 1995,347-48, 366-71, 1902-1904.


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CORPORATE HEADQUARTERS:

Philips Medical SystemsNederland B.V.Postbus 10.0005680 DA BestNetherlands

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Philips Medizinsysteme Bblingen GmbHCardiac and Monitoring SystemsHewlett-Packard Str. 271034 BblingenGermanyFax: (+49) 7031 463 1552

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www.medical.philips.com

Koninklijke Philips Electronics N.V., 2001.

All rights are reserved. Reproduction in whole or in part is

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Published June 30, 2002

5990-0516EN

Philips Medical Systems is part of Royal Philips Electronics

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STAR algorithm.pdf