• Title/Summary/Keyword: Noise Classification

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R Wave Detection Considering Complexity and Arrhythmia Classification based on Binary Coding in Healthcare Environments (헬스케어 환경에서 복잡도를 고려한 R파 검출과 이진 부호화 기반의 부정맥 분류방법)

  • Cho, Iksung;Yoon, Jungoh
    • Journal of Korea Society of Digital Industry and Information Management
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    • v.12 no.4
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    • pp.33-40
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    • 2016

  • Previous works for detecting arrhythmia have mostly used nonlinear method to increase classification accuracy. Most methods require accurate detection of ECG signal, higher computational cost and larger processing time. But it is difficult to analyze the ECG signal because of various noise types. Also in the healthcare system based IOT that must continuously monitor people's situation, it is necessary to process ECG signal in realtime. Therefore it is necessary to design efficient algorithm that classifies different arrhythmia in realtime and decreases computational cost by extrating minimal feature. In this paper, we propose R wave detection considering complexity and arrhythmia classification based on binary coding. For this purpose, we detected R wave through SOM and then RR interval from noise-free ECG signal through the preprocessing method. Also, we classified arrhythmia in realtime by converting threshold variability of feature to binary code. R wave detection and PVC, PAC, Normal classification is evaluated by using 39 record of MIT-BIH arrhythmia database. The achieved scores indicate the average of 99.41%, 97.18%, 94.14%, 99.83% in R wave, PVC, PAC, Normal.

  • https://doi.org/10.17662/ksdim.2016.12.4.033 인용 PDF KSCI

Enhancement Voiced/Unvoiced Sounds Classification for 3GPP2 SMV Employing GMM (3GPP2 SMV의 실시간 유/무성음 분류 성능 향상을 위한 Gaussian Mixture Model 기반 연구)

  • Song, Ji-Hyun;Chang, Joon-Hyuk
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.45 no.5
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    • pp.111-117
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    • 2008

  • In this paper, we propose an approach to improve the performance of voiced/unvoiced (V/UV) decision under background noise environments for the selectable mode vocoder (SMV) of 3GPP2. We first present an effective analysis of the features and the classification method adopted in the SMV. And then feature vectors which are applied to the GMM are selected from relevant parameters of the SMV for the efficient voiced/unvoiced classification. For the purpose of evaluating the performance of the proposed algorithm, different experiments were carried out under various noise environments and yields better results compared with the conventional scheme of the SMV.

  • PDF KSCI