Deteksi Arritmia pada Sinyal EKG dengan Deep Neural Network

Bayu Wijaya Putra, Rahmat Fadly Isnanto, Purwita Sari


Health practitioners need electrocardiogram (ECG) devices in supervising the heart health.. Where currently p  enelitian presents abnormal classification of arritmia in ECG  signals based on MIT-BIH dataset using deep neural network..  Before the classification process, good  data preparation is required in order to get good accuracy results. The research method starts from data preparation, preprocessing of ECG beat signal, extraction feature and classification of ECG beat signal. The final result of the classification can be seen from the data validation. Validation  results get excellent results reaching  mencapai  99.62% and sensitivity reaches 98. 92%.

Teks Lengkap:



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ISSN (cetak): 2654-4032

Kantor Redaksi:
Fakultas Sains dan Teknologi
Universitas Islam Negeri Raden Fatah Palembang

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