2020 Signal Processing Workshop (SPW)最新文献

Application of Turbo Codes for Data Transmission in UWB Using PSK Modulated Complex Wavelets Turbo码在PSK调制复小波超宽带数据传输中的应用

2020 Signal Processing Workshop (SPW) Pub Date : 2020-10-05 DOI: 10.23919/SPW49079.2020.9259136

B. Assanovich

引用次数: 0

EEG Signal Analysis for Human Verification using Neural Networks – Preliminary Experimental Results 利用神经网络对脑电图信号进行人体验证分析-初步实验结果

2020 Signal Processing Workshop (SPW) Pub Date : 2020-10-05 DOI: 10.23919/SPW49079.2020.9259137

Renata Plucińska, K. Jędrzejewski, Marek Waligóra, U. Malinowska

{"title":"EEG Signal Analysis for Human Verification using Neural Networks – Preliminary Experimental Results","authors":"Renata Plucińska, K. Jędrzejewski, Marek Waligóra, U. Malinowska","doi":"10.23919/SPW49079.2020.9259137","DOIUrl":"https://doi.org/10.23919/SPW49079.2020.9259137","url":null,"abstract":"The results of experimental studies on human verification by EEG signal analysis are presented in this paper. The developed approach was investigated using 220 EEG examinations recorded from 11 people, 20 examinations for every person. The first fifteen examinations were used for neural networks learning, and the rest 5 examinations for their evaluation. The EEG signals recorded for every person were separated into short segments for which feature extraction was conducted. After that, the features were introduced to a feedforward neural network, trained by the Levenberg-Marquardt backpropagation algorithm. We focused on spectral features, calculated separately for four EEG frequency bands. After the network training, we evaluated our approach by introducing at the network inputs the examinations from other days that were not presented to the neural network before. The results for two electrode sets: placed on the central (C3, Cz, C4, C3CzC4) and centro-occipital (C3, C4, O1, O2, C3C4, O1O2, C3C4O1O2), using accuracy, sensitivity, specificity, and precision measures, are presented and discussed in this paper. Regardless of the number of electrodes, almost all mean metrics were above 0.70 and increased with the number of electrodes from which the EEG signal features fed the neural network. One of the aims of this work was to investigate, whether temporary, daily changes in EEG signals would prevent people from being recognized.","PeriodicalId":399741,"journal":{"name":"2020 Signal Processing Workshop (SPW)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133923491","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

SPW 2020 Cover Page SPW 2020封面

2020 Signal Processing Workshop (SPW) Pub Date : 2020-10-05 DOI: 10.23919/spw49079.2020.9259129

引用次数: 0

Multiscale detrended cross-correlation of EEG and RR intervals during focal epilepsy 局灶性癫痫时脑电图和RR间期的多尺度去趋势相互关系

2020 Signal Processing Workshop (SPW) Pub Date : 2020-10-05 DOI: 10.23919/SPW49079.2020.9259132

Ivan Seleznov, I. Kotiuchyi, A. Popov, A. Nakata, Volodymyr Kharytonov, Miki Kaneko, K. Kiyono

{"title":"Multiscale detrended cross-correlation of EEG and RR intervals during focal epilepsy","authors":"Ivan Seleznov, I. Kotiuchyi, A. Popov, A. Nakata, Volodymyr Kharytonov, Miki Kaneko, K. Kiyono","doi":"10.23919/SPW49079.2020.9259132","DOIUrl":"https://doi.org/10.23919/SPW49079.2020.9259132","url":null,"abstract":"To evaluate the interaction between epilepsy-related brain activities and heart rate dynamics, we analyze electroencephalogram (EEG) and heart rate variability (HRV) using detrended moving-average cross-correlation analysis (DMCA) for pre- and postictal periods in subjects with focal epilepsy. The DMCA was applied to the 5 min. long periods of heart beat-to-beat intervals and power spectral density time series, located 5 and 10 min. before and after seizures. Statistically significant differences were found in the cross-correlation in δ (0.5–4 Hz) band for the periods before and after seizure, which shows the correlative coupling between RR intervals that δ band activity is changing while approaching to and after the epileptic seizure, that suggests the presence of nonlinear mechanisms of interactions between low-band EEG and RR intervals in observed periods of brain and heart activity in epilepsy. In contrast, no statistically significant changes could be observed while comparing brain-heart coupling in preictal periods. The wide scatter of distributions of all frequency bands in periods before epileptic seizure suggests that the multiscale cross-correlation coefficient is highly subject dependent and needs further subject-specific analysis for this particular period with longer time series.","PeriodicalId":399741,"journal":{"name":"2020 Signal Processing Workshop (SPW)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125937595","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: 0

Estimation of the Harmonic Signal Parameters in the Complex Interferences 复杂干扰中谐波信号参数的估计

2020 Signal Processing Workshop (SPW) Pub Date : 2020-10-05 DOI: 10.23919/SPW49079.2020.9259135

I. Prokopenko, Ihor Omelchuk, A. Osipchuk, J. Petrova

引用次数: 0

Feasibility Study on the Use of Heart Rate Variability Parameters for Detection of Atrial Fibrillation with Machine Learning Techniques 利用机器学习技术检测心房颤动心率变异性参数的可行性研究

2020 Signal Processing Workshop (SPW) Pub Date : 2020-10-05 DOI: 10.23919/SPW49079.2020.9259140

Szymon Buś, K. Jędrzejewski, T. Krauze, P. Guzik

{"title":"Feasibility Study on the Use of Heart Rate Variability Parameters for Detection of Atrial Fibrillation with Machine Learning Techniques","authors":"Szymon Buś, K. Jędrzejewski, T. Krauze, P. Guzik","doi":"10.23919/SPW49079.2020.9259140","DOIUrl":"https://doi.org/10.23919/SPW49079.2020.9259140","url":null,"abstract":"The paper is devoted to development and studies on atrial fibrillation (AFib) detection in electrocardiogram (ECG) using digital signal processing (DSP) and machine learning (ML). The goal of this pilot study was to find the DSP and ML methods suitable for the AF detection in real-time in short single-lead ECGs containing 32 consecutive cardiac cycles. Three simple Heart Rate Variability (HRV) parameters from the time domain analysis were calculated and used as features for ML algorithms. Binary decision tree and shallow neural network were used for classification, and the impact of metaparameters on the performance of the AFib detection algorithms was investigated to determine the lower limit of their required complexity. In the neural network, different numbers of hidden neurons and different activation functions were examined. In the decision tree, different limits on the maximum number of splits were set. For both AFib detection algorithms, various sets of HRV-based features were tested. With neural network (two features, ten hidden neurons), 98.3% accuracy, 97.1% sensitivity and 99.1% specificity were obtained. With decision tree (two features, seven splits), 96.9% accuracy, 96.3% sensitivity and 97.4% specificity were reached. This study shows the usefulness of neural network and decision tree algorithms for the detection of atrial fibrillation using the simplest HRV parameters. The use of more complex HRV parameters in AFib detection with the proposed ML algorithms requires further investigation.","PeriodicalId":399741,"journal":{"name":"2020 Signal Processing Workshop (SPW)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128410818","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

Frequency Modulated Chaos Shift Keying System for Wireless Sensor Network 无线传感器网络的调频混沌移位键控系统

2020 Signal Processing Workshop (SPW) Pub Date : 2020-10-05 DOI: 10.23919/SPW49079.2020.9259138

A. Litvinenko, A. Aboltins, D. Pikulins, Jānis Eidaks

引用次数: 5

SPW 2020 Breaker Page SPW 2020断路器页

2020 Signal Processing Workshop (SPW) Pub Date : 2020-10-05 DOI: 10.23919/spw49079.2020.9259130

引用次数: 0

Adaptive Active Noise Cancelling System for Headphones on Raspberry Pi Platform 树莓派平台耳机自适应主动降噪系统

2020 Signal Processing Workshop (SPW) Pub Date : 2020-10-05 DOI: 10.23919/SPW49079.2020.9259141

Piotr Sykulski, K. Jędrzejewski

引用次数: 1

SPW 2020 TOC

2020 Signal Processing Workshop (SPW) Pub Date : 2020-10-05 DOI: 10.23919/spw49079.2020.9259131

引用次数: 0