Earticle

초록

영어

In this study, the development of Cardioid based graph electrocardiogram heart abnormalities classification technique is presented. ECG signals in this work were attained from a public online database UCD Sleep Apnea database (UCDB) with sampling rate of 250 Hz. Each recording has 60 seconds of electrocardiogram signals. Unique features were extracted using the Pan Tompkins algorithm, later Cardioid based graph was formed as the result of the differentiation process. The various shapes of closed-loop created were then observed. From the Cardioid loop, we evaluated the area and standard deviation to differentiate between normal and abnormal heartbeats. As a result, the area, standard deviation, and mean values of abnormal heartbeat were twice the value of a normal heartbeat thus indicating the differences between two types of heart morphologies. Thus, the output of the study suggests the proof-of-concept of our proposed mechanisms to detect heart abnormalities and has the potential to act as an alternative to the current techniques.

목차

Abstract
 1. Introduction
 2. Related Works
 3. Methodology
  3.1. Data Collection
  3.2. Pre-processing
  3.3. Feature Extraction
  3.4. Classification
 4. Experimentation and Results
 5. Conclusion
 References

키워드

저자

• Siti Nurfarah Ain Mohd Azam [ Department of Electrical and Computer Engineering, International Islamic University Malaysia P. O. Box 10, Jalan Gombak, 50728 Kuala Lumpur ]
• Khairul Azami Sidek [ Department of Electrical and Computer Engineering, International Islamic University Malaysia P. O. Box 10, Jalan Gombak, 50728 Kuala Lumpur ]
• Nur Izzati Zainal [ Department of Electrical and Computer Engineering, International Islamic University Malaysia P. O. Box 10, Jalan Gombak, 50728 Kuala Lumpur ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Bio-Science and Bio-Technology

• 간기

  격월간

• pISSN

  2233-7849

• 수록기간

  2009~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605