The heart is one of the most important human organs. One of instruments to detect cardiac abnormalities is the electrocardiogram (ECG). This research tries to analyze ECG image in normal heart condition from ECG machine. The previous research related to the pre-processing process is the same, only at the feature extraction process look for peaks P, Q, R, S, T, heart rate, and Deviation-ST. While this research is the characteristic extraction process using wavelet transformation. The image of lead ECG 12 is processed using discrete wavelet transforms with decomposition up to ten levels, by searching for mean square error (MSE). The type of mother wavelet and the wavelet order used are Daubechies (db) with 1 (db1 (Haar)). The smallest MSE value decomposition results are obtained at the level 5, which are lead I, II, III, aVR, aVF, V4 and V5, lead V1 & V2 on level 4, for aVL (level 9), V3 (level 7) and V4 (level 6). It is expected that such research can be followed up to the identification model of cardiac abnormalities using wavelets.
Currently the introduction and detection of heart abnormalities using electrocardiogram (ECG) is very much. ECG conducted many research approaches in various methods, one of which is wavelet. This article aims to explain the trends of ECG research using wavelet approach in the last ten years. We reviewed journals with the keyword title "ecg wavelet" and published from 2011 to 2020. Articles classified by the most frequently discussed topics include: datasets, case studies, pre-processing, feature extraction and classification/identification methods. The increase in the number of ECG-related articles in recent years is still growing in new ways and methods. This study is very interesting because only a few researchers focus on researching about it. Several approaches from many researchers are used to obtain the best results, both by using machine learning and deep learning. This article will provide further explanation of the most widely used algorithms against ECG research with wavelet approaches. At the end of this article it is also shown that the critical aspect of ECG research can be done in the future is the use of datasets, as well as the extraction of characteristics and classifications by looking at the level of accuracy.
Jantung sangat penting dalam sistem organ tubuh manusia. Apabila terjadi kesalahan pada fungsi jantung akibatnya sangat fatal. Oleh karenanya sangatlah penting menjaga kondisi jantung agar tetap sehat. Penelitian ini mencoba menawarkan untuk meneliti terkait kelainan jantung dengan menggunakan citra Electrocardigram (EKG) 12 lead. Data EKG yang digunakan berupa citra. Tujuan penelitian ini untuk memperoleh model yang tepat dalam mengidentifikasi kelainan jantung dengan menggunakan wavelet. Tahapan penelitian terdiri dari pre-processing, ekstraksi ciri dan klasifikasi. Tahap pre-processing menggunakan metode segmentasi (merubah data citra dari grayscale ke biner), morfologi (metode dilasi dan metode erosi) dan transformasi ke sinyal. Tahap ektraksi ciri menggunakan metode dekomposisi transformasi wavelet dengan tingkatan tiga level, dimana mother wavelet yang digunakan berupa symlet orde 4 (Sym4). Tahap klasifikasi menggunakan jaringan syaraf tiruan dengan metode backpropagation. Adapun metode validasi dan evaluasi menggunakan k-fold cross validation dan confusion matrix. Penggunaan metode k-fold cross validation, dimana k=5 dengan pembagian data training 80% dan testing 20%. Hasil yang diperoleh dari keseluruhan sistem dimana tingkat akurasi sebesar 92,94%, sensitifitas sebesar 90% dan spesifisitas sebesar 94,55%.
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