Aplikasi Machine Learning Untuk Pengklasifikasian Sinyal Elektrokardiograf

Penulis :
Ratnadewi, Aan Darmawan Hangkawidjaja, Agus Prijono, & Jo Suherman

Cetakan Pertama : April 2020

viii + 68; 15.5×23 cm
ISBN : 978-623-93060-7-6

Bab 1. Pendahuluan ………………………………………………………………………………. 1
1.1 Latar Belakang………………………………………………………………………………. 1
1.2 Perumusan Masalah / Identifikasi Masalah………………………….. 2
1.3 Tujuan dan Manfaat Penelitian ………………………………………………… 3
1.4 Kerangka Pemikiran dan Hipotesis…………………………………………. 3
1.5 Diagram Blok Sistem …………………………………………………………………… 3

Bab 2. Tinjauan Pustaka……………………………………………………………………….. 7

Bab 3. Elektrokardiogram …………………………………………………………………… 9
3.1 Pengantar Elektrokardiogram………………………………………………….. 9
3.2 Elektrokardiogram normal ……………………………………………………….. 14
3.3 Interval R-R……………………………………………………………………………………. 16
3.4 Normal Sinus Rythm……………………………………………………………………. 16
3.5 Atrial Premature Beat Arrhythmia………………………………………….. 17
3.6 Atrial Flutter Arrhythmia…………………………………………………………… 17
3.7 Atrial Fibrillation Arrhythmia…………………………………………………… 18

Bab 4. Discrete Cosine Transform (DCT)……………………………………….. 21
4.1 Transformator Citra…………………………………………………………………….. 21
4.1.1 Sifat-Sifat dari Transformasi
Unitary ……………………………………… 23
4.1.1.1 Penyimpan Enerji…………………………………………………………………………. 23
4.1.1.2 Pengkompak Enerji……………………………………………………………………… 23
4.1.1.3 Dekorelasi………………………………………………………………………………………. 24
4.1.2 Pemilihan Transformator Citra………………………………………………… 24
4.2 Discrete Cosine Transform (DCT)……………………………………………. 26
4.3 Sifat Dari Matrik Transformasi Kosinus (DCT)…………………….. 27
4.4 Pemilihan Nilai Signifikan………………………………………………………….. 28


Bab 5. Distance ……………………………………………………………………………………….. 31
5.1 Euclidean Distance ………………………………………………………………………. 31
5.2 Cityblock/Manhatan Distance ………………………………………………….. 32
5.3 Chebychef Distance……………………………………………………………………… 32
5.4 Canberra Distance………………………………………………………………………… 32

Bab 6. Support Vector Machine dan Multiclass Support Vector Machine ……………………………………. 35
6.1 Pendahuluan………………………………………………………………………………….. 35
6.2 Support Vector Machines (SVM)………………………………………………. 35
6.3 Multi Class SVM…………………………………………………………………………….. 37
6.4 Metode “One – against – all” ……………………………………………………… 37
6.5 Metode “One – against – one”……………………………………………………. 38
6.6 Metode Directed Acyclic Graph Support Vector Machine (DAGSVM) …………………………………….. 38
6.7 SVM untuk pengklasifikasi sinyal EKG……………………………………. 39
6.8 Evaluasi…………………………………………………………………………………………… 39
Bab 7. Aplikasi Klasifikasi Sinyal Elektrokardiograf Menggunakan Pemrograman Matlab ………………………………… 41
7.1 Preprocessing ……………………………………………………………………………….. 41
7.2 Transformasi DCT dan pemilihan ekstraksi fitur………………… 42
7.3 Perhitungan Jarak ………………………………………………………………………… 43
7.4 Pengukuran jarak interval R-R…………………………………………………. 44
7.5 Pengklasifikasian dengan Classification learner pada MATLAB ……………………………………………………… 46
Bab 8. Simpulan……………………………………………………………………………………….. 63

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