Detection of Transient ST Segment Episodes During Ambulatory ECG Monitoring

Jager, F.; Moody, G.B.; Mark, Roger G.

doi:10.1006/cbmr.1998.1483

Cited by 69 publications

(58 citation statements)

References 16 publications

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“…The estimated parameters of the ECG allow us to detect MI [15]. For instance, a Q-wave that is more than 1/4 the size of the S-wave could be an indication of a possible MI [5].…”

Section: Detecting Myocardial Infarctionmentioning

confidence: 99%

Adaptive Filtering for Detecting Myocardial Infarction using Noninvasive Conducting Polymer Composite Sensors

Montavon

Ergezer²,

Lozovyy³

et al. 2012

Imaging and Signal Processing in Health Care and Technology / 772: Human-Computer Interaction / 773: Communication, Internet An

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Continuous electrocardiographic (ECG) monitoring using conducting polymer composite sensors (CPS) presents a non-invasive way to detect cardiac irregularities such as myocardial infarction (MI). Electromyography (EMG), which measures muscle activity in the human body, has a frequency range that overlaps that of the ECG wave. As a result, both EMG and ECG data are present when CPSs collect ECG signals. When measuring ECG waves of an individual during motion, we account for EMG by removing the motion artifact from the ECG signal. With the use of a normalized least mean square (NLMS) algorithm and known signal characteristics, we show that EMG noise can be successfully filtered from an ECG signal that is collected using our CPSs in the standard 12 lead ECG placement.Our software produces a diagnostic-friendly ECG signal and then determines the patient's heart rate.When applied to the arrhythmia database from the Massachusetts Institute of Technology and Beth Israel Hospital (MIT-BIH), our heartbeat detection logic has an accuracy of 99.6% with only 199 false beats and 240 missed beats out of 109,494 total heartbeats taken from 48 individual recordings. KEY WORDSSignal processing of physiological signals, wearable devices, biomedical signal processing, medical signal processing.

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“…The estimated parameters of the ECG allow us to detect MI [15]. For instance, a Q-wave that is more than 1/4 the size of the S-wave could be an indication of a possible MI [5].…”

Section: Detecting Myocardial Infarctionmentioning

confidence: 99%

Adaptive Filtering for Detecting Myocardial Infarction using Noninvasive Conducting Polymer Composite Sensors

Montavon

Ergezer²,

Lozovyy³

et al. 2012

Imaging and Signal Processing in Health Care and Technology / 772: Human-Computer Interaction / 773: Communication, Internet An

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“…We successfully used the tool for evaluating our two ST episode detection algorithms [8,9] using the LTST DB and ESC DB. Availability of the tool to the world community simplifies as well as promote easy and unique use of specific and complex performance measures and evaluation protocol, helps to easily compare different algorithms, encourages the use of unique performance measures and evaluation protocols in the field, and gain the consistent use of standard performance measures.…”

Section: Discussionmentioning

confidence: 99%

“…The "Comparison" window of the EVAL ST tool. Evaluation results shown are of the ST episode detection algorithm from [9] using the ESC DB as development set (top), of the ST episode detection algorithm from [8] using the LTST DB (annotation protocol B) as development set (middle), and of the ST algorithm from [8] using the ESC DB as test set (bottom).…”

Section: Open-source Toolmentioning

confidence: 99%

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An open-source tool to evaluate performance of transient ST segment episode detection algorithms

Jager

Smrdel²,

Mark³

Computers in Cardiology, 2004

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[3,4,5] by adding performance matrices to differentiate between ischemic and non-ischemic heart-rate related ST segment episodes for use with the LTST DB. Performance measuresEvaluation of an ST segment episode detection algorithm should answer the following questions:• How well are ST episodes detected?• How well are ischemic and non-ischemic heart-rate related ST episodes differentiated?• How reliably are ST episode or ischemic ST episode duration measured?• How accurately are ST deviations measured?• How well will the ST algorithm perform in the real world? Transient ST segment episodes (the events of interest) are characterized by: 1) number, 2) length, and 3) extrema deviation. When evaluating multi-channel ST-algorithm performance, the ST annotation stream for all leads must be combined into one reference stream using a logical OR function. The fact that at any given time there is either an ST episode or an interval with no ST deviation implies the use of two-by-two performance evaluation matrices. We further assume that all ST episodes are equally important. Evaluation of ST episode detection algorithms consists of comparing algorithm-annotated episodes with referenceannotated episodes. Algorithm-and reference-annotated episodes may differ considerably in length, there is not a one-to-one correspondence between the episodes from the two groups, nor non-events can be counted.Sensitivity matrix (see figure 1, left) summarizes how the reference ischemic ST episodes were labelled by the algorithm, i.e., how many of the reference ST episodes were detected, TP S , and how many were missed, FN . The positive predictivity matrix (figure 1, right) summarizes how many of the algorithm-annotated ST episodes were actually ST episodes, TP P , and how many were falsely detected, FP . The performance measures to assess ability to detect ST episodes depend on the concept of matching [3]. In measuring sensitivity, we declared that matching of a reference ST episode occurs when the period of overlap includes at least one of the extrema of the reference ST episode, or at least one-half of the length of the reference ST episode. In measuring positive predictivity,

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Electrocardiogram (ECG): Automated Diagnosis

Fotiadis

Likas

Michalis

et al. 2006

Wiley Encyclopedia of Biomedical Engineering

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Electrocardiogram (ECG) is one of the most common signals used in medical practice because of its noninvasive nature and the information it contains. Its analysis can be used to assess the pathophysiological condition of the heart. Several systems for ECG recording and analysis have been developed for more than a century. Early ECG systems included recording and printing of the signal. Modern systems use computer technology to provide automated diagnosis. The latter is a significant research field and many methods and approaches have been proposed for the detection of ischemia, arrhythmia detection and classification, and diagnosis of chronic myocardial diseases. Those methods are based on the processing of the signal to remove noise and artifacts, extraction of certain features related to diseases, and analysis of the features to obtain the final decision. The analysis is usually based on signal processing, fuzzy logic, and artificial neural networks concepts, mixed with knowledge provided by medical experts. Today, those systems are evaluated using standard databases and must be introduced in clinical practice to be further validated.

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Detection of Transient ST Segment Episodes During Ambulatory ECG Monitoring

Cited by 69 publications

References 16 publications

Adaptive Filtering for Detecting Myocardial Infarction using Noninvasive Conducting Polymer Composite Sensors

Adaptive Filtering for Detecting Myocardial Infarction using Noninvasive Conducting Polymer Composite Sensors

An open-source tool to evaluate performance of transient ST segment episode detection algorithms

Electrocardiogram (ECG): Automated Diagnosis

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