Projects:2020s2-7451 Analysing Health Data from Wearables - Revision history

A1789120: /* HR and RR Calculation */

2021-06-07T21:03:23Z

‎HR and RR Calculation

A1789120: /* HR and RR Calculation */

2021-06-07T21:01:16Z

‎HR and RR Calculation

A1789120: /* HR and RR Calculation */

2021-06-07T20:49:09Z

‎HR and RR Calculation

A1789120: /* HR and RR Calculation */

2021-06-07T20:46:24Z

‎HR and RR Calculation

A1789120: /* HR and RR Calculation */

2021-06-07T20:43:43Z

‎HR and RR Calculation

A1702270: /* GUI Design and User Editing Function */

2021-06-07T10:16:32Z

‎GUI Design and User Editing Function

A1702270: /* HR and RR Calculation */

2021-06-07T09:52:13Z

‎HR and RR Calculation

A1702270: /* Arfifact Detection and Removal */

2021-06-07T09:01:23Z

‎Arfifact Detection and Removal

A1702270: /* Arfifact Detection and Removal */

2021-06-07T06:52:28Z

‎Arfifact Detection and Removal

A1702270: /* Support Vector Machine */

2021-06-07T05:13:46Z

‎Support Vector Machine

@@ Line 90: / Line 90: @@
 For finding the HR from the PPG waveform, the systolic peaks of the PPG waveform were found by "findpeak" MATLAB function. The heart rate is equal to the ratio of the numbers of the detected peaks to the time duration of the PPG signal. For the analysed PPG waveform, the time duration for only good sections was used. This algorithms are proved to provide a good estimation of the heart rate in a noise-free PPG signal and to calculate the accuracy of this algorithms, 47 data which have already been pre-processed are used and the results shows that this algorithms could provide an accuracy of 97.67% even when the pre-processing stage are done poorly in the context of this project.
 [[File:Capture11.png|thumb|Variation induced signals]]
-For RR calculation, feature based extraction have to be carried out first in order to generate 3 signals; respiratory-induced amplitude variation (RIAV), respiratory-induced frequency variation (RIFV), and respiratory-induced intensity variation (RIIV). RIAV could be generated by calculating the change in pulse strengh, RIIV is calculated by connecting consecutive peaks in the whole signal, while RIFV is generated by calculating the distance between each peaks. After this 3 variations induced signal are generated, a respiratory signal could be formed by combining these 3 signals into one signal using fusion method. A fusion method used in this project is done by calculating the mean of the induced variations signal for the whole time duration. The respiration signal represents the waveform for breaths. Similar to the HR calculation, the respiration rate is equal to the ratio of the numbers of the detected peaks to the time duration of the respiration signal.
+For RR calculation, feature based extraction have to be carried out first in order to generate 3 signals; respiratory-induced amplitude variation (RIAV), respiratory-induced frequency variation (RIFV), and respiratory-induced intensity variation (RIIV). RIAV could be generated by calculating the change in pulse strengh, RIIV is calculated by connecting consecutive peaks in the whole signal, while RIFV is generated by calculating the distance between each peaks. After this 3 variations induced signal are generated, a respiratory signal could be formed by combining these 3 signals into one signal using fusion method. A fusion method used in this project is done by calculating the mean of the induced variations signal for the whole time duration. The respiration signal represents the waveform for breaths. Similar to the HR calculation, the respiration rate is equal to the ratio of the numbers of the detected peaks to the time duration of the respiration signal. However in our project, the accuracy of the RR estimation are not quite accurate since this process depends a lot on the outcome of the prior stage; noise or artifacts elimination.
 === GUI Design and User Editing Function ===

@@ Line 89: / Line 89: @@
 [[File:Capture4.png|thumb|HR Estimation]]
 For finding the HR from the PPG waveform, the systolic peaks of the PPG waveform were found by "findpeak" MATLAB function. The heart rate is equal to the ratio of the numbers of the detected peaks to the time duration of the PPG signal. For the analysed PPG waveform, the time duration for only good sections was used. This algorithms are proved to provide a good estimation of the heart rate in a noise-free PPG signal and to calculate the accuracy of this algorithms, 47 data which have already been pre-processed are used and the results shows that this algorithms could provide an accuracy of 97.67% even when the pre-processing stage are done poorly in the context of this project.
 [[File:Capture11.png|thumb|Variation induced signals]]
-For RR calculation, the first step was to label all the local maxima and minima in the PPG signals. Then, the corresponding peaks were connected for extracting the restoration signal. The respiration signal represents the waveform for breaths. Similar to the HR calculation, the respiration rate is equal to the ratio of the numbers of the detected peaks to the time duration of the respiration signal.
+For RR calculation, feature based extraction have to be carried out first in order to generate 3 signals; respiratory-induced amplitude variation (RIAV), respiratory-induced frequency variation (RIFV), and respiratory-induced intensity variation (RIIV). RIAV could be generated by calculating the change in pulse strengh, RIIV is calculated by connecting consecutive peaks in the whole signal, while RIFV is generated by calculating the distance between each peaks. After this 3 variations induced signal are generated, a respiratory signal could be formed by combining these 3 signals into one signal using fusion method. A fusion method used in this project is done by calculating the mean of the induced variations signal for the whole time duration. The respiration signal represents the waveform for breaths. Similar to the HR calculation, the respiration rate is equal to the ratio of the numbers of the detected peaks to the time duration of the respiration signal.
 === GUI Design and User Editing Function ===

@@ Line 88: / Line 88: @@
 === HR and RR Calculation ===
 [[File:Capture4.png|thumb|HR Estimation]]
-For finding the HR from the PPG waveform, the systolic peaks of the PPG waveform were found by "findpeak" MATLAB function. The heart rate is equal to the ratio of the numbers of the detected peaks to the time duration of the PPG signal. For the analysed PPG waveform, the time duration for only good sections was used. This algorithms are proved to provide a good estimation of the heart rate in a noise-free PPG signal and to calculate the accuracy of this algorithms, 47 data which have already been pre-processed are used and the results shows that this algorithms could provide an accuracy of 97.67% in the context of this project.
+For finding the HR from the PPG waveform, the systolic peaks of the PPG waveform were found by "findpeak" MATLAB function. The heart rate is equal to the ratio of the numbers of the detected peaks to the time duration of the PPG signal. For the analysed PPG waveform, the time duration for only good sections was used. This algorithms are proved to provide a good estimation of the heart rate in a noise-free PPG signal and to calculate the accuracy of this algorithms, 47 data which have already been pre-processed are used and the results shows that this algorithms could provide an accuracy of 97.67% even when the pre-processing stage are done poorly in the context of this project.
 For RR calculation, the first step was to label all the local maxima and minima in the PPG signals. Then, the corresponding peaks were connected for extracting the restoration signal. The respiration signal represents the waveform for breaths. Similar to the HR calculation, the respiration rate is equal to the ratio of the numbers of the detected peaks to the time duration of the respiration signal.

← Older revision		Revision as of 20:46, 7 June 2021
Line 87:		Line 87:

	=== HR and RR Calculation ===		=== HR and RR Calculation ===
		+	[[File:Capture4.png\|thumb\|HR Estimation]]
	For finding the HR from the PPG waveform, the systolic peaks of the PPG waveform were found by "findpeak" MATLAB function. The heart rate is equal to the ratio of the numbers of the detected peaks to the time duration of the PPG signal. For the analysed PPG waveform, the time duration for only good sections was used. This algorithms are proved to provide a good estimation of the heart rate in a noise-free PPG signal and to calculate the accuracy of this algorithms, 47 data which have already been pre-processed are used and the results shows that this algorithms could provide an accuracy of 97.67% in the context of this project.		For finding the HR from the PPG waveform, the systolic peaks of the PPG waveform were found by "findpeak" MATLAB function. The heart rate is equal to the ratio of the numbers of the detected peaks to the time duration of the PPG signal. For the analysed PPG waveform, the time duration for only good sections was used. This algorithms are proved to provide a good estimation of the heart rate in a noise-free PPG signal and to calculate the accuracy of this algorithms, 47 data which have already been pre-processed are used and the results shows that this algorithms could provide an accuracy of 97.67% in the context of this project.

@@ Line 87: / Line 87: @@
 === HR and RR Calculation ===
-For finding the HR from the PPG waveform, the systolic peaks of the PPG waveform were found by "findpeak" MATLAB function. The heart rate is equal to the ratio of the numbers of the detected peaks to the time duration of the PPG signal. For the analysed PPG waveform, the time duration for only good sections was used.
+For finding the HR from the PPG waveform, the systolic peaks of the PPG waveform were found by "findpeak" MATLAB function. The heart rate is equal to the ratio of the numbers of the detected peaks to the time duration of the PPG signal. For the analysed PPG waveform, the time duration for only good sections was used. This algorithms are proved to provide a good estimation of the heart rate in a noise-free PPG signal and to calculate the accuracy of this algorithms, 47 data which have already been pre-processed are used and the results shows that this algorithms could provide an accuracy of 97.67% in the context of this project.
 For RR calculation, the first step was to label all the local maxima and minima in the PPG signals. Then, the corresponding peaks were connected for extracting the restoration signal. The respiration signal represents the waveform for breaths. Similar to the HR calculation, the respiration rate is equal to the ratio of the numbers of the detected peaks to the time duration of the respiration signal.

← Older revision		Revision as of 10:16, 7 June 2021
Line 92:		Line 92:

	=== GUI Design and User Editing Function ===		=== GUI Design and User Editing Function ===
		+	[[File:GUI2.png\|thumb]]
		+
		+	The figure above shows the final version of the GUI design, it was designed by using the App Designer. The following paragraph explained the functions for each element in GUI such as buttons and plots. In addition, the User Editing Function was displayed in this GUI under the tabs – “User Editing Function” and “Analysed PPG waveform after user editing”. The integrated code was moved and modified into a code designing panel under App Designer.
		+
		+	The App Designer were mainly two views for design – design view and code view. In the design view, there were some common components provided such as axes and button. Overall, the axes were used to display the waveforms as plotted figures; the text fields were used to show the calculated values of HR and RR, and the buttons were used to execute the commands when pressed.
		+
		+	The users could select which PPG data by pressing the "Changed Pathway" button. If the users do not have any PPG data, the GUI would automatically analyse the sample PPG provided. Before clicking the "Analyse Signal" button, the users are required to enter the set number of PPG data they want to analyse. After licking the "Analyse Signal" button, the original and analysed waveforms would be shown. The red sections are the detected artifacts and the other blue section represented the good signal. Additionally, the HR and RR were calculated and displayed in the GUI.
		+
		+	The user editing function allowed users to modify the artifact sections by themselves. Some artifacts might not be detected and removed, and even some good signals could be mis-detected as artifacts. Each time users pressed the button - "Add Artifact Section" and "Remove Artifact Section", the users are asked to select the start and end points for the section added and removed. After each editing, the HR and RR would be calculated and updated. The users could also reset all the editing by pressing the "Reset" button. The users could also be saved all the PPG data, HR and RR values into a new file.

	== Conclusion ==		== Conclusion ==

← Older revision		Revision as of 09:52, 7 June 2021
Line 87:		Line 87:

	=== HR and RR Calculation ===		=== HR and RR Calculation ===
		+	For finding the HR from the PPG waveform, the systolic peaks of the PPG waveform were found by "findpeak" MATLAB function. The heart rate is equal to the ratio of the numbers of the detected peaks to the time duration of the PPG signal. For the analysed PPG waveform, the time duration for only good sections was used.

		+	For RR calculation, the first step was to label all the local maxima and minima in the PPG signals. Then, the corresponding peaks were connected for extracting the restoration signal. The respiration signal represents the waveform for breaths. Similar to the HR calculation, the respiration rate is equal to the ratio of the numbers of the detected peaks to the time duration of the respiration signal.

	=== GUI Design and User Editing Function ===		=== GUI Design and User Editing Function ===

@@ Line 78: / Line 78: @@
 === Arfifact Detection and Removal ===
-The PPG data used for this project is "PPG.mat" provided by the supervisor. There were 100 PPG signal data that had different performances for analysing.
+The PPG data used for this project is "PPG.mat" provided by the supervisor. 100 PPG signal data had different performances for analysing.
 There were two methods for removing the artifacts. The first method was to detect the peaks for the PPG waveforms. Then to set some conditions for classifying the artifacts section such as the peak to peak differences and the amplitudes of peaks. According to the tests, the accuracy of this method was above 90% for most signal. IF the signals were too bad for analysing, the accuracy of this method would become extremely low.
-Another method was to use the SVM design. Firstly, the features of the PPG signal were extracted such as the  Peak to peak difference. Then, to select some training data of good and artifact sections for training the SVM
+Another method was to use the SVM design. Firstly, the whole PPG waveform was separated into 20 - 30 sections, and the features of the PPG signal were extracted such as the  Peak to peak difference. Then, to select some training data of good and artifact sections for training the SVM. As a result, the SVM would show which sections are artifact and which are good sections. The result of the training showed that the accuracy of the SVM was more than 97%.
 === HR and RR Calculation ===

@@ Line 53: / Line 53: @@
 === Support Vector Machine ===
-The research review - “Automatic EEG Artifact Removal: A Weighted Support Vector Machine Approach With Error Correction” – which written by <ref>S.-Y. Shao, K.-Q. Shen, C. J. Ong and E. P. Wilder-Smith, “Automatic EEG Artifact Removal: A Weighted Support Vector Machine Approach With Error Correction,” IEEE transactions on biomedical engineering, vol. 56, no. 2, pp. 336-344, February 2009. . was used to understand the processes of using an SVM for detecting artifacts.
+The research review - “Automatic EEG Artifact Removal: A Weighted Support Vector Machine Approach With Error Correction” – which written by <ref>S.-Y. Shao, K.-Q. Shen, C. J. Ong and E. P. Wilder-Smith, “Automatic EEG Artifact Removal: A Weighted Support Vector Machine Approach With Error Correction,” IEEE transactions on biomedical engineering, vol. 56, no. 2, pp. 336-344, February 2009. </ref> . was used to understand the processes of using an SVM for detecting artifacts.
 [[File:The structure of the automatic artifact removal system .png|thumb]]
@@ Line 60: / Line 60: @@
 According to the testing results, approximate 98% of artifacts were removed in the artifact-free EEG. There were only 2% of artifact which were not detected and removed. Hence, the performance was significantly reduced if the training data was not accurate enough.
 To sum up, this review explained the basic structure of SVM design. However, the SVM design in the review used has already been improved by professionals and it was only appropriate for ECG. During the project, the team were required to design their own SVM for PPG and the performance of SVM design could be not as accurate as of the performance for SVM used in this review. The reason was the accuracy of the SVM design would be reduced if the training data was not selected appropriately.
 == Method ==