Cardiac auscultation analysis system with Neural Network and SVM technique

Phatiwuttipat, Pipatthana; Kongprawechon, Waree; Tungpimolrut, Kanokvate; Yuenyong, Sumeth

doi:10.1109/ecticon.2011.5948018

Cited by 9 publications

(1 citation statement)

References 2 publications

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“…From the previous research, SVM classifier was applied to automate system based on impact acoustic method for the evaluation of tile-wall bonding integrity [4]. The SVM classifier can be used to diagnosing disease from heart sounds in digital format by a screening system called Cardiac Auscultation Analysis [5], applying SVM model for classification of childhood musical murmur [6], and analyzing the ultrasonic detection echoes in pipeline [7]. The SVM also used to compare with other classifier such as analyzing construction materials with ultrasonic waves to find the number of defects inside the construction [8].…”

Section: Introductionmentioning

confidence: 99%

Automatic wedge tightness classifying system by support vector machine

Poombansao¹,

Kongprawechnon²,

Theeraworn³

2015

2015 6th International Conference of Information and Communication Technology for Embedded Systems (IC-ICTES)

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This paper introduces a newly developed automatic classification system for wedge tightness inside the generator by applying support vector machine (SVM) classifier. The automatic classifying system for wedge tightness of the generator consists of 4 parts including data collection, preprocessing, feature extraction, and classification. Machine learning algorithm called SVM is used with the linear and radial basis function (RBF) classifier. Each input feature is extracted in different ways to evaluate the performance of classification. The evaluation is completed by using a 10-fold cross validation technique to provide high accuracy and a low number of False Negatives (FN). By applying the proposed system, the number of tightness and looseness inside wedge generator can be classified. Based on the classification results, the signals extracted in the frequency domain gives the best performance among the time domain and the frequency domain. This paper shows that the automatic classifying method has a high potential to identify the wedge tightness inside the generator.

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