scholarly journals DESIGN OF FIR FILTER ALGORITHM FOR NOISE REMOVAL IN ECG SIGNAL USING OPEN CORE SoC

2021 ◽  
Vol 10 (2) ◽  
pp. , 1044-1046
Keyword(s):  
Computer Architecture ◽  
Noise Removal ◽  
Fir Filter ◽  
Ecg Signal ◽  
Instruction Set ◽  
Cycle Count ◽  
The Many ◽  
Processor Cores ◽  
Open Core ◽  
Dsp Applications

The emerging technology in computer architecture has led to the development of various ISAs depending on the needs of the desired technology, architectures, and processor cores. Instruction Set Architectures (ISAs) for processors from Intel, AMD, Intel, RISC-V, etc. This has provided the path to implement various functions on an open core SoC Platform. Among the many DSP applications, the FIR filter has been implemented on an open core SoC platform that uses RISCV. Here specifically filtering of noise from ECG signal. The performance cycle count has been obtained for the same and compared with its counterpart ARM M7 on the Keil platform.

scholarly journals Denoising of Ecg Signals Using Fir & Iir Filter: a Performance Analysis

2018 ◽  
Vol 7 (4.12) ◽  
pp. 1
Author(s):  
Dr. Chhavi Saxena ◽  
Dr. Avinash Sharma ◽  
Dr. Rahul Srivastav ◽  
Dr. Hemant Kumar Gupta
Keyword(s):  
Low Frequency ◽  
Noise Removal ◽  
Ecg Signal ◽  
Clinical Practices ◽  
Iir Filters ◽  
Ecg Signals ◽  
Iir Filter ◽  
Ecg Signal Processing ◽  
Movement Artifacts ◽  
Electric Signals

Electrocardiogram (ECG) signal is the electrical recording of coronary heart activity. It is a common routine and vital cardiac diagnostic tool in which in electric signals are measured and recorded to recognize the practical status of heart, but ECG signal can be distorted with noise as, numerous artifacts corrupt the unique ECG signal and decreases it quality. Consequently, there may be a need to eliminate such artifacts from the authentic signal and enhance its quality for better interpretation. ECG signals are very low frequency signals of approximately 0.5Hz-100Hz and digital filters are used as efficient approach for noise removal of such low frequency signals. Noise may be any interference because of movement artifacts or due to power device that are present wherein ECG has been taken. Consequently, ECG signal processing has emerged as a common and effective tool for research and clinical practices. This paper gives the comparative evaluation of FIR and IIR filters and their performances from the ECG signal for proper understanding and display of the ECG signal.  

scholarly journals Convolution Neural Network Based Ecg Classifier

2019 ◽  
Vol 10 (3) ◽  
pp. 1626-1630
Author(s):  
Sharanya S ◽  
Sridhar PA ◽  
Poornakala J ◽  
Muppala Vasishta ◽  
Tharani U
Keyword(s):  
Neural Network ◽  
Noise Removal ◽  
Convolution Neural Network ◽  
P Wave ◽  
Time Interval ◽  
Ecg Signal ◽  
Related Condition ◽  
Simple Method ◽  
Medical Practitioners ◽  
Ecg Signals

Classification of Electrocardiogram (ECG) signals plays a significant role in the identification of the functioning of the heart. This work pertains with the ECG signals, where the classifier is developed for identification of normal or abnormal conditions of the heart. The raw ECG signals are collected from an online database (www.physioNet.org) for classification. The raw ECG signal is pre-processed for noise removal, and the frequency spectrum is analysed to compare raw and denoised ECG signal. Attributes (P, Q, R, S, T time intervals) from denoised ECG signal is analysed and classified using Convolution Neural Network (CNN). The paper reports a classification technique to differentiate ECG signals from the MIT-BIH database (arrhythmia database, arrhythmia p-wave annotations, atrial fibrillation). The CNN analyses the deviation between nominal ranges of attributes (amplitude and time interval) and classifies between the abnormality and normal ECG wave. This work provides a simple method for interpreting ECG related condition for the clinician and helps medical practitioners to make diagnostic decisions.

scholarly journals Beyond imitation: Zero-shot task transfer on robots by learning concepts as cognitive programs

2019 ◽  
Vol 4 (26) ◽  
pp. eaav3150 ◽  
Author(s):  
Miguel Lázaro-Gredilla ◽  
Dianhuan Lin ◽  
J. Swaroop Guntupalli ◽  
Dileep George
Keyword(s):  
Working Memory ◽  
Computer Architecture ◽  
Arm Movement ◽  
Physical World ◽  
Instruction Set ◽  
Computational Framework ◽  
Perception System ◽  
Image Pairs ◽  
High Level ◽  
Transfer Tasks

Humans can infer concepts from image pairs and apply those in the physical world in a completely different setting, enabling tasks like IKEA assembly from diagrams. If robots could represent and infer high-level concepts, then it would notably improve their ability to understand our intent and to transfer tasks between different environments. To that end, we introduce a computational framework that replicates aspects of human concept learning. Concepts are represented as programs on a computer architecture consisting of a visual perception system, working memory, and action controller. The instruction set of this cognitive computer has commands for parsing a visual scene, directing gaze and attention, imagining new objects, manipulating the contents of a visual working memory, and controlling arm movement. Inferring a concept corresponds to inducing a program that can transform the input to the output. Some concepts require the use of imagination and recursion. Previously learned concepts simplify the learning of subsequent, more elaborate concepts and create a hierarchy of abstractions. We demonstrate how a robot can use these abstractions to interpret novel concepts presented to it as schematic images and then apply those concepts in very different situations. By bringing cognitive science ideas on mental imagery, perceptual symbols, embodied cognition, and deictic mechanisms into the realm of machine learning, our work brings us closer to the goal of building robots that have interpretable representations and common sense.

Approximate Compressor-Based Multiplier Design Methodology for Error-Resilient Digital Signal Processing

2020 ◽  
Vol 29 (14) ◽  
pp. 2050233
Author(s):  
Zhixi Yang ◽  
Xianbin Li ◽  
Jun Yang
Keyword(s):  
Image Processing ◽  
Signal Processing ◽  
Digital Signal Processing ◽  
Resource Utilization ◽  
Finite Impulse Response ◽  
Digital Signal ◽  
Fir Filter ◽  
Electrical Performance ◽  
Approximate Computing ◽  
Dsp Applications

As many digital signal processing (DSP) applications such as digital filtering are inherently error-tolerant, approximate computing has attracted significant attention. A multiplier is the fundamental component for DSP applications and takes up the most part of the resource utilization, namely power and area. A multiplier consists of partial product arrays (PPAs) and compressors are often used to reduce partial products (PPs) to generate the final product. Approximate computing has been studied as an innovative paradigm for reducing resource utilization for the DSP systems. In this paper, a 4:2 approximate compressor-based multiplier is studied. Approximate 4:2 compressors are designed with a practical design criterion, and an approximate multiplier that uses both truncation and the proposed compressors for PP reduction is subsequently designed. Different levels of truncation and approximate compression combination are studied for accuracy and electrical performance. A practical selection algorithm is then leveraged to identify the optimal combinations for multiplier designs with better performance in terms of both accuracy and electrical performance measurements. Two real case studies are performed, i.e., image processing and a finite impulse response (FIR) filter. The design proposed in this paper has achieved up to 16.96% and 20.81% savings on power and area with an average signal-to-noise ratio (SNR) larger than 25[Formula: see text]dB for image processing; similarly, with a decrease of 0.3[Formula: see text]dB in the output SNR, 12.22% and 30.05% savings on power and area have been achieved for an FIR filter compared to conventional multiplier designs.

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