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Robust and accurate anomaly detection in ECG artifacts using time series motif discovery.

Sivaraks H, Ratanamahatana CA - Comput Math Methods Med (2015)

Bottom Line: Our method can be utilized to both single-lead ECGs and multilead ECGs.Our experiment results on real ECG datasets are interpreted and evaluated by cardiologists.Our proposed algorithm can mostly achieve 100% of accuracy on detection (AoD), sensitivity, specificity, and positive predictive value with 0% false alarm rate.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Engineering, Chulalongkorn University, Phayathai Road, Pathumwan, Bangkok 10330, Thailand.

ABSTRACT
Electrocardiogram (ECG) anomaly detection is an important technique for detecting dissimilar heartbeats which helps identify abnormal ECGs before the diagnosis process. Currently available ECG anomaly detection methods, ranging from academic research to commercial ECG machines, still suffer from a high false alarm rate because these methods are not able to differentiate ECG artifacts from real ECG signal, especially, in ECG artifacts that are similar to ECG signals in terms of shape and/or frequency. The problem leads to high vigilance for physicians and misinterpretation risk for nonspecialists. Therefore, this work proposes a novel anomaly detection technique that is highly robust and accurate in the presence of ECG artifacts which can effectively reduce the false alarm rate. Expert knowledge from cardiologists and motif discovery technique is utilized in our design. In addition, every step of the algorithm conforms to the interpretation of cardiologists. Our method can be utilized to both single-lead ECGs and multilead ECGs. Our experiment results on real ECG datasets are interpreted and evaluated by cardiologists. Our proposed algorithm can mostly achieve 100% of accuracy on detection (AoD), sensitivity, specificity, and positive predictive value with 0% false alarm rate. The results demonstrate that our proposed method is highly accurate and robust to artifacts, compared with competitive anomaly detection methods.

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Related in: MedlinePlus

Anomaly detection results among three algorithms on MITDB dataset. (a), (b), (c), and (d) are results of our proposed RAAD, BFDD, HOT SAX, and BitClusterDiscord, respectively. RAAD produced correct results whereas BFDD, HOT SAX, and BitClusterDiscord produced incomplete results along with some false alarm.
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fig11: Anomaly detection results among three algorithms on MITDB dataset. (a), (b), (c), and (d) are results of our proposed RAAD, BFDD, HOT SAX, and BitClusterDiscord, respectively. RAAD produced correct results whereas BFDD, HOT SAX, and BitClusterDiscord produced incomplete results along with some false alarm.

Mentions: Many existing algorithms can only detect anomalies in one single lead. This experiment therefore aims to compare effectiveness among existing works and our proposed RAAD algorithm on a single-lead ECG (MITDB dataset). Even though RAAD is designed under multilead setting, Figure 11 demonstrates that RAAD can correctly detect premature ventricular contraction (PVC) in accordance with cardiologists' diagnosis and has superior performance to other competitive algorithms because the result from RAAD covers an entire morphology of PVC as shown in a dotted-line box in Figure 11(a) and also does not cover any portion of adjacent beats as shown in a solid-line box in Figure 11(a). More importantly, no false alarm results are produced. On the other hand, BFDD and BitClusterDiscord detect an anomaly subsequence that does not completely cover the morphology of the anomalous beat, and they cover some portion of the following beat, as shown in the dotted-line and solid-line boxes of Figures 11(b) and 11(d) and the zoom-in picture in Figure 12. In HOT SAX algorithm, its first detection turns out to be a false alarm (shown as (1) in Figure 11(c)), and the second detection does not cover the entire beat; that is, some portion of the previous beat is covered, but some part of TP segment at the end of the beat is missing (shown as (2) in Figure 11(c)).


Robust and accurate anomaly detection in ECG artifacts using time series motif discovery.

Sivaraks H, Ratanamahatana CA - Comput Math Methods Med (2015)

Anomaly detection results among three algorithms on MITDB dataset. (a), (b), (c), and (d) are results of our proposed RAAD, BFDD, HOT SAX, and BitClusterDiscord, respectively. RAAD produced correct results whereas BFDD, HOT SAX, and BitClusterDiscord produced incomplete results along with some false alarm.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4320938&req=5

fig11: Anomaly detection results among three algorithms on MITDB dataset. (a), (b), (c), and (d) are results of our proposed RAAD, BFDD, HOT SAX, and BitClusterDiscord, respectively. RAAD produced correct results whereas BFDD, HOT SAX, and BitClusterDiscord produced incomplete results along with some false alarm.
Mentions: Many existing algorithms can only detect anomalies in one single lead. This experiment therefore aims to compare effectiveness among existing works and our proposed RAAD algorithm on a single-lead ECG (MITDB dataset). Even though RAAD is designed under multilead setting, Figure 11 demonstrates that RAAD can correctly detect premature ventricular contraction (PVC) in accordance with cardiologists' diagnosis and has superior performance to other competitive algorithms because the result from RAAD covers an entire morphology of PVC as shown in a dotted-line box in Figure 11(a) and also does not cover any portion of adjacent beats as shown in a solid-line box in Figure 11(a). More importantly, no false alarm results are produced. On the other hand, BFDD and BitClusterDiscord detect an anomaly subsequence that does not completely cover the morphology of the anomalous beat, and they cover some portion of the following beat, as shown in the dotted-line and solid-line boxes of Figures 11(b) and 11(d) and the zoom-in picture in Figure 12. In HOT SAX algorithm, its first detection turns out to be a false alarm (shown as (1) in Figure 11(c)), and the second detection does not cover the entire beat; that is, some portion of the previous beat is covered, but some part of TP segment at the end of the beat is missing (shown as (2) in Figure 11(c)).

Bottom Line: Our method can be utilized to both single-lead ECGs and multilead ECGs.Our experiment results on real ECG datasets are interpreted and evaluated by cardiologists.Our proposed algorithm can mostly achieve 100% of accuracy on detection (AoD), sensitivity, specificity, and positive predictive value with 0% false alarm rate.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Engineering, Chulalongkorn University, Phayathai Road, Pathumwan, Bangkok 10330, Thailand.

ABSTRACT
Electrocardiogram (ECG) anomaly detection is an important technique for detecting dissimilar heartbeats which helps identify abnormal ECGs before the diagnosis process. Currently available ECG anomaly detection methods, ranging from academic research to commercial ECG machines, still suffer from a high false alarm rate because these methods are not able to differentiate ECG artifacts from real ECG signal, especially, in ECG artifacts that are similar to ECG signals in terms of shape and/or frequency. The problem leads to high vigilance for physicians and misinterpretation risk for nonspecialists. Therefore, this work proposes a novel anomaly detection technique that is highly robust and accurate in the presence of ECG artifacts which can effectively reduce the false alarm rate. Expert knowledge from cardiologists and motif discovery technique is utilized in our design. In addition, every step of the algorithm conforms to the interpretation of cardiologists. Our method can be utilized to both single-lead ECGs and multilead ECGs. Our experiment results on real ECG datasets are interpreted and evaluated by cardiologists. Our proposed algorithm can mostly achieve 100% of accuracy on detection (AoD), sensitivity, specificity, and positive predictive value with 0% false alarm rate. The results demonstrate that our proposed method is highly accurate and robust to artifacts, compared with competitive anomaly detection methods.

Show MeSH
Related in: MedlinePlus