Prenata, Giovanni Dimas (2017) Klasifikasi Sinyal ECG Menggunakan SVM Untuk Mempelajari Potensi Negatif Emosi Pada Orang Berusia Lanjut. Masters thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Penelitian ini meneliti aktifitas jantung orang berusia lanjut dengan menggunakan sensor ECG. Analisa dilakukan setelah proses ektraksi fitur sinyal ECG. Metode SVM dipergunakan sebagai metode pengenalan indikasi ritme sinyal ECG. Input machine learning (SVM) adalah fitur sinyal ECG yang sudah terlabeli ritme diagnosis sinyal ECG (sitasi penelitian kedokteran). Pemberian video stimulus emosi negatif bertujuan untuk membangkitkan emosi negatif partisipan.
Penelitian ini menggunakan 30 partisipan orang berusia lanjut. Hanya 19 data partisipan yang bisa dipergunakan. Dari 19 data partisipan tersebut, 3 data terindikasi penyakit jantung tertentu. Terdapat partisipan yang terkena penyakit Myocardial Infarction, Pacemaker Failure, Cardiac Disorder, Hypoxia dan lain sebagainya. 3 data tersebut terindikasi ritme jantung Attrial Flutter, Accelerated Junctional dan Accelerated Idioventricular. Pada partisipan yang tergolong ritme jantung Accelerated Junctional, tidak terdapat gelombang P sama sekali pada sinyal ECG, Heart Rate menurun dari keadaan normal menjadi 60 bpm dan frekuensi detak jantung (HF, LF dan VLF) juga relatif rendah dibanding keadaan normal.
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This study examined heart activity of elderly people using ECG sensors. The analysis is performed after ECG signal extracting process extraction. The SVM method is used as an introduction method of ECG signal rhythm indication. Input machine learning (SVM) is an ECG signal feature that has been labeled the rhythm of ECG signal diagnosis (medical research citation). The provision of negative emotional stimulus video aims to evoke the participants' negative emotions.
This study used 30 participants of elderly people. Only 19 data participants can be used. Of the 19 data participants, 3 data indicated specific heart disease. There are participants affected by the disease Myocardial Infarction, Pacemaker Failure, Cardiac Disorder, Hypoxia and so forth. 3 data is indicated by heart rhythm Attrial Flutter, Accelerated Junctional and Accelerated Idioventricular. In the participants who belong to the Accelerated Junctional heart rhythm, there is no P wave at all on the ECG signal, the Heart Rate decreases from normal to 60 bpm and the heart rate frequency (HF, LF and VLF) is also relatively low compared to normal.
| Item Type: | Thesis (Masters) |
|---|---|
| Additional Information: | RTE 610.284 Pre k-1 3100018074189 |
| Uncontrolled Keywords: | Health, Elderly Human, Stimulus Videos, ECG, Rhythm diagnosis, SVM and Heart Disease |
| Subjects: | R Medicine > RC Internal medicine > RC683.5.E5 Electrocardiography T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Electrical Technology > Electrical Engineering > 20101-(S2) Master Thesis |
| Depositing User: | Giovanni Dimas Prenata |
| Date Deposited: | 20 Feb 2018 02:48 |
| Last Modified: | 30 Apr 2020 02:26 |
| URI: | http://repository.its.ac.id/id/eprint/49320 |
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