![\"MTE-40031](\"https:\/\/onlineassignmentservices.com\/wp-content\/uploads\/2024\/03\/MTE-40031-Biomedical-Signal-Processing-and-Analysing-Assignment-Help.png\")
<\/p>\n<\/p>\n
<\/p>\n
<\/p>\n
MTE-40031: This assignment for Keele University requires the student to write a structured report in order to demonstrate comprehension of the data processing and analysis listed in the assessment file. It is necessary for the student to use MATLAB for the data analysis, along with utilizing other requirements like a goniometer signal and two EMG signals. The complexity of this assignment makes it difficult for the Biomedical Engineering students to complete this assessment, which is why they trust OAS subject matter experts for this MSc Biomedical Engineering Assignment Help.<\/b><\/p>\n
<\/p>\n
The solution to this assignment consists of a Biomedical Signal Processing Analysis done by our experts using MATLAB, as per the assessment file. In providing Keele University Assignment Help for this analysis, our experts have first loaded the raw data files and gave unique variable names to each of the columns. Then, the analysis was done, based on which all five questions were answered by our experts.\u00a0<\/span><\/i><\/p>\n <\/p>\n <\/p>\n The Repeated_Flexion_Extension and Fatigue_Trial files provided in the task file have been utilized to normalize the EMG data. Additionally, our experts have computed the envelopes of emg1_mvc and emg2_mvc first, and then determined each envelope’s maximum value.\u00a0<\/span><\/i><\/p>\n <\/p>\n % Calculate the envelopes of EMG1mvc and EMG2mvc<\/b><\/p>\n envelope_emg1_mvc = abs(hilbert(EMG1mvc));<\/span><\/p>\n envelope_emg2_mvc = abs(hilbert(EMG2mvc));<\/span><\/p>\n <\/p>\n % Find the maximum values of the envelopes<\/b><\/p>\n max_envelope_emg1_mvc = max(envelope_emg1_mvc);<\/span><\/p>\n max_envelope_emg2_mvc = max(envelope_emg2_mvc);<\/span><\/p>\n <\/p>\n Feeling stuck with this assignment? Don\u2019t worry. Buy MTE-40031 assignment help from OAS at 25% off. WhatsApp us at +447700174710 today!<\/i><\/b><\/span><\/p>\n <\/p>\n <\/p>\n The second question demands the student to eliminate any artifacts from the signals. This question is further divided into different sections:\u00a0<\/span><\/i><\/p>\n <\/p>\n <\/p>\n In this part, our experts have utilized the Butterworth filter to describe the selection of cut-off frequencies and the rationale behind using a 4th order filter.\u00a0<\/span><\/i><\/p>\n <\/p>\n % Define the filter order and cutoff frequency<\/b><\/p>\n filter_order = 4; % Filter order<\/span><\/p>\n cutoff_freq = 100; % Cutoff frequency in Hz<\/span><\/p>\n sampling_freq = 1000; % Sampling frequency in Hz<\/span><\/p>\n <\/p>\n % Compute the normalized cutoff frequency<\/b><\/p>\n normalized_cutoff_freq = cutoff_freq \/ (sampling_freq \/ 2);<\/span><\/p>\n <\/p>\n <\/p>\n In providing MTE-40031 assignment help for this sub-section, our experts have examined and discussed the variations in frequency spectra prior to and following filtering.<\/span><\/i><\/p>\n <\/p>\n % Compute the FFT of the normalized signals before filtering<\/span><\/p>\n fft_EMG1_before_filtering = fft(normalized_EMG1_Repeated_Flexion_Extension);<\/span><\/p>\n fft_EMG2_before_filtering = fft(normalized_EMG2_Repeated_Flexion_Extension);<\/span><\/p>\n % Compute the frequency axis for plotting<\/b><\/p>\n N = length(normalized_EMG1_Repeated_Flexion_Extension);<\/span><\/p>\n frequencies = (0:N\/2-1) * (sampling_freq \/ N);<\/span><\/p>\n <\/p>\n % Plot the frequency spectra before filtering<\/b><\/p>\n figure;<\/span><\/p>\n subplot(2,1,1);<\/span><\/p>\n plot(frequencies, abs(fft_EMG1_before_filtering(1:N\/2)));<\/span><\/p>\n title(‘Frequency Spectrum of EMG1 (Before Filtering)’);<\/span><\/p>\n xlabel(‘Frequency (Hz)’);<\/span><\/p>\n ylabel(‘Magnitude’);<\/span><\/p>\n subplot(2,1,2);<\/span><\/p>\n plot(frequencies, abs(fft_EMG2_before_filtering(1:N\/2)));<\/span><\/p>\n title(‘Frequency Spectrum of EMG2 (Before Filtering)’);<\/span><\/p>\n xlabel(‘Frequency (Hz)’);<\/span><\/p>\n ylabel(‘Magnitude’);<\/span><\/p>\n <\/p>\n % Compute the FFT of the filtered signals<\/b><\/p>\n fft_EMG1_after_filtering = fft(filtered_EMG1_Repeated_Flexion_Extension);<\/span><\/p>\n fft_EMG2_after_filtering = fft(filtered_EMG2_Repeated_Flexion_Extension);<\/span><\/p>\n <\/p>\n <\/p>\n The last part of this question displays the raw and filtered signal versus time for each signal in the Repeated_Flexion_Extension and Fatigue_Trial data in a plot with two subplots. You can read a snippet of the complete solution below:\u00a0<\/span><\/i><\/p>\n <\/p>\n % Define the time axis<\/b><\/p>\n time_rfe = (0:length(EMG1rfe)-1) \/ sampling_freq;<\/span><\/p>\n time_ft = (0:length(EMG1ft)-1) \/ sampling_freq;<\/span><\/p>\n <\/p>\n % Plot the raw and filtered signals from Repeated_Flexion_Extension<\/b><\/p>\n figure;<\/span><\/p>\n subplot(2,2,1);<\/span><\/p>\n plot(time_rfe, EMG1rfe);<\/span><\/p>\n title(‘Raw EMG1 – Repeated Flexion Extension’);<\/span><\/p>\n xlabel(‘Time (s)’);<\/span><\/p>\n ylabel(‘Amplitude’);<\/span><\/p>\n subplot(2,2,2);<\/span><\/p>\n plot(time_rfe, filtered_EMG1_Repeated_Flexion_Extension);<\/span><\/p>\n title(‘Filtered EMG1 – Repeated Flexion Extension’);<\/span><\/p>\n xlabel(‘Time (s)’);<\/span><\/p>\n ylabel(‘Amplitude’);<\/span><\/p>\n subplot(2,2,3);<\/span><\/p>\n plot(time_rfe, EMG2rfe);<\/span><\/p>\n title(‘Raw EMG2 – Repeated Flexion Extension’);<\/span><\/p>\n xlabel(‘Time (s)’);<\/span><\/p>\n ylabel(‘Amplitude’);<\/span><\/p>\n subplot(2,2,4);<\/span><\/p>\n plot(time_rfe, filtered_EMG2_Repeated_Flexion_Extension);<\/span><\/p>\n title(‘Filtered EMG2 – Repeated Flexion Extension’);<\/span><\/p>\n xlabel(‘Time (s)’);<\/span><\/p>\n ylabel(‘Amplitude’);<\/span><\/p>\n <\/p>\n If you are looking for Biomedical Engineering assignment help, don\u2019t hesitate to call us at +61 871501720.<\/i><\/b><\/span><\/p>\n <\/p>\n <\/p>\n <\/p>\n By comparing the timing of the EMG activity with the goniometer data from the Repeated_Flexion_Extension data, our highly skilled subject matter experts have determined which EMG signal was captured from each muscle. We provide a step-by-step analysis and a clear description of the results. This is how we provide the best biomedical engineering assignment help in England.<\/span><\/i><\/p>\n <\/p>\n % Rectify the filtered EMG1 and EMG2 signals<\/b><\/p>\n rectified_EMG1 = abs(filtered_EMG1_Repeated_Flexion_Extension);<\/span><\/p>\n rectified_EMG2 = abs(filtered_EMG2_Repeated_Flexion_Extension);<\/span><\/p>\n <\/p>\n % Obtain the upper envelope of each signal using the envelope function<\/b><\/p>\nQuestion 1<\/b><\/h3>\n
Question 2<\/b><\/h3>\n
Part A<\/i><\/b><\/h4>\n
Part B<\/i><\/b><\/h4>\n
Part C<\/i><\/b><\/h4>\n
![\"Order](\"https:\/\/onlineassignmentservices.com\/wp-content\/uploads\/2024\/03\/Order-today-and-get-Buy-2-Get-1-Free-for-MTE-40031.png\")
<\/p>\nQuestion 3<\/b><\/h3>\n
![\"MTE-40031](\"https:\/\/onlineassignmentservices.com\/wp-content\/uploads\/2024\/03\/MTE-40031-Question-5-Graph.png\")
<\/p>\n![\"Book](\"https:\/\/onlineassignmentservices.com\/wp-content\/uploads\/2024\/03\/Book-your-Biomedical-Engineering-Assignment-Now-forMTE-40031.png\")
<\/p>\n","protected":false},"excerpt":{"rendered":"