Segmentasi Data Sinyal EEG Berdasarkan Domain Waktu Sebagai Dasar Dalam Pengolahan Sinyal Pengambilan Keputusan Dalam Rehabilitasi Stroke

Authors

  • MY Teguh Sulistyono Universitas Dian Nuswantoro
  • Dyah Ernawati Universitas Dian Nuswantoro
  • Stalina Anggraeny Dewi Amodia Universitas Dian Nuswantoro
  • Davin Hernanda Putra Universitas Dian Nuswantoro

DOI:

https://doi.org/10.33633/joins.v9i1.9561

Keywords:

EEG, Stroke, Segmentasi, Cleaning Data, Band Pass Filter, Feature

Abstract

Penyakit stroke adalah salah satu penyakit kardiofaskuler  jika menyerang akan menyebabkan cacad permanen dan meninggal dunia. Proses pemeriksaan membutuhkan dokter hanya berdasarkan visual unruk mendiagnosa penyakit, jika pemeriksaan dilakukan banyak dokter maka diagnose akan berbeda-beda. Unruk menghindari hal tersebut dibutuhkan alat EEG untuk mengambil aktivitas gelombang otak yang hasil pengambilan data tersebut dalam bentuk data mentah. Data mentah agar dapat dihasilkan untuk proses analisis diperlukan pemrosesan sinyal yang terdiri dari band pass filter, cleaning data, segmentasi dan decomposisi. Permasalahan selama ini yang timbul bahwa data mentah tersebt masih dalam bentuk data yang masih banyak noise baik dari pergerakan mata ataupun aktivitas otot., sehingga data mentah yang telah diolah akan menjadi dasar dalam pemilihan feature. Penelitian ini menggunakan metode penelitian menggunakan 2 tahapan yaitu data mentah dan pre processing, dimana pre processing memiliki 3 langkah yaitu band pass filter, cleaning data dan segmentasi.Hasil akhir dari penelitian ini adalah Segmentasi Data Sinyal EEG Berdasarkan Domain Waktu Sebagai Dasar Dalam Pengolahan Sinyal Pengambilan Keputusan Dalam  Rehabilitasi Stroke. Kata kunci: EEG, Stroke, Segmentasi, Cleaning Data, Band Pass Filter, Feature

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Published

2024-07-19

How to Cite

[1]
M. T. Sulistyono, D. . Ernawati, S. A. D. Amodia, and D. H. Putra, “Segmentasi Data Sinyal EEG Berdasarkan Domain Waktu Sebagai Dasar Dalam Pengolahan Sinyal Pengambilan Keputusan Dalam Rehabilitasi Stroke”, Journal of Information System, vol. 9, no. 1, pp. 67–74, Jul. 2024.

Issue

Vol. 9 No. 1 (2024): Edisi Mei 2024

Section

Articles

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