Review: Recent Directions in ECG-FPGA Researches

لقد شهدت السنوات القليلة الماضية اهتماماً متزايداً نحو استخدام مصفوفة البوابات المنطقية القابلة للبرمجة FPGA في التطبيقات المختلفة. لقد أدى التقدم الحاصل في مرونة التعامل مع الموارد بالاضافة الى الزيادة في سرعة الاداء وانخفاض الثمن للـ FPGA وكذلك الاستهلاك القليل للطاقة الى هذا الاهتمام المتزايد بالـ FPGA. ان استخدام الـ FPGA في مجالات الطب والصحة يهدف بشكل عام الى استبدال اجهزة المراقبة الطبية كبيرة الحجم وغالية الثمن باخرى أصغر حجماً مع امكانية تصميمها لكي تكون اجهزة محمولة اعتماداً على مرونة التصميم التي يوفرها الـ FPGA. إنصب الاهتمام في العديد من البحوث الحالية على استخدام نظام FPGA لمعالجة الجوانب المتعلقة بإشارة تخطيط القلب وذلك لتوفير التحسينات في الاداء وزيادة السرعة بالاضافة الى أيجاد وإقتراح افكار جديدة لمثل هذه التطبيقات. ان هذا البحث يوفر نظرة عامة عن الاتجاهات الحالية في انظمة ECG-FPGA.The last few years witnessed an increased interest in utilizing field programmable gate array (FPGA) for a variety of applications. This utilizing derived mostly by the advances in the FPGA flexible resource configuration, increased speed, relatively low cost and low energy consumption. The introduction of FPGA in medicine and health care field aim generally to replace costly and usually bigger medical monitoring and diagnostic equipment with much smaller and possibly portable systems based on FPGA that make use of the design flexibility of FPGA. Many recent researches focus on FPGA systems to deal with the well-known yet very important electrocardiogram (ECG) signal aspects to provide acceleration and improvement in the performance as well as finding and proposing new ideas for such implementations. The recent directions in ECG-FPGA are introduced in this paper

Diseño e implementación de un clasificador SVM para la detección de arritmias a partir del análisis de señales electrocardiográficas (ECG)

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A novel Discrete Wavelet-Concatenated Mesh Tree and ternary chess pattern based ECG signal recognition method

Electrocardiogram (ECG) signals have been widely used to diagnose heart arrhythmias. In order to detect these arrhythmias using ECG signals, many machine learning methods have been presented. In this article, a novel Discrete Wavelet Concatenated Mesh Tree (DW-CMT) and ternary chess pattern (TCP) based ECG signal recognition method is presented. The proposed ECG signal recognition method consists of 4 main steps: pre-processing using DW-CMT, feature extraction using TCP, feature selection, and classification. In the pre-processing step, 15 sub-bands of an ECG signals are generated. By using TCP, features are extracted from the sub-bands of the ECG signal. The extracted features are concatenated in the feature concatenation phase. In order to select distinctive features, the neighborhood component analysis (NCA) based feature selection method is used and the 128 most distinctive features are selected. In order to demonstrate the strength of the extracted and selected features, conventional classifiers which are linear discriminant analysis (LDA), k-nearest neighbor (k-NN), support vector machine (SVM) are used. To test the success of the proposed method, the MIT-BIH dataset and St. Petersburg dataset were used. The 96.60% maximum classification accuracy is achieved for the MIT-BIH dataset using k-NN and 97.80% accuracy is achieved using SVM for St. Petersburg ECG dataset. The obtained results clearly prove the success of the proposed method.</p