Serial Holter ST-Segment Monitoring after First Acute Myocardial Infarction

Changes in the ECG IntroductionTherapeutic intervention to reduce transient myocardial ischaemic episodes could significantly improve the quality of life in affected subjects by reducing morbidity and mortality. The current methods of diagnosing these ischaemic events include cardiovascular imaging of the coronary arteries [1]. However, such specialised and resource intensive techniques are, arguably, unsuitable for studying ischaemic events brought on by activities of daily living in any one individual. Another technique, based on the analysis of the ECG waveform, has shown promise since abnormalities in the repolarization of ischaemic myocardial regions are visible in the ST segment of the ECG [2,3]. Although changes in ST elevation/depression can be quantified they can also occur because of a wide variety of other causes, including changes in heart rate, conduction pattern, hyperventilation, electrolyte abnormalities, response to medication, response to temperature changes, position of the subject, and noise in the ECG [4,5].Despite these uncertainties, ECG measurements can be highly sensitive, easy to do, and lend themselves to ambulatory (e.g. 24 hour) assessments [6][7][8] and computer-based automated analyses [9][10][11]. If it were possible to accurately distinguish between ischaemic and non-ischaemic ST changes in ambulatory ECG recordings made during subjects' normal activities, the benefits could be immediate and substantial to the patient.The aims of this study were therefore a) to produce a novel algorithm t o distinguish ischaemic and nonischaemic ST changes in the ECG waveform and b) to determine the accuracy of the algorithm using expertly annotated ambulatory ECG data sets as a reference. Methods Basic algorithmData for the changes in ST ( D ST) provided by PhysioNet were used. An example of DST is shown in figure 1. Event start times (T s ), for which the expertly classified ST changes (ischaemic or non-ischaemic) were known, were also provided and used in the development of the algorithm. DST represents the difference in ST between the current ST level and the baseline level. Principal components of ST provided by PhysioNet were also used in the optimization of the algorithm. The algorithm was based on the premise that ischaemic ST changes are large relative to non-ischaemic changes and that they are maintained for a period of time. The algorithm classified events as ischaemic if at the start of the event DST was greater than a threshold DST (V thres ), and before the end of the event DST maintained a minimum level (V min ) for a period of time (T min ).

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Serial Holter ST-Segment Monitoring after First Acute Myocardial Infarction

Cited by 2 publications

References 40 publications

Fifteen-year risk of major coronary events predicted by Holter ST-monitoring in asymptomatic middle-aged men

Fifteen-year risk of major coronary events predicted by Holter ST-monitoring in asymptomatic middle-aged men

An algorithm to distinguish ischaemic and non-ischaemic ST changes in the Holter ECG

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