Hasil Pencarian  ::  Simpan CSV :: Kembali

"As arrhythmias may be transient in nature and not seen during the shorter recording times of the standard ECG, ECG Holter monitoring allows the physician to make better informed decisions for the cardiac patient. The devices are worn by patients on an outpatient basis for days or weeks and can also be implanted subcutaneously. ECG Holter recordings are especially useful since they can be programmed individually for activation and specific tracing analysis. Designed for rapid study, this book contains 100 illustrative cases in ECG Holter monitoring. Each case consists of a tracing followed by a brief explanation of the findings. 100 Cases in ECG Holter is the perfect resource for busy physicians looking to optimize their skills at interpreting ECG Holter readings."

"Encourages the reader to accept that the ECG is easy to understand and that its use is just a natural extension of taking the patient's history and performing a physical examination. This title directs users of the electrocardiogram to straightforward and accurate identification of normal and abnormal ECG patterns"

"Dalam penelitian ini, telah dibangun sebuah sistem akuisisi data elektrokardiograf (EKG) 12-lead berbasis Raspberry Pi 4 yang berbobot rendah, berdaya rendah dan terjangkau. Raspberry Pi 4 digunakan untuk mengakuisisi dan memproses sinyal elektrokardiograf (EKG) dengan performa tinggi, karena memiliki kombinasi antara fleksibilitas dan versality. Sebagai pusat dari sistem akuisisi data yang dibangun, Raspberry Pi menerima, memproses, dan menyimpan data dari Analog Front-End to Digital Converter (ADC) ADS1298RECGFE-PDK. ADS1298 memiliki beberapa kelebihan diantaranya adalah akuisisi data secara simultan, resolusi 24-bit, membutuhkan daya <0.2 mW dan noise<1μV. Komunikasi data yang digunakan dalam sistem yang dibangun adalah Serial Peripheral Interface (SPI). Sistem ini menggunakan sumber daya dari baterai Sony VTC5 18650 untuk mencegah interferensi power line. Untuk bagian pemrosesan sinyal, penulis mengimplementasikan filter low pass Butterworth dengan orde 5 dan Fast Fourier Transform (FFT) pada program Python. Bahasa pemrograman yang digunakan adalah C yang digunakan untuk komunikasi antara Raspberry Pi dengan ADS1298RECGFE-PDK dan Python yang digunakan pemrosesan sinyal. Sistem ini telah dievaluasi menggunakan ProSim 4 yang menghasilkan bentuk gelombang ECG dengan ECG rate 120 BPM, 150 BPM, dan Aritmia, serta pengambilan data partisipan. Dicari juga selisih sinyal yang diperoleh dengan CardioCare 2000 dan hubungannya menggunakan regresi linier pada 120 BPM. Didapatkan nilai error selisih, gradien, dan intercept terbesar adalah 23.615%, 0.062%, dan 9.030%. Sistem ini akan digunakan dalam studi lain untuk mendeteksi Aritmia dengan metode klasifikasi Convolutional Neural Network (CNN). Hasil dari klasifikasi menunjukkan accuracy 100%, specificity 100%, dan sensitivity 100%.

---

In this study, a low weight, low cost, and affordable Raspberry Pi 4 based 12-lead electrocardiograph (ECG) data acquisition system has been built. Raspberry Pi is used to acquire and process electrocardiograph (ECG) signals in high performance, because it has a combination of flexibility and versality. As the center of the data acquisition system built, Raspberry Pi acquires, processes, and stores data from the ADS1298RECGFE-PDK Analog Front-End to Digital Converter (ADC). ADS1298 has several advantages including simultaneous data acquisition, 24-bit resolution, requires power <0.2 mW and noise <1μV. Data communication used in the system built is the Serial Peripheral Interface (SPI). The system uses the power source of the Sony VTC5 18650 battery to prevent power line interference. For the signal processing section, the authors implement the Butterworth low pass filter in order 5 and Fast Fourier Transform (FFT) in the Python program. The programming language used is C which is used for communication between Raspberry Pi with ADS1298RECGFE-PDK and Python which is used for signal processing. This system has been evaluated using ProSim 4 which produces ECG waveforms with ECG rates of 120 BPM, 150 BPM, and Arrhythmia, as well as participant data collection. This system is also looking for the difference in the signal obtained by CardioCare 2000 and its linear relationship using linear regression.The biggest difference, gradient, and intercept error values are 23.615%, 0.062%, and 9.030%. This system will be used in other studies to predict arrhythmias using the Convolutional Neural Network (CNN) classification method. The results of the classification show 100% accuracy, 100% specificity, 100% sensitivity."

"The book shows how the various paradigms of computational intelligence, employed either singly or in combination, can produce an effective structure for obtaining often vital information from ECG signals. The text is self-contained, addressing concepts, methodology, algorithms, and case studies and applications, providing the reader with the necessary background augmented with step-by-step explanation of the more advanced concepts. It is structured in three parts, part I covers the fundamental ideas of computational intelligence together with the relevant principles of data acquisition, morphology and use in diagnosis, part II deals with techniques and models of computational intelligence that are suitable for signal processing, and part III details ECG system-diagnostic interpretation and knowledge acquisition architectures. Illustrative material includes, brief numerical experiments. detailed schemes, exercises and more advanced problems."