Implementasi Deteksi Kompleks QRS ECG dengan Algoritma Pan-Tompkins pada Perangkat FPGA

ABSTRAKSI: Serangan dan gangguan jantung merupakan penyebab kematian nomor satu di dunia. Dalam menanggulangi terjadinya serangan serta gangguan pada jantung, maka monitoring terhadap kondisi jantung sangatlah penting. ECG merupkan sinyal hasil aktivitas kelistrikan jantung yang dapat memberikan informasi kondisi fisik dari pasien dan dapat mengindikasikan sebuah penyakit. Suatu sinyal ECG memiliki komponen utama berupa kompleks QRS. Sehingga pendeteksian nilai kompleks QRS memegang peranan yang sangat penting pada sistem pengolahan sinyal ECG. Salah satu metode yang menjadi referensi untuk perhitungan sebuah kompleks QRS secara real time adalah dengan metode Pan- Tompkins.Tujuan penelitian ini adalah untuk membuat sebuah hardware yang diimplementasikan untuk menghitung nilai dari QRS kompleks. Hardware yang akan direalisasikan mengambil masukan berupa sinyal ECG realtime yang telah mengalami konversi menjadi sinyal digital melalui perangkat Analog to Digital Converter (ADC). Operasi pada sinyal digital keluaran ADC adalah penghitungan nilai kompleks QRS dengan bantuan FPGA yang akan menjalankan sistem sesuai dengan metode Pan-Tompkins. Penggunaan FPGA digunakan karena perangkatnya yang relatif lebih murah dan mudah untuk dimodifikasi dibandingkan dengan perangkat yang biasa digunakan saat ini.Pada metode yang akan digunakan ini sinyal kompleks ECG akan mengalami beberapa blok pengolahan, yaitu blok B andpass filter , blok Differensiasi, blok Squaring Operation (pengkuadratan), blok integrasi, blok terakhir yaitu Thresholding. Blok-blok tersebut diimplementasikan pada perangkat FPGA sebagai sistem operasi perhitungan yang terdiri dari blok logika adder, resister, address loader, register, memory serta control unit. Hasil implementasi sistem ini didapatkan perangkat pendeteksi kompleks QRS dengan tingkat ketepatan sebesar 92% dengan total waktu pemprosesan sebesar 245 ns untuk clock 2 ms. Kata kunci: Electrocardiograf, Pan-Tompkins, QRS kompleks, ADC, FPGAKata Kunci : Electrocardiograf, Pan-Tompkins, QRS kompleks, ADC, FPGAABSTRACT: Heart attack is number one cause of human dead in the world. In purpose of controlling the number of death caused by heart attack monitoring the condition of heart is very important. ECG is an interpretation of the electrical activity of the heart over time captured and externally recorded that can detect the condition of heart and trouble on it. An ECG signal has QRS complex as it main component. So, detecting the value of QRS complex is very important in the way get the information of heart conditions. Pan-Tompkins method is the most wide common use in hardware implementation of QRS complex detector.This research’s goal is to implement the hardware that can be use in detecting the value of QRS complex. The future implemented hardware will be supplied by ECG signal that has been converted to digital signal before by an Analog to Digital Converter (ADC). Operation to ADC output signal will be handled by programmed FPGA that run the operating as the Pan-Tompkins method. This research implement in FPGA due to its flexibility in programmed and also the price is relative cheaper than other hardwares that commonly use nowadays.In Pan-Tompkins method the implemented system will be consist by several block, they are band pass filter block, derivation block, squaring operation block, integration block and decision block. The blocks will be implemented on FPGA as calculating operation blocks system that consists of adder, resister, address loader, memory and also control unit.As the result, this project successfully produces QRS detection system with 92% accuracy ang 245 ms processing time delay within clock of 5 ms.Keyword: Electrocardiograph, Pan-Tompkins, complex QRS, ADC, FPG

Reversed-Trellis Tail-Biting Convolutional Code (RT-TBCC) Decoder Architecture Design for LTE

Tail-biting convolutional codes (TBCC) have been extensively applied in communication systems. This method is implemented by replacing the fixed-tail with tail-biting data. This concept is needed to achieve an effective decoding computation. Unfortunately, it makes the decoding computation becomes more complex. Hence, several algorithms have been developed to overcome this issue in which most of them are implemented iteratively with uncertain number of iteration. In this paper, we propose a VLSI architecture to implement our proposed reversed-trellis TBCC (RT-TBCC) algorithm. This algorithm is designed by modifying direct-terminating maximum-likelihood (ML) decoding process to achieve better correction rate. The purpose is to offer an alternative solution for tail-biting convolutional code decoding process with less number of computation compared to the existing solution. The proposed architecture has been evaluated for LTE standard and it significantly reduces the computational time and resources compared to the existing direct-terminating ML decoder. For evaluations on functionality and Bit Error Rate (BER) analysis, several simulations, System-on-Chip (SoC) implementation and synthesis in FPGA are performed

Developing digital teaching material on Basic Electricity based on problem-based learning in vocational education

The electrical topic is considered a material that is difficult to understand because electricity is a substance that is not seen but can be felt. The objectives of this study were 1.) to develop digital textbooks for basic electricity materials based on problem-based learning; 2.) to analyze the feasibility of digital textbook products for basic electricity materials based on problem-based learning. This research is development research using the 4D model, namely define, design, develop, disseminate. This study involved language, material, and media experts in validating research products. The research subjects were students of technical engineering vocational education in one of the public universities in Indonesia who took Basic Electrical and Electronics courses, namely 25 students. The data collection technique used interviews, document analysis, and questionnaires. The data analysis technique used descriptive statistical analysis. Based on the research results that have been done, it has resulted in several conclusions, namely; 1.) Development of digital textbooks on basic electricity materials based on problem-based learning is carried out by referring to the 4D model development steps, namely define, design, develop, and disseminate; 2.) The appropriateness of digital textbooks according to the media expert's assessment of getting a mean score of 3.61 is declared very good, the material expert's assessment is 3.52, which is declared very good, and the user response gets an assessment of 3.51, which is declared very good Theoretically developed digital textbooks are able to increase student interest and motivation to learn basic electrical materials in an easy way

Evaluasi Program terapi Individual education program (IEP) untuk anak tunagrahita di sekolah khusus putra putri mandiri

Penelitian yang dilakukan ini merupakan bentuk inisiatif yangmerupakan sebuah perkumpulan ibu-ibu yang memiliki anak keterbatasanfisik, motorik dan juga hambatan dalam belajar, tentunya lembaga inibukanlah berasal dari kalangan orang-orang yang berkelebihan nilaiekonomi, tapi memiliki tekad bergotong royong, sehingga anak tidak perludirumahkan. Selain itu, yang ditawarkan dari lembaga swadaya ini yaituterapi memiliki tujuan untuk memperbaiki fungsi motorik anak, agar dapatberbaur didalam masyarakat. Program terapi ini memerlukan waktu yangtidak sebentar & anak diharapkan dapat sembuh.Penelitian ini dilakukandengan tujuan untuk mengetahui apakah evaluasi program yang meliputi 3tipe evaluasi, Inputs, Process, Outcomes, yang berada di Sekolah KhususPutra Putri Mandiri Desa Sasak Tinggi ini memiliki program yangmengacu pada Individual Education Program (IEP).Dalam penelitian ini, peneliti memfokuskan kepada anakTunagrahita yang ada dalam kategori di Sekolah Khusus Putra PutriMandiri, dalam hal ini pendekatan yang digunakan adalah pendekatankualitatif dengan melakukan beberapa studi dokumentasi, observasi danwawancara. Informan yang akan dipilih secara Nonprobabilty Samplingyang berjumlah 10 orang. Hasil dari penelitian Evaluasi Hasil PogramTerapi untuk Anak Tunagrahita yang berada di Sekolah Khusus Putra PutriMandiri.Konteks Evaluasi berdasarkan pada Indikator Relevansi, dalamupaya & keterjangkauan yang dinilai baik dan tepat bagi anak penyandangtunagrahita. Hasil Evaluasi Input berdasarkan pada indikator cakupan hasilsasaran dan ketersediaan dinilai efektif, namun aspek mitra kerjasama dandonator pelayanan dinilai masih kurang. Evaluasi Proses menunjukanbahwa pemberian layanan dinilai cukup baik, namun terdapat temuandimana 2 dari 6 subjek penelitian yang mengikuti terapi jarang sekalimengikuti terapi, sehingga tidak memenuhi target pencapaian tujuan.Evaluasi Output menggunakan indikator dampak yang dinilai baik karenadapat melihat hasil dampak perubahan kondisi dan perilaku klien yangmengikuti terapi, agar menjadi lebih positif dan baik di dalam masyarakat.120 hlm

FPGA-Based Implementation for Real-Time Epileptic EEG Classification Using Hjorth Descriptor and KNN

The EEG is one of the main medical instruments used by clinicians in the analysis and diagnosis of epilepsy through visual observations or computers. Visual inspection is difficult, time-consuming, and cannot be conducted in real time. Therefore, we propose a digital system for the classification of epileptic EEG in real time on a Field Programmable Gate Array (FPGA). The implemented digital system comprised a communication interface, feature extraction, and classifier model functions. The Hjorth descriptor method was used for feature extraction of activity, mobility, and complexity, with KNN was utilized as a predictor in the classification stage. The proposed system, run on a The Zynq-7000 FPGA device, can generate up to 90.74% accuracy in normal, inter-ictal, and ictal EEG classifications. FPGA devices provided classification results within 0.015 s. The total memory LUT resource used was less than 10%. This system is expected to tackle problems in visual inspection and computer processing to help detect epileptic EEG using low-cost resources while retaining high performance and real-time implementation