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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: In addition, every step of the algorithm conforms to the interpretation of cardiologists.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 for the INCARTDB05 dataset by RAAD algorithm.
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fig21: Anomaly detection results for the INCARTDB05 dataset by RAAD algorithm.

Mentions: Figures 21 and 22 are shown to compare the results of RAAD and BFDD. Due to space limitations and full clarity/readability in the figures, only the results of leads I, II, AVR, and V5 are shown. The results of other leads are very similar. The results of HOT SAX and BitClusterDiscord are also similar to that of BFDD so they are not presented here. Therefore, the complete results and details are provided in our support website [74]. The results show that all algorithms are able to detect the real anomaly beat. However, RAAD produces much fewer false alarm results than BFDD, HOT SAX, and BitClusterDiscord. Nonetheless, the results of all four algorithms still do not cover the morphology which is essential for diagnosis.


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 for the INCARTDB05 dataset by RAAD algorithm.
© Copyright Policy
Related In: Results  -  Collection

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

fig21: Anomaly detection results for the INCARTDB05 dataset by RAAD algorithm.
Mentions: Figures 21 and 22 are shown to compare the results of RAAD and BFDD. Due to space limitations and full clarity/readability in the figures, only the results of leads I, II, AVR, and V5 are shown. The results of other leads are very similar. The results of HOT SAX and BitClusterDiscord are also similar to that of BFDD so they are not presented here. Therefore, the complete results and details are provided in our support website [74]. The results show that all algorithms are able to detect the real anomaly beat. However, RAAD produces much fewer false alarm results than BFDD, HOT SAX, and BitClusterDiscord. Nonetheless, the results of all four algorithms still do not cover the morphology which is essential for diagnosis.

Bottom Line: In addition, every step of the algorithm conforms to the interpretation of cardiologists.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