A MATLAB-based biomedical signal de-noising applied to digital signal processing course for third-year students

Luo, Guoxi

doi:10.1177/0020720918780843

Cited by 13 publications

(15 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The frequency bands interface (Figure 15) filters the EEG data into the five frequency bands corresponding to; delta (0-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), beta (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma (30-100 Hz) by using a second-order IIR Butterworth band-pass filter [24]. Before the EEG signals are separated into frequency bands, 50 Hz power line noise is removed with the band-stop filter, and baseline drift noise is removed with the high-pass filter in the Filter Interface.…”

Section: Frequency Bands Interfacementioning

confidence: 99%

“…Students gained significant experience in biomedical amplifier design, data analysis, mobile phone communication, and interface design [3]. Luo developed an experimental application to filter the ECG signal from diaphragm EMG signals for use by students taking the digital signal processing course [22]. Nonclercq et al created a virtual data collection application to teach data acquisition for EEG data.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A computer‐aided educational device and toolbox for biomedical signal processing at the undergraduate level

Tepe

Savaştaer

2022

Comp Applic In Engineering

View full text Add to dashboard Cite

Engineering education requires both a theoretical and practical basis; some aspects are better understood through practical examples. Providing practical experience in biomedical engineering is of great importance in terms of understanding concepts and assisting the retention of learning. Hands‐on experience can be particularly beneficial in supporting the theoretical explanation of the acquisition, processing, and displaying of biomedical signals. This study has developed an expansion board that can be plugged into Raspberry Pi boards to capture electroencephalography (EEG), electrooculography (EOG), electrocardiography (ECG), and electromyography (EMG) biomedical signals with a single‐channel amplifier circuit. A toolbox has been written in the Python programming language to; capture, process, filter, apply fast fourier transform, and display these signals. The device and laboratory applications were introduced in the curriculum of a final year module, “Introduction to Biomedical Engineering” in the Department of Electrical and Electronics Engineering at Gazi University. The pedagogical value of the application was assessed by administering a questionnaire to the students. Results show the use of measurement and display of biomedical signals increased the curiosity and motivation of the students in Biomedical Engineering.

show abstract

Section: Frequency Bands Interfacementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A computer‐aided educational device and toolbox for biomedical signal processing at the undergraduate level

Tepe

Savaştaer

2022

Comp Applic In Engineering

View full text Add to dashboard Cite

show abstract

“…Signal de-noising is one of the major topics of telecommunication application covered in an undergraduate-level digital signal processing course, and it involves a number of tedious concepts which can be difficult for students to understand. Luo [10] implemented an educational method which runs on the Matlab and classroom experience in the Nanfang College of Sun Yat-sen University has shown that the developed method helps in consolidating a better understanding of signal de-noising processing in digital signal processing course.…”

Section: Literature Overviewmentioning

confidence: 99%

Teaching spread spectrum in the course Telecommunication Systems using Octave

Čisar¹,

Ódry

Čisar

et al. 2020

Comp Applic In Engineering

View full text Add to dashboard Cite

Spread spectrum is a technique used for transmitting telecommunications signals. This topic is relatively complex and can be challenging for students. Thus, programming language Octave has been introduced in the course curriculum of Telecommunication Systems as one of the powerful tools that aid in student understanding of the spread‐spectrum principles. The purpose of this paper is to elaborate on the teaching method of spread‐spectrum technique and its application in telecommunications, with the aim to make this topic understandable to students. The participants of the experiment were fifth‐semester electrical engineering students (N = 86) at a college of applied sciences. To test the previous knowledge of students needed to understand the theory of spread spectrum, a pretest was carried out which refers to general terms from the fundamentals of telecommunications. With the aim to deeper apprehend the necessary theory from this area, the students had to solve examples from engineering practice using open source software Octave, as well as computational tasks, regarding both methods in spread‐spectrum technology. It has been shown that thanks to the visualization of appropriate material, students of the experimental group understood in more detail the method of generating spread‐spectrum signals and the influence of individual parameters on partial signals, in relation to students who did not have the opportunity to use Octave. The results of the research lead us to conclude that software like Octave should be included in courses like Telecommunication Systems as a supplement to the traditional teaching and learning methods.

show abstract

“…Pada artikel ini akan dikembangkan penggunakan filter IIR pada data sinyal pernapasan EMGdi pernah dilakukan pada [2] dengan membandingkan segi keefektifan filter dengan menggunakan filter FIR. Berdasarkan [3][4] filter FIR mampu menghilangkan noise pada pengaplikasian sinyal ECG karena memiliki properti fasa yang linear dan karakteristik yang stabil serta mampu meningkatkan kecepatan kalkulasi filter sebesar 13,65%.…”

Section: Pendahuluanunclassified

“…Berdasarkan [3][4] filter FIR mampu menghilangkan noise pada pengaplikasian sinyal ECG karena memiliki properti fasa yang linear dan karakteristik yang stabil serta mampu meningkatkan kecepatan kalkulasi filter sebesar 13,65%. Oleh karena itu, artikel ini akan mencoba mengaplikasikan penelitian [2] dengan menggunakan filter FIR dan melihat apakah keefektifan filter tersebut juga berlaku ketika diaplikasikan pada sinyal EMGdi dengan menggunakan Signal-to-Noise Ratio (SNR), execution time, serta zero-pole diagram kedua filter.…”

Section: Pendahuluanunclassified

Analisis Keefektifan Penggunaan Filter FIR dan IIR pada Sinyal Pernapasan EMGdi dengan Simulasi MATLAB

Leonard

Shabrina²

2020

ULTIMA Computing

View full text Add to dashboard Cite

Instrumentasi medis digunakan untuk mendapatkan dan merekam data sinyal dari tubuh pasien salah satunya adalah sinyal pernapasan. Data dari sinyal pernapasan tersebut mampu mendiagnosa kesehatan pernapasan pasien. Dalam memperoleh data sinyal tersebut, terdapat noise yang mengganggu informasi sinyal yang diperlukan. Pada artikel ini, sinyal input akan dilakukan filter dengan dua tipe filter (filter FIR dengan metode window method dan filter IIR dengan pole-zero placement method atau bilinear transformation method) dan dibandingkan keefektifan kedua tipe filter dalam mengurangi atau menghilangkan noise sinyal. Kedua filter memiliki frekuensi cut-off sebesar 50 Hz. Sinyal output menunjukkan bahwa sinyal noise ECG lebih efektif dihilangkan menggunakan filter IIR. Oleh karena itu, filter IIR lebih direkomendasikan digunakan dalam instrumentasi medis karena keefektifan serta penggunaan perangkat keras yang minimum.

show abstract

A MATLAB-based biomedical signal de-noising applied to digital signal processing course for third-year students

Cited by 13 publications

References 10 publications

A computer‐aided educational device and toolbox for biomedical signal processing at the undergraduate level

A computer‐aided educational device and toolbox for biomedical signal processing at the undergraduate level

Teaching spread spectrum in the course Telecommunication Systems using Octave

Analisis Keefektifan Penggunaan Filter FIR dan IIR pada Sinyal Pernapasan EMGdi dengan Simulasi MATLAB

Contact Info

Product

Resources

About