Revisiting signal processing with spectrogram analysis on EEG, ECG and speech signals

Weijie Wang; Gaopeng Zhang; Luming Yang; V.S. Balaji; V. Elamaran; N. Arunkumar

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Revisiting signal processing with spectrogram analysis on EEG, ECG and speech signals

机译：通过频谱图分析对EEG，ECG和语音信号进行信号处理

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Biomedical signal processing is the utilization of digital signal processing techniques, such as Fourier transform, filtering, spectral estimation and wavelet transform to biomedical complications, such as the analysis of breathing cycle, cardiac signals, brain signals, etc. Digital filters are used to preserve the in-band signals and to block out-of-band noise. Low-pass, high-pass, band-pass, and band-stop filters are commonly used for filtering applications. The digital signal processing (DSP) concepts can be used for other biomedical applications such as biomedical imaging (MRI, CT, X-ray, PET, ultrasound) and genomic signal processing too. The experiments such as denoising corrupted ECG signals, respiratory artifacts removal, and an identification of rhythmic patterns from EEG signals with spectrogram analysis are performed. Matlab R2016b tool is used for this study. Finally, the assessment, survey and feedback results from the student are tabulated for the better improvement of the course study.

机译：生物医学信号处理是利用数字信号处理技术（例如傅立叶变换，滤波，频谱估计和小波变换）来生物医学并发症，例如呼吸周期，心脏信号，脑信号等的分析。数字滤波器用于保存带内信号并阻止带外噪声。低通，高通，带通和带阻滤波器通常用于滤波应用。数字信号处理（DSP）概念也可以用于其他生物医学应用，例如生物医学成像（MRI，CT，X射线，PET，超声）和基因组信号处理。进行了诸如对损坏的ECG信号进行降噪，消除呼吸伪影以及使用频谱图分析从EEG信号中识别有节奏模式的实验。 Matlab R2016b工具用于此研究。最后，将学生的评估，调查和反馈结果制成表格，以更好地改进课程学习。

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来源
《Future generation computer systems》 |2019年第9期|227-232|共6页
作者
Weijie Wang; Gaopeng Zhang; Luming Yang; V.S. Balaji; V. Elamaran; N. Arunkumar;
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作者单位

College of Light Industry Textile and Food Engineering Sichuan University Chengdu China;

SASTRA University Thanjavur India;

Solamalai College of Engineering Madurai India;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种 eng
中图分类
关键词
Corrupted ECG; EEG signal analysis; Spectrogram; Matlab; Bio-signal processing;

机译：心电图损坏;脑电信号分析;频谱图Matlab;生物信号处理;

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1. Analysis of Energy Concentration of the Speech, EEG, and ECG Signals in Healthcare Applications-A Survey [J] . Balaji M. Sundar Prakash, Jayabharathy R., Jegadeesan Nirmala, Journal of Medical Imaging and Health Informatics . 2020,第1期

机译：医疗保健应用中语音，EEG和ECG信号的能量浓度分析 - 调查
2. Principal component analysis of the spectrogram of the speech signal: Interpretation and application to dysarthric speech [J] . Kacha Abdellah, Grenez Francis, Rafael Orozco-Arroyave Juan, Computer speech and language . 2020,第Jana期

机译：语音信号频谱图的主成分分析：解构语音的解释和应用
3. Mu suppression as an index of sensorimotor contributions to speech processing: Evidence from continuous EEG signals [J] . CuellarM., BowersA., HarkriderA.W., International journal of psychophysiology: official journal of the International Organization of Psychophysiology . 2012,第2期

机译：Mu抑制作为感觉运动对语音处理贡献的指标：来自连续EEG信号的证据
4. FUZZY SYSTEMS TO PROCESS ECG AND EEG SIGNALS FOR QUANTIFICATION OF THE MENTAL WORKLOAD [C] . International FLINS conference on intelligent techniques and soft computing in nuclear science and engineering . 2000

机译：用于处理ECG和EEG信号的模糊系统，以定量心理工作量
5. PATTERN RECOGNITION AND SIGNAL PROCESSING TECHNIQUES APPLIED TO EEG MOTORSIGNAL ANALYSIS. [D] . NIRENBERG, LLOYD MICHAEL. 1969

机译：模式识别和信号处理技术应用于脑电信号分析。
6. Effects of Tau and Sampling Frequency on the Regularity Analysis of ECG and EEG Signals Using ApEn and SampEn Entropy Estimators [O] . Ricardo Espinosa, Jesica Talero, Alejandro Weinstein 2020

机译：TAU和采样频率对ECG和脑电图信号的规律性分析的影响涵盖熵估算器
7. EEG Signal Discrimination using Non-linear Dynamics in the EMD Domain S. M. Shafiul Alam,S. M. Shafiul Alam,Aurangozeb, and Syed TarekShahriar Abstract—An EMD-chaos based approach is proposed todiscriminate EEG signals corresponding to healthy persons,and epileptic patients during seizure-free intervals and seizureattacks. An electroencephalogram (EEG) is first empiricallydecomposed to intrinsic mode functions (IMFs). The nonlineardynamics of these IMFs are quantified in terms of the largestLyapunov exponent (LLE) and correlation dimension (CD).This chaotic analysis in EMD domain is applied to a large groupof EEG signals corresponding to healthy persons as well asepileptic patients (both with and without seizure attacks). It isshown that the values of the obtained LLE and CD exhibitfeatures by which EEG for seizure attacks can be clearlydistinguished from other EEG signals in the EMD domain.Thus, the proposed approach may aid researchers in developingeffective techniques to predict seizure activities. Index Terms—Electroencephalogram (EEG), empiricalmode decomposition (EMD), largest Lyapunov exponent (LLE),correlation dimension (CD), epileptic seizures. The Authors are with the Electrical and Electronic EngineeringDepartment, Bangladesh University of Engineering and Technology,Dhaka-1000, Bangladesh (e-mail: imamul@eee.buet.ac.bd) PDF Cite: S. M. Shafiul Alam,S. M. Shafiul Alam,Aurangozeb, and Syed Tarek Shahriar, "EEG Signal Discrimination using Non-linear Dynamics in the EMD Domain," International Journal of Computer and Electrical Engineering vol. 4, no. 3, pp. 326-330, 2012. PREVIOUS PAPER Perception of Emotions Using Constructive Learningthrough Speech NEXT PAPER Physical Layer Impairments Aware OVPN Connection Selection Mechanisms Copyright © 2008-2013. International Association of Computer Science and Information Technology Press (IACSIT Press) [O] . S. M. Shafiul Alam, Syed TarekShahriar 2012

机译：EEG信号在EMD域S. S. Shafiul Alam，S中的非线性动力学使用非线性动力学。 M. Shafiul Alam，Aurangozeb和Syed Tarekshahriar摘要 - 基于EMD Chaos的方法，提出了对应于健康人的EEG信号，癫痫发作期间的癫痫患者和Seizureattacks。脑电图（EEG）首先被凭经上分解为内在模式功能（IMF）。这些IMF的非线性动力学在最大范围的指数（LLE）和相关尺寸（CD）方面是量化的。本域中的混沌分析应用于与健康人相对应的大型脑电图（Asepileptic患者）（两者都有癫痫发作）。因此，所获得的LLE和CD表展的价值可以从EMD领域的其他EEG信号中清晰地区分脑电图的表达展示。本拟议的方法可以帮助研究人员以预测癫痫发作的癫痫发作技术。索引术语 - 脑电图（EEG），仿真态分解（EMD），最大的Lyapunov指数（LLE），相关维度（CD），癫痫发作。作者与电气电子和电子工程公司，孟加拉国工程和技术大学，孟加拉国达卡 - 1000（电子邮件：imamul@eee.buet.ac.bd）pdf cite：s. m. shafiul Alam，s。 M. Shafiul Alam，Aurangozeb和Syed Tarek Shahriar，“EEG信号歧视在EMD领域的非线性动态，”计算机电气工程卷国际杂志。 4，不。 3，pp。326-330,2012，上一篇论文对情绪的看法，使用建设性的学习言论下一篇论文物理层障碍意识到OVPN连接选择机制版权所有©2008-2013。国际计算机科学与信息技术协会出版社（IACSIT Press）

Revisiting signal processing with spectrogram analysis on EEG, ECG and speech signals

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