2020 IEEE Applied Signal Processing Conference (ASPCON)最新文献_第6页

Multiscale convolutional neural network for detecting paroxysmal atrial fibrillation from single lead ECG signals 基于单导联心电信号的多尺度卷积神经网络检测阵发性心房颤动

2020 IEEE Applied Signal Processing Conference (ASPCON) Pub Date : 2020-10-07 DOI: 10.1109/ASPCON49795.2020.9276690

Eedara Prabhakararao, S. Dandapat

{"title":"Multiscale convolutional neural network for detecting paroxysmal atrial fibrillation from single lead ECG signals","authors":"Eedara Prabhakararao, S. Dandapat","doi":"10.1109/ASPCON49795.2020.9276690","DOIUrl":"https://doi.org/10.1109/ASPCON49795.2020.9276690","url":null,"abstract":"Atrial fibrillation (AF) is one of the most common chronic cardiac arrhythmias often associated with stroke, heart failure, coronary artery disease, etc. Based on the stage of progression and chances of restorability, AF can be categorized into paroxysmal, persistent and permanent. Among all, automatic detection of paroxysmal (early stage) AF (PAF), is challenging as it is clinically silent and occurs in short duration (episodic pattern) an has a high chances of being undiagnosed. In this paper, we present a multiscale deep convolution neural network (MS-DCNN) framework for automatic detection of paroxysmal AF episodes from single lead short electrocardiogram (ECG) signals. The MS-DCNN employs the architecture of multiple-stream CNNs for the multiscale decomposed single lead ECG signal. The proposed method is evaluated on the Physionet/CinC Challenge 2017 ECG dataset consists of 5048 normal sinus rhythm (NSR) and 756 AF rhythm subjects acquired using AliveCor hand-held device. The proposed method achieves an average accuracy, sensitivity, specificity and F1-score of 84.31%, 84.80%, 83.82% and 84.31% respectively on the validation dataset.","PeriodicalId":193814,"journal":{"name":"2020 IEEE Applied Signal Processing Conference (ASPCON)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125377934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Secrecy Performance of an improved Interference-aided RF Energy Harvesting scheme in Two-Way Multi-Antenna Relay Network 双向多天线中继网络中一种改进的干扰辅助射频能量收集方案的保密性能

2020 IEEE Applied Signal Processing Conference (ASPCON) Pub Date : 2020-10-07 DOI: 10.1109/ASPCON49795.2020.9276720

Chandrima Thakur, S. Chattopadhyay

引用次数: 2

Analytical Model of a Strain Induced Lateral Channel Workfunction Engineered Surrounding Gate MOSFET 围绕栅极MOSFET的应变诱导横向沟道工作函数解析模型

2020 IEEE Applied Signal Processing Conference (ASPCON) Pub Date : 2020-10-07 DOI: 10.1109/ASPCON49795.2020.9276694

S. Sarkhel, N. Bagga

引用次数: 0

An Efficient Deep Learning Strategy: Its Application in Sclera Segmentation 一种高效的深度学习策略及其在巩膜分割中的应用

2020 IEEE Applied Signal Processing Conference (ASPCON) Pub Date : 2020-10-07 DOI: 10.1109/ASPCON49795.2020.9276718

Sumanta Das, I. De Ghosh, Abir Chattopadhyay

引用次数: 2

Linearized Receding Horizon Model Predictive Controller Design to Regulate Glucose in Type 1 Diabetic Patients 线性化后退水平模型预测控制器设计调节1型糖尿病患者的血糖

2020 IEEE Applied Signal Processing Conference (ASPCON) Pub Date : 2020-10-07 DOI: 10.1109/ASPCON49795.2020.9276696

D. Acharya, G. Gurumurthy, D. Das

引用次数: 2

Segment Specific Modeling of Electrocardiogram for Improved Reconstruction Error 改善重建误差的心电图分段特定建模

2020 IEEE Applied Signal Processing Conference (ASPCON) Pub Date : 2020-10-07 DOI: 10.1109/ASPCON49795.2020.9276731

A. Mitra, P. Kundu, Rajarshi Gupta

{"title":"Segment Specific Modeling of Electrocardiogram for Improved Reconstruction Error","authors":"A. Mitra, P. Kundu, Rajarshi Gupta","doi":"10.1109/ASPCON49795.2020.9276731","DOIUrl":"https://doi.org/10.1109/ASPCON49795.2020.9276731","url":null,"abstract":"Electrocardiogram (ECG) modeling is useful for abnormality detection and data compression. The common research problem in modeling is retaining pathological information using minimum number of model coefficients. In this paper, a new modeling technique for different wave segments of ECG signal, viz., baseline to P-onset, P wave, P-offset to Q, QRS complex, S to T-onset, T wave and T-offset to next baseline is presented. The processing steps included preprocessing, R-peak detection, beat segmentation and waveform partitioning, followed by modeling of individual partitions. For P, QRS and T wave, Gaussian model was adopted and for other segments, Fourier model was adopted to minimize reconstruction error. For testing of the proposed model, normal sinus rhythm (NSR) and myocardial infarction (MI) data records of PTB Diagnostic ECG database (ptbdb) and atrial premature (APC), premature ventricular contraction (PVC), left bundle branch block (LBBB) and right bundle branch block (RBBB) data records of MIT-BIH arrhythmia database (mitdb) under PhysioNet were used. The average SNR, and MSE using proposed method for ptbdb NSR was 86.33, and 4.41×10-6, respectively; for AMI 96.18, and 3.70×10-6 respectively; for IMI 80.86, and 1.36×10-6 respectively; for mitdb NSR 90.94 and 3.50×10-6 respectively; for APC 89.42, and 2.34×10-6 respectively; for PVC 93.28 and 3.06×10-6, respectively; for LBBB 93.77 and 2.74×10-6, respectively; for RBBB 92.83 and 3.52×10-6 respectively. Segment specific modelling approach provides better reconstruction performance in comparison with the few published works using Gaussian and Fourier model.","PeriodicalId":193814,"journal":{"name":"2020 IEEE Applied Signal Processing Conference (ASPCON)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130268623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Fusion of Mathematical Morphology with Adaptive Gamma Correction for Dehazing and Visibility Enhancement of Images 数学形态学与自适应伽玛校正的融合用于图像去雾和可见度增强

2020 IEEE Applied Signal Processing Conference (ASPCON) Pub Date : 2020-10-07 DOI: 10.1109/ASPCON49795.2020.9276734

Biswarup Ganguly, Anwesa Bhattacharya, Ananya Srivastava, D. Dey, S. Munshi

引用次数: 1

Retinal Disease Classification from Optical Coherence Tomographical Scans using Multilayered Convolution Neural Network 基于多层卷积神经网络的光学相干断层扫描视网膜疾病分类

2020 IEEE Applied Signal Processing Conference (ASPCON) Pub Date : 2020-10-07 DOI: 10.1109/ASPCON49795.2020.9276708

R. Bhadra, Subhajit Kar

引用次数: 8

Finite Impulse Response Filter Design using Grasshopper Optimization Algorithm and Implementation on FPGA 基于Grasshopper优化算法的有限脉冲响应滤波器设计及FPGA实现

2020 IEEE Applied Signal Processing Conference (ASPCON) Pub Date : 2020-10-07 DOI: 10.1109/ASPCON49795.2020.9276711

Tanay Dutta, Raina Modak Aich, Supriya Dhabal, P. Venkateswaran

{"title":"Finite Impulse Response Filter Design using Grasshopper Optimization Algorithm and Implementation on FPGA","authors":"Tanay Dutta, Raina Modak Aich, Supriya Dhabal, P. Venkateswaran","doi":"10.1109/ASPCON49795.2020.9276711","DOIUrl":"https://doi.org/10.1109/ASPCON49795.2020.9276711","url":null,"abstract":"This paper establishes an efficient design style of Type-1 FIR filter using Grasshopper Optimization Algorithm (GOA) and its implementation on FPGA. GOA is a newly developed population-based meta-heuristic optimization algorithm motivated by the swarming activities of grasshoppers. GOA with its better problem-solving capability has revolutionized the contemporary era. Minimization of faults in the optimum response and the estimated response in digital filters are the main feature of meta-heuristic optimization algorithms. Therefore, this algorithm has been broadly accepted in various fields due to its high efficiency in solving problem sets. Further, to emphasis the usefulness of the suggested algorithm, the simulated outcomes have been compared with the results of the well-established algorithms such as Parks McClellan (PM) Algorithm and Sine Cosine Algorithm (SCA) and it has been found that Grasshopper Optimization Algorithm (GOA) outperform PM and SCA in terms of stop-band attenuation and pass-band ripple. Additionally, this paper also explains the hardware function of the concept being contemplated within the FPGA platform.","PeriodicalId":193814,"journal":{"name":"2020 IEEE Applied Signal Processing Conference (ASPCON)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121607884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

A novel approach to detect and classify fruits using ShuffleNet V2 基于ShuffleNet V2的水果检测与分类新方法

2020 IEEE Applied Signal Processing Conference (ASPCON) Pub Date : 2020-10-07 DOI: 10.1109/ASPCON49795.2020.9276669

Sourodip Ghosh, Md. Jashim Mondal, Sourish Sen, Soham Chatterjee, Nilanjan Kar Roy, S. Patnaik

引用次数: 9