Abstract
It is well known that the electrocardiogram (ECG) signal has crucial information to detect heart disease. However, development of automated heart disease diagnosis system is known as nontrivial problem since the ECG signal is different from patient to patient, measured time and environmental conditions. To overcome this problem, context-aware computing based adaptable heart disease diagnosis algorithm is proposed. Before diagnosis step, patient signal type recognition module groups various types of signal characteristics for patients and genetic algorithm (GA) finds optimal set of preprocessing, feature extraction and classifier for each group of signal types. Evaluation results using MIT-BIH database showed that various types of signal type require different preprocessing, feature extraction method and classifier and the test results showed 98.36% of classification accuracy for best optimization case.
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Kim, T.S., Kim, HD. (2005). Context-Aware Computing Based Adaptable Heart Diseases Diagnosis Algorithm. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552451_38
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DOI: https://doi.org/10.1007/11552451_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28895-4
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