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Detection of Heart Diseases by Mathematical Artificial Intelligence Algorithm Using Phonocardiogram Signals

Journal Name:

  • International Journal of Innovation and Applied Studies

Publication Year:

  • 2013

Key Words:

Author NameUniversity of Author
Abstract (2. Language): 
An artificial intelligence (AI) algorithm has been developed using Mathematical formula to diagnose heart disease from Phonocardiogram (PCG) signals. Auscultation, the technique of listening to heart sounds with a stethoscope can be used as a primary detection technique for detecting heart disorders for the past years. But now the Phonocardiogram, the digital recording of heart sounds is becoming very popular technique as it is relatively inexpensive. Four amplitude parameters of the PCG signal are extracted by using filter technique and are used as input. PCG signals for three types of heart diseases such as Tachycardia, Bradycardia and Atrial fibrillation were used in this paper to test the accuracy. These disease types that affect the electrical system of heart are known as arrhythmias, cause the heart to beat very fast (Tachycardia) or very slow (Bradycardia), or unexpectedly (Atrial fibrillation). After the signals are filtered and the parameters are extracted, the parameters are fed to the AI algorithm. Classifications of heart diseases are carried using the AI algorithm by comparing the extracted parameters. Here comparison is done using Min Max method. The developed mathematical artificial intelligence algorithm is implemented in MATLab using Simulink and the simulation results proved that the developed algorithm has been shown to be a powerful technique in detection of heart diseases using PCG signals.
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  • English

REFERENCES

References: 

[1] S. S. Tripathy, “System for diagnosing valvular heart disease using heart sounds,” Master’s Thesis, India, 2005.
[2] Ibrahim Abdel-Motaleb and Rohit Akula, “Artificial Intelligence Algorithm for Heart Disease Diagnosis using
Phonocardiogram Signals,” IEEE transaction on biomedical engineering, Vol. 51, NO. 7, pp. 1196-1202, 2012.
[3] A. S. A. Mohamed and H. M. Raafat, “Recognition of heart sounds and murmurs for cardiac diagnosis,” In Proceedings of
9th International Conference on Pattern Recognition, pp. 133-145, 1988.
[4] S. B .Patil and Y. S. Kumaraswamy, “Intelligent and Effective Heart Attack Prediction System Using Data Mining and
Artificial Neural Network,” European Journal of Scientific Research, ISSN 1450-216X Vol.31 No.4. pp. 642-656, 2009.
[5] C. N. Gupta, Ramaswamy Palaniappan, Sundaram Swaminathan and Shankar M. Krishnan ,“Neural Network
Classification of Homomorphic Segmented Heart Sounds, “ Applied Soft Computing 7, pp. 286–297, 2007.
[6] T. R. Reed , N. E Reed and P. Fritzson, “The Analysis of Heart Sounds for Symptom Detection and Machine-Aided
Diagnosis,” In Proceedings of the 4th International EUROSIM conference, pp. 01-06, 2001.
[7] F. Yaghouby, A. Ayatollahi and R. Soleimani, “Classification of Cardiac Abnormalities Using Reduced Features of Heart
Rate Variability Signal,” World Applied Sciences Journal 6, pp. 1547-1554, 2009.
[8] “Heart Sounds. Phonocardiography” [Online] Available:
www.fiziologie.ro/engleza/Practicals_Semester_1/Practicals%20CV%204.pdf (April 2013)
[9] “Heart Articles” [Online] Available: http://heart.emedtv.com/heart_diseases/heart_diseases.html (April 2013)

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