Pengembangan Metode Monitoring Rehabilitasi Pasien Stroke Dengan Teknik Bobath Menggunakan Sinyal EEG

Suyasmad, Suyasmad (2020) Pengembangan Metode Monitoring Rehabilitasi Pasien Stroke Dengan Teknik Bobath Menggunakan Sinyal EEG. Masters thesis, Institut Teknologi Sepeluh Nopember.

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Abstract

Program monitoring rehabilitasi Stroke (MSR) adalah bagian penting dalam mengendalikan perkembangan aktivitas pemulihan otak selama perawatan rehabilitasi. MSR biasanya dilakukan dengan menggunakan pengamatan manual oleh para medis. Namun, itu menunjukkan subjektivitas yang tinggi tergantung pada pengamatan dan evaluator, selain itu juga menunjukkan kurang sensitivitas mengenai perubahan kecil yang terjadi selama kemajuan rehabilitasi. Banyak pasien Stroke telah mengikuti program rehabilitasi namun belum dapat diukur secara objektif progresifitas terapinya karena kesulitan untuk memantau kemajuan selama rehabilitasi. Karena alasan-alasan tersebut, Electroencephalograph (EEG) dapat digunakan sebagai teknologi alternatif untuk memantau proses rehabilitasi Stroke. EEG adalah alat yang dapat merekam aktivitas listrik di sepanjang kulit kepala, sehingga perubahan kecil terjadi di otak mengenai kemampuan pasien selama rehabilitasi kemudian dapat ditangkap. Studi ini mengimplementasikan EEG untuk memantau proses rehabilitasi stroke dengan menganalisis parameter EEG yang dapat digunakan untuk melihat kemajuan rehabilitasi. Dalam studi ini, empat-stroke pasien berpartisipasi dalam program rehabilitasi terapi fisik menggunakan metode Bobath pada fungsi tangan, dengan menerapkan modified ashworth scale sebagai acuan evaluasi manual. Evaluasi EEG dilakukan pada post-test dengan pre-test dan post-test dengan bagian tubuh yang sehat, dengan menempatkan dua elektroda C3 dan C4 pada kulit kepala pasien dalam setiap perawatan. Pada tahap preprocessing, Finite Impulse Response (FIR) digunakan untuk filter band EEG. Pembersihan serta filtering artefact menggunakan Cleanline dan Artifact Subspace Reconstruction (ASR), sedangkan ICA digunakan untuk menguraikan EEG setelah ASR. Data EEG kemudian diklasifikasikan ke dalam tiga pita frekuensi seperti Alpha, Beta High, dan Beta Low. Fitur statistic yang digunakan adalah Power Spectral Density (PSD), Power Perscentage (PP), Standard Deviasi (STD), dan Mean Absolute Value (MAV). Analisis ini diterapkan pada data individu dalam mengevaluasi kemajuan rehabilitasi antara pre-test dengan post-test dan post-test dengan bagian tubuh yang sehat dalam setiap perawatan. Hasil menunjukkan bahwa fitur yang paling stabil yaitu STD Alpha dan PP Beta Low pada channel C4. Hasil latihan mempengaruhi secara signifikan terhadap proses rehabilitasi. ================================================================================================================== Monitoring stroke rehabilitation (MSR) program is a crucial part in controlling the progression of brain recovery activity during rehabilitation treatment. MSR usually is done using manual observation by clinicians. However, it showed high subjectivity depending on observations and evaluators, besides it also shows less sensitivity regarding the small changes that occur during the rehabilitation progress. Many stroke patients have joined the rehabilitation program but with less result and progress of rehabilitation due to the difficulties to monitor the progress during rehabilitation. Due to those reasons, Electroencephalograph (EEG) can be used as an alternative technology to monitor the process of stroke rehabilitation. EEG is a device that can record electrical activity along the scalp, so small changes happen in the brain regarding the patient's capability during rehabilitation then can be captured. This study implements EEG for monitoring the stroke rehabilitation process by analyzing EEG parameters that can be used to seeing the progress of rehabilitation. In this study, four-stroke patients participated in a physical therapy rehabilitation program using the Bobath method on hand function, by applying a modified ashworth scale as a reference for manual evaluation. EEG evaluation is carried out on the post-test with pre-test and post-test with healthy body parts, by placing two electrodes C3 and C4 on the scalp of the patient in each treatment. In the preprocessing stage, Finite Impulse Response (FIR) is used to filter the EEG band. Cleaning and filtering artifacts use Cleanline and Artifact Subspace Reconstruction (ASR), while ICA is used to decipher EEG after ASR. EEG data is then classified into three frequency bands such as Alpha, Beta High, and Beta Low. The statistical features that were used are Power Spectral Density (PSD), Power Percentage (PP), Standard Deviation (STD), and Mean Absolute Value (MAV). This analysis is applied to individual data in evaluating the progress of rehabilitation between pre-test and post-test and post-test with healthy body parts in each treatment. The results show that the most stable features are STD Alpha and PP Beta Low on the C4 channel. The results of the exercise significantly influence the rehabilitation process.

Item Type: Thesis (Masters)
Additional Information: RTE 629.895 Suy p-1
Uncontrolled Keywords: Monitoring Rehabilitasi Stroke, Bobath rehabilitation method, electroencephalograph, EEG Statistical Feature, Individual Analysis, Monitoring Stroke Rehabilitation, Bobath rehabilitation method, electroencephalograph, EEG Statistical Feature, Individual Analysis
Subjects: R Medicine > RC Internal medicine > RC386.5 Electroencephalography.
Divisions: Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Electrical Engineering > 20101-(S2) Master Thesis
Depositing User: SUYASMAD SUYASMAD
Date Deposited: 06 Aug 2020 07:19
Last Modified: 12 Sep 2020 08:01
URI: https://repository.its.ac.id/id/eprint/76643

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