Early abnormal heartbeat multistage classification by using decision tree and K-nearest neighbor

Mohamad Sabri Bin Sinal, Eiji Kamioka

研究成果: Conference contribution

抄録

Heart diseases contribute to the highest cause of death around the world particularly for middle aged and elderly people. There are various types of heart disease symptoms. One of the most common types is Arrhythmia which is considered as a dangerous heart condition since the symptom itself may initiate more chronic heart diseases and result in death if it is not treated earlier. However, the detection of Arrhythmia by humans is regarded as a challenging task because the natures of the symptom appear at random times. Therefore, an automatic detection method of abnormal heartbeat in ECG (electrocardiogram) data is needed to overcome the issue. In this paper, a novel multistage classification approach using K-Nearest Neighbor and decision tree of the 3 segments in the ECG cycle is proposed to detect Arrhythmia heartbeat from the early minute of ECG data. Specific attributes based on feature extraction in each heartbeat are used to classify the Normal Sinus Rhythm and Arrhythmia. The experimental result shows that the proposed multistage classification approach is able to detect the Arrhythmia heartbeat with 90.6% accuracy for the P and the Q peak segments, 91.1% accuracy for the Q, R and S peak segments and lastly, 97.7% accuracy for the S and the T peak segments, outperforming the other data mining techniques.

元の言語English
ホスト出版物のタイトルAICCC 2018 - Proceedings of 2018 Artificial Intelligence and Cloud Computing Conference
出版者Association for Computing Machinery
ページ29-34
ページ数6
ISBN(電子版)9781450366236
DOI
出版物ステータスPublished - 2018 12 21
イベント2018 International Conference on Artificial Intelligence and Cloud Computing, AICCC 2018 - Tokyo, Japan
継続期間: 2018 12 212018 12 23

出版物シリーズ

名前ACM International Conference Proceeding Series

Conference

Conference2018 International Conference on Artificial Intelligence and Cloud Computing, AICCC 2018
Japan
Tokyo
期間18/12/2118/12/23

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Decision trees
Electrocardiography
Data mining
Feature extraction

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

これを引用

Bin Sinal, M. S., & Kamioka, E. (2018). Early abnormal heartbeat multistage classification by using decision tree and K-nearest neighbor. : AICCC 2018 - Proceedings of 2018 Artificial Intelligence and Cloud Computing Conference (pp. 29-34). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3299819.3299848

Early abnormal heartbeat multistage classification by using decision tree and K-nearest neighbor. / Bin Sinal, Mohamad Sabri; Kamioka, Eiji.

AICCC 2018 - Proceedings of 2018 Artificial Intelligence and Cloud Computing Conference. Association for Computing Machinery, 2018. p. 29-34 (ACM International Conference Proceeding Series).

研究成果: Conference contribution

Bin Sinal, MS & Kamioka, E 2018, Early abnormal heartbeat multistage classification by using decision tree and K-nearest neighbor. : AICCC 2018 - Proceedings of 2018 Artificial Intelligence and Cloud Computing Conference. ACM International Conference Proceeding Series, Association for Computing Machinery, pp. 29-34, 2018 International Conference on Artificial Intelligence and Cloud Computing, AICCC 2018, Tokyo, Japan, 18/12/21. https://doi.org/10.1145/3299819.3299848
Bin Sinal MS, Kamioka E. Early abnormal heartbeat multistage classification by using decision tree and K-nearest neighbor. : AICCC 2018 - Proceedings of 2018 Artificial Intelligence and Cloud Computing Conference. Association for Computing Machinery. 2018. p. 29-34. (ACM International Conference Proceeding Series). https://doi.org/10.1145/3299819.3299848
Bin Sinal, Mohamad Sabri ; Kamioka, Eiji. / Early abnormal heartbeat multistage classification by using decision tree and K-nearest neighbor. AICCC 2018 - Proceedings of 2018 Artificial Intelligence and Cloud Computing Conference. Association for Computing Machinery, 2018. pp. 29-34 (ACM International Conference Proceeding Series).
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