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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한의용생체공학회 Biomedical Engineering Letters (BMEL) Biomedical Engineering Letters (BMEL) Vol.11 No.1
- 발행연도
- 2021.1
- 수록면
- 69 - 84 (16page)
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초록· 키워드
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One of the most crucial and informative tools available at the disposal of a Cardiologist for examining the condition of apatient’s cardiovascular system is the electrocardiogram (ECG/EKG). A major reason behind the need for accurate reconstructionof ECG comes from the fact that the shape of ECG tracing is very crucial for determining the health condition ofan individual. Whether the patient is prone to or diagnosed with cardiovascular diseases (CVDs), this information can begathered through examination of ECG signal. Among various other methods, one of the most helpful methods in identifyingcardiac abnormalities is a beat-wise categorization of a patient’s ECG record. In this work, a highly efficient deep representationlearning approach for ECG beat classification is proposed, which can significantly reduce the burden and time spentby a Cardiologist for ECG Analysis. This work consists of two sub-systems: denoising block and beat classification block. The initial block is a denoising block that acquires the ECG signal from the patient and denoises that. The next stage is thebeat classification part. This processes the input ECG signal for finding out the different classes of beats in the ECG throughan efficient algorithm. In both stages, deep learning-based methods have been employed for the purpose. Our proposedapproach has been tested on PhysioNet’s MIT-BIH Arrhythmia Database, for beat-wise classification into ten importanttypes of heartbeats. As per the results obtained, the proposed approach is capable of making meaningful predictions andgives superior results on relevant metrics.
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참고문헌 (18)
참고문헌 신청
Ullah A / 2020 / Classifcation of arrhythmia by using deep learning with 2-d ecg spectral image representation / Remote Sens 12:1685
Nurmaini S / 2019 / An automated ecg beat classification system using deep neural networks with an unsupervised feature extraction technique’’ / Appl Sci 9(14):2921
Willems JL / 1987 / Comparison of multigroup logistic and linear discriminant ecg and vcg classifcation / J Electrocardiol 20(2):83~92
Coast DA / 1990 / An approach to cardiac arrhythmia analysis using hidden markov models / IEEE Trans Biomed Eng 37(9):826~836
Dutta S / 2010 / Correlation technique and least square support vector machine combine for frequency domain based ecg beat classifcation / Med Eng Phys 32(10):1161~1169
Nurmaini S / 2019 / An automated ecg beat classification system using deep neural networks with an unsupervised feature extraction technique’’ / Appl Sci 9(14):2921
Willems JL / 1987 / Comparison of multigroup logistic and linear discriminant ecg and vcg classifcation / J Electrocardiol 20(2):83~92
Coast DA / 1990 / An approach to cardiac arrhythmia analysis using hidden markov models / IEEE Trans Biomed Eng 37(9):826~836
Dutta S / 2010 / Correlation technique and least square support vector machine combine for frequency domain based ecg beat classifcation / Med Eng Phys 32(10):1161~1169
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