Implementasi Penapis Digital Lolos Rendah untuk Pengolahan Sinyal Eeg dengan Menggunakan Pricoblaza Fpga

Penapis Digital Lolos Rendah adalah rangkaian elektronika digital yangberfungsi untuk melewatkan sinyal frekuensi yang berada dibawah ambangfrekuensi yang ditentukan. Rangkaian ini digunakan untuk menghilangkankomponen DC yang terdapat pada sinyal EEG. Picoblaze merupakan sebuahprosesor yang memiliki 3 buah core yang tertanam di dalam satu chip.Picoblaze merupakan mikrokontroler 8-bit yang didesain khusus untukdiimplementasikan pada FPGA Prosesor picoblaze dapat diimplementasikandalam sistem yang besar dan mempunyai fleksibilitas yang tinggi dalamdesaib berbasis FPGA. Perancangan Picoblaze yang dapat mengerjakanpengolahan sinyal EEG diperlukan untuk dapat mempermudah prosespengolahan berikutnya. Sinyal EEG dilewatkan pada port masukan picoblazeuntuk dianalisa sinyal hasil pada port keluaran picoblaze.. Hasil pengujianpada sistem FPGA Spartan 3 berjalan dengan baik dengan kebutuhan slicekurang dari 10 %

Design of EEG Signal Acquisition System Using Arduino MEGA1280 and EEGAnalyzer

This study integrates the hardware circuit design and software development to achieve a 16 channels Electroencephalogram (EEG) system for Brain Computer Interface (BCI) applications. Signals obtained should be strong enough amplitude that is usually expressed in units of millivolts and reasonably clean of noise that appears when the data acquisition process. The process of data acquisition consists of two stages are the acquisition of the original EEG signal can be done by the active electrode with an instrumentation amplifier or a preamplifier and processing the signal to get better signals with improved signal quality by removing noise using filters with IC OPAMP. The design of a preamplifier with high common-mode rejection ratio and high signal-to-noise ratio is very important. Moreover, the friction between the electrode pads and the skin as well as the design of dual power supply. Designs used single-power AC-coupled circuit, which effectively reduces the DC bias and improves the error caused by the effects of part errors. At the same time, the digital way is applied to design the adjustable amplification and filter function, which can design for different EEG frequency bands. The next step, those EEG signals received by the microcontroller through a port Analog to Digital Converter (ADC) that integrated and converted into digital signals and stored in the RAM of microcontroller which simultaneously at 16 ports in accordance with the minimal number of points of data collection on the human scalp. Implementation results have shown the series of acquisitions to work properly so that it can be displayed EEG signals via software EEGAnalyzer

Design of EEG Signal Acquisition System Using Arduino MEGA1280 and EEGAnalyzer

This study integrates the hardware circuit design and software development to achieve a 16 channels Electroencephalogram (EEG) system for Brain Computer Interface (BCI) applications. Signals obtained should be strong enough amplitude that is usually expressed in units of millivolts and reasonably clean of noise that appears when the data acquisition process. The process of data acquisition consists of two stages are the acquisition of the original EEG signal can be done by the active electrode with an instrumentation amplifier or a preamplifier and processing the signal to get better signals with improved signal quality by removing noise using filters with IC OPAMP. The design of a preamplifier with high common-mode rejection ratio and high signal-to-noise ratio is very important. Moreover, the friction between the electrode pads and the skin as well as the design of dual power supply. Designs used single-power AC-coupled circuit, which effectively reduces the DC bias and improves the error caused by the effects of part errors. At the same time, the digital way is applied to design the adjustable amplification and filter function, which can design for different EEG frequency bands. The next step, those EEG signals received by the microcontroller through a port Analog to Digital Converter (ADC) that integrated and converted into digital signals and stored in the RAM of microcontroller which simultaneously at 16 ports in accordance with the minimal number of points of data collection on the human scalp. Implementation results have shown the series of acquisitions to work properly so that it can be displayed EEG signals via software EEGAnalyzer

COST EFFECTIVENESS ANALYSIS PADA PERAWATAN GAGAL GINJAL DENGAN METODE HEMODIALISIS DAN PERITONEAL DIALISIS (Studi Kasus pada RSUP Dr Sardjito Yogyakarta)

The aim of this study is to evaluate treatment method for renal failure patient that give more effective result between hemodialysis and peritoneal dialysis treatment in Dialysis Instalation, RSUP Dr. Sardjito Yogyakarta. This study using Cost Effectiveness Analysis (CEA). Effectiveness measured by compare the result of cost per outcome (Cost Effectiveness Ratio) between hemodialysis and peritoneal dialysis treatment. This study was conducted by collecting primary data from 30 respondents of hemodialysis patient and 8 respondents of peritoneal dialysis patient. The data was collected by interview with key informants and using EQ-5D questionnaire to measured patient�s health condition. The result show that peritoneal dialysis give better health condition (0,913375) than hemodialysis (0,5879) even it need more money to spend for the treatment (Rp. 99.425.000,- per patient per year) than hemodialysis (Rp. 66.609.675,- per patient per year). Hemodialysis have CER 22.660.205,82 and peritoneal dialysis have CER 21.770.904,61. The conclusion is peritoneal dialysis more effective than dialysis treatment

Kartografi Dasar

xii, 131 hlm.; 23 x 16 c