EKSTRASI FITUR SINYAL EKG MYOCARDIAL INFARCTIN MENGGUNAKAN DISCRETE WAVELET TRANSFORMATION

Siti Agrippina Alodia Yusuf; Nani Sulistianingsih; Helmi Imaduddin

doi:10.46764/teknimedia.v4i1.96

Siti Agrippina Alodia Yusuf Universitas Muhammadiyah Mataram
Nani Sulistianingsih Universitas Muhammadiyah Mataram
Helmi Imaduddin Universitas Muhammadiyah Surakarta

DOI: https://doi.org/10.46764/teknimedia.v4i1.96

Kata Kunci: DWT, ECG, KNN, K-fold cross validation

Abstrak

Salah satu tahapan penting pada proses identifikasi sinyal EKG adalah ekstraksi fitur, dimana fitur yang diperoleh mencirikan kondisi dari jantung. Kondisi dari jantung dapat dilihat berdasarkan gelombang-gelombang yang dihasilkan pada sinyal EKG, dimana gelombang-gelombang tersebut dihasilkan oleh aktivitas listrik jantung. Pada penelitian ini, dua tipe mother wavelet akan dibandingkan untuk melihat tipe mana yang paling cocok untuk mengekstraksi fitur dari sinyal EKG. Tipe mother wavelet yang akan dibandingkan adalah Daubechies dan Symlet dengan orde masing-masing adalah 5, 6, dan 7 untuk Daubechies, dan 6, 7, dan 8 untuk Symlet. Sinyal EKG dengan kondisi jantung MI dan normal yang telah ditingkatkan kualitasnya dan telah melalui proses pemotongan sinyal, diekstraksi menggunakan Discrete Wavelet Transformation (DWT) dengan mother wavelet Daubechies dan Symlet pada dekomposisi level dua dan fitur yang diambil yaitu fitur statistik seperti rata-rata, median, standar deviasi, kurtosis, dan skewness. Fitur-fitur diambil dari sub-band D2 dan D1, sehingga total fitur yang diperoleh yaitu 10 fitur. Sinyal EKG kemudian diklasifikasi menggunakan metode KNN dan untuk mendapatkan hasil yang tergenaralisasi, maka K-fold cross validation juga diaplikasikan. Berdasarkan percobaan yang telah dilakukan, diperoleh akurasi tertinggi sebesar 94% dengan sensitivitas dan spesifisitas sebesar 82% dan 91% dengan mengaplikasikan mother wavelet Daubechies orde 7.

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