Earticle

초록

영어

This paper develops a novel framework for feature extraction based on a combination of Linear Discriminant Analysis and cross-correlation. Multiple Electrocardiogram (ECG) signals, acquired from the human heart in different states such as in fear, during exercise, etc. are used for simulations. The ECG signals are composed of P, Q, R, S and T waves. They are characterized by several parameters and the important information relies on its HRV (Heart Rate Variability). Human interpretation of such signals requires experience and incorrect readings could result in potentially life threatening and even fatal consequences. Thus a proper interpretation of ECG signals is of paramount importance. This work focuses on designing a machine based classification algorithm for ECG signals. The proposed algorithm filters the ECG signals to reduce the effects of noise. It then uses the Fourier transform to transform the signals into the frequency domain for analysis. The frequency domain signal is then cross correlated with predefined classes of ECG signals, in a manner similar to pattern recognition. The correlated co-efficients generated are then thresholded. Moreover Linear Discriminant Analysis is also applied. Linear Discriminant Analysis makes classes of different multiple ECG signals. LDA makes classes on the basis of mean, global mean, mean subtraction, transpose, covariance, probability and frequencies. And also setting thresholds for the classes. The distributed space area is divided into regions corresponding to each of the classes. Each region associated with a class is defined by its thresholds. So it is useful in distinguishing ECG signals from each other. And pedantic details from LDA (Linear Discriminant Analysis) output graph can be easily taken in account rapidly. The output generated after applying cross-correlation and LDA displays either normal, fear, smoking or exercise ECG signal. As a result, the system can help clinically on large scale by providing reliable and accurate classification in a fast and computationally efficient manner. The doctors can use this system by gaining more efficiency. As very few errors are involved in it, showing accuracy between 90% - 95%.

목차

Abstract
 1. Introduction
 2. Proposed Architecture
  2.1. Data Acquisition and Processing
  2.2. Noise Reduction
  2.3. Dimensionality Reduction and Transformation
 3. Feature Extraction Techniques
  3.1. Cross-correlation
  3.2. Linear Discriminant Analysis
 4. Simulation Results and Discussions
  4.1. Improvement Achieved by the Proposed Framework
  4.2. Accuracy
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• Muhammad Fahad Shinwari [ Department of Engineering, International Islamic University, Islamabad, Pakistan, National University of Modern Languages, Islamabad, Pakistan ]
• Naveed Ahmed [ Department of Engineering, International Islamic University, Islamabad, Pakistan, National University of Modern Languages, Islamabad, Pakistan ]
• Hassan Humayun [ Department of Engineering, International Islamic University, Islamabad, Pakistan, National University of Modern Languages, Islamabad, Pakistan ]
• Ihsan ul Haq [ Department of Engineering, International Islamic University, Islamabad, Pakistan, National University of Modern Languages, Islamabad, Pakistan ]
• Sajjad Haider [ Department of Engineering, International Islamic University, Islamabad, Pakistan, National University of Modern Languages, Islamabad, Pakistan ]
• Atiq ul Anam [ Department of Engineering, International Islamic University, Islamabad, Pakistan, National University of Modern Languages, Islamabad, Pakistan ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Advanced Science and Technology

• 간기

  월간

• pISSN

  2005-4238

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605