Software for feature identification

Interactive software was developed in MATLAB Graphic User Interface (GUI) toolbox to assist in time feature identification on images and physiological signals beat-by-beat. Three independent components were designed for the analysis of the echocardiogram, ECGs and recordings from the TaskForce Monitor system separately. The following 5 stages were applied in sequence when using the software for the feature identification on each signal:

 Stage 1. Loading the signal to be analysed into the software;

 Stage 2. For physiological signals, pre-processing filtering procedures to remove the baseline wander caused by respiratory and motion and high frequency interference from main power supply were first implemented. Automatic feature identification algorithms were then applied;

 Stage 3. Displaying the signal beat-by-beat, with the automatically detected features marked on it;

 Stage 4. Manually checking the results from the automatic detection algorithms and identifying the other features;

 Stage 5. Saving the final results into an excel sheet for further analysis.

The interface of the component for echocardiographic feature identification is shown in Figure 4-1, which consisted of 6 main elements. The 1st component was a customized toolbar allowing the loading of the signal into the program and exporting identification results out to excel sheets. The 2nd element included a slide bar which was used for beat selection. The selected beat was displayed on the window provided by the 3rd element. The 4th element contained a set of editing boxes with each corresponding to a feature. A cursor was activated by right clicking a box to help to detect the position of the selected feature on the displayed signal. The box was then updated by the absolute position of the feature in pixels (each was 5 ms wide for M-mode and Doppler echocardiograms). The 5th element

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was a table, which saved the identification results. The values given in this table were the relative position of the features in time (unit: ms) measured from the ECG R wave in the same beat, which were exported for further analysis. The 6th element had a series of check boxes and a window for result visualization.

An example is shown on Figure 4-1 of use of the software for the feature identification on aortic flow tracing from Doppler echocardiography. The start, peak and end points of the aortic flow were identified manually on each beat and indicated by solid vertical bars on the figure. The ECG R wave, indicated by the dashed bar on the plot, was identified automatically on the simultaneous recording from the ECG device. There were 12 beats contained in this recording. The times measured from each beat were listed in the table. A quick check of the variation of the identified features across all beats was enabled by the results displayed on the window of the right bottom.

4-5 4. Feature selection panel 5. Result recording table 6. Result display window

Figure 4-1. Illustration of the interface of the software developed for timing feature identification on echocardiograms. The software comprised 6 elements, which are labeled on the figure. An example is given to demonstrate the usage of the software for feature identification on Doppler image for aortic flow. Vst, Vp and Ved here denote the start, peak and end points of the flow respectively.

1. Customized toolbar R Vst Vp Ved 3. Signal display window 2. Beat selection slide bar

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The interfaces of the components for timing feature identification on ECGs and the recordings from the TaskForce Monitor system were similar with that for the images. However, because there were multiple leads included in each ECG recording, a popup menu was added to allow selecting the lead to be analyzed. In addition, because the main ECG characteristic components, the P wave, QRS complex and the T wave have different frequency ranges, it was better to identify the features on them separately rather than deal with them as a whole. Therefore, another popup menu was designed to select the characteristic component to be analyzed. According to the selection, corresponding pre- processing procedure and the automatic feature identification algorithm were implemented.

The signal selection and display elements of the interface for the ECG feature identification, with an example for P wave analysis, are shown in Figure 4-2. Once the P wave was selected, its peak was identified by an automatic algorithm. Then the original waveform was displayed beat-by-beat, with the identified peak position on it. The plot was zoomed in order to give a large P wave. A smoothed waveform, generated by applying a smoothing filter on the original signal was also displayed, from which the start and end points of the P wave were detected. The design of filtering procedure and the identification methods are described in a later section of this chapter.

Figure 4-2. The signal selection and display elements of the software for ECG analysis. The selection of different leads and characteristic components to be analyzed was allowed by the two popup menus on the right side. An example of using the software for the feature detection on the P wave of Lead I is shown. The automatically identified P wave peak was indicated by the red circle, and the manually detected start and end of the P wave were indicated by the green circles.

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As in the previous description, the signals recorded by the TaskForce Minitor system included a synchronous ECG, thoracic impedance and pulse waveforms. An integrated component was developed to assist feature identification on these signals. The selection of the signal to be analyzed was enabled by a popup menu. A pre-processing procedure and automatic feature identification algorithm were then applied to the selected signal. Because the identification of features on the impedance and pulse waveforms was referred to the ECG times, the synchronous ECG and automatically identified times were also displayed. An example for the pulse analysis by using the software is shown in the Figure 4-3. The selected beat of the pulse waveform is displayed with the synchronous ECG. The automatically identified pulse foot, early systolic peak, notch and diastolic peak were marked by red markers on the pulse waveform. The ECG P peak, R wave and T peak were also identified automatically, and marked on the ECG waveform. The pre-processing procedure and automatic feature identification algorithm on each signal is described in a later section in this chapter.

Figure 4-3. The signal selection and display elements of the software for the analysis of signals from the TaskForce Monitor system. The selection of the signal to be analyzed was allowed by the popup menu on the right side. The selected signal was displayed with the synchronous ECG. An example of using the software for pulse analysis is shown. The automatically identified time features on the pulse and ECG waveforms were indicated by the red markers.

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4.3 Feature identification