Realization

This chapter describes the process of developing the program that cleans, classifies and analyzes the breathing data. The first section will discuss the process of development and how the author went about recording data. The second section describes a prediction accuracy test for the various classification models when the classifiers are trained on various features of the breathing data. The chapter ends with the a decomposition of the major functions used in the program. The functions written to clean, classify and analyze the data can be found in Appendix 11 F.

6.1 Development

While developing the program, the author constantly tested it on data recorded by himself in order to verify the functionality of the code. To train the classifiers, the author recorded 10 minutes of data while in the seated position and 10 minutes of data in the standing position. Of those 10 minute sessions, 5 minutes were spent breathing diaphragmatically and the other 5 were spent breathing primarily using the thorax (upper chest). The accuracy of various classifiers is investigated and compared in Appendix 11 D. with the combination of features that yielded that highest accuracy is covered in Section 6.2.

Scikit-learn, a popular machine learning library for python, will be utilized for the classification algorithms used in this project (Scikit-learn.org, 2018). The author recorded a smaller ratio of training data compared to testing data in order to model a real world situation as closely as possible. If this product was to be released on the market, the users could not be expected to sit through hours of sessions where they are to record training data. The calibration routines would be short and easy. Therefore the models were trained with relatively little training data to the ratio of testing data (recording sessions).

The author recorded his own breathing spanning hours as well as shorter amounts of time, where he was more aware of the type of breathing he was using, to see if this was reflected in the results of the classification. During recordings where the author deliberately used more diaphragmatic breathing, there was a positive relationship between the amount of time spent breathing diaphragmatically and the number of DBMs, as well as the quality factor.

A sliding window was used to analyze the raw data, where each frame was one minute long. The classification of the data can be visualized by the graph in Image 6.1 below.

Image 6.1:​ Visualization of how the data was analyzed and classified

6.1.1 Main program functions

The implementation of the five main functions of the program as described in the Functions and Events in the FICS analysis in Section 5.1 are shown here.

function, _​remove_noise_training_data(trainingData)​ is used to remove noise and artefacts from the training data recorded.

Classification models are trained on data recorded separately that were compiled into files containing moving data, stationary data, chest breathing and diaphragmatic breathing data. The functions

get_trained_breathing_classifier(trainingData, labels) ​and

get_trained_movement_classifier(trainingData, labels)​ are used to return classifiers for breathing and movement respectively that are trained on the training data recorded. The labels argument to both functions is a list of labels like “Diaphragmatic” or “Chest” to allow the classify to relate each data sample with a label to teach itself.

Classify the data from the recording sessions in terms of “Diaphragmatic breathing”, “Repetitive breathing” and “Movement”. The functions get_breathing_prediction(clf, data, threshold,

unfilteredData) and get_movement_prediction(clf, data) add the classification labels to the ends of each sample in the data variable provided as input. Both functions take a classifier as an argument, which are the trained classifiers from the previous functions. In the case of the breathing prediction function, it also takes the threshold level inputted at the beginning of the program and the unfiltered data. Unfiltered data is also included in the classification of breathing due to results in Section 6.2.

The fourth function is to extract features from the data. This includes breathing frequency per minute, the number of DBMs per day and the other such features listed in Section 4.8. The function

db_features(data)​ gets the data, where each sample has all of its labels appended (diaphragmatic, repetitive and moving). The function checks for the times a moment begins and ends by looking at the diaphragmatic label, calculates the length of the duration of a DBM and finds the longest duration. It also compiles the breathing frequencies per minute. A full explanation of this can be found in Section 5.4.3.

The fifth and final function is that of compiling the aforementioned features and results of the

classification in order to display them to the user using the Graphical User Interface designed by Florian Naumilkat. This function is carried out by ​write_features_to_file(features, name)​, which takes the list of features as an argument as well as the name of the file that the features should be written to. If the file already exists in the folder, the program overwrites the file, and if the file does not exist, it creates a file with the name specified and writes the features to it.

6.2 Classifier accuracy tests

In order to choose the right classifier for each type of data, RIP and accelerometer, tests were carried out comparing their prediction accuracies. A function from the scikit-learn python library called train_test_split() is used in order to split the labeled data into training and testing data, where the size of the testing data can be specified as a parameter for the function. Considering that only 20 minutes of training data in total will be recorded during the program tests, and that the ratio of the training data to

the testing data is quite low due to an hour of testing data being recorded, the test split will be taken as 0.9 or 90% of the training data.

The classifiers that are compared are a Support Vector Machine, a Neural Network, and a Logistic regression classifier. The Logistic regression classifier is the only linear classifier that is considered; the other classifiers belong to the non-linear category.

6.2.1 Breathing classifier test

For the breathing classifiers, different combinations of features were tested in order to find the

combination that yields the highest prediction accuracy. The inclusion of extra features was a suggestion from Mrs. Garde, interviewed in Section 4.5 during the Expert Review. The features relevant to the RIP data are the raw values obtained from the two bands, their relative contribution to their sum signal and their filtered values. The breathing classifiers all have various parameters that they can be initialized with, for example, with a Neural Network, the number of layers in between the input and output layers can be specified. Due to the scope of this project, this test will use the classifiers on the basis of their default parameter values.

While testing the various classifiers, the function train_test_split from scikit-learn’s library shall be used to split the training data into training and testing data. In other words, this ratio represents the data from the original training set that will be used for testing. This ratio between training and testing data is specified as a parameter to the train_test_split function. Considering the ratio of training data to breathing session data is notably low, the data will be split with a ratio of 1:10 training to testing data.

Raw data, filtered data and relative contribution

All the features regarding the RIP data were compiled to train the classifiers. This means for each sample, the raw RIP values, the band pass filtered RIP values and the respective contributions to the RIP sum value. This combination yielded the highest accuracy for the SVM from all the tested combinations found in Appendix 11.D. Seeing as the SVM outperformed the other classifiers in classifying

diaphragmatic breathing, it will be chosen as the classifier for diaphragmatic breathing and this combination of values will be used to train it.

Concluding from the tests above, the combination of the raw RIP values, band pass filtered RIP values and their respective contributions to the RIP sum value yields the highest prediction accuracy for all the classifiers. The highest prediction accuracy is achieved by the SVM classifier and will therefore be chosen to classify the breathing data.

6.2.2 Movement classifier test

The same classifiers tested for the breathing data will be used to classify motion. The classifiers are trained on the raw data coming from the accelerometer, which has been labeled according to the activity performed during the recordings by the researcher. The SVM and Logistic Regression analysis and both did exceptionally well in differentiating between “Stationary” and “Moving” data.

Image 6.3: ​Movement prediction scores of classifiers trained on raw motion data It was previously suggested by Mrs. Garde that a linear classifier should be sufficient to classify

breathing data; however in light of these tests, and giving priority to the scenario where the amount of unlabeled testing data will outweigh the training data, the author decides to utilize an SVM for

classification of both the breathing data and movement data.

It should be noted however, that the while recording the training data, the author made a conscious effort to distinguish between diaphragmatic and chest breathing. This slight exaggeration may have played a role in oversensitizing the classifier, so it may miss the more subtle breathing values that lie in between the two extremes of diaphragmatic and chest breathing.

6.3 Program decomposition

This section serves to provide the reader with an overview of the functions the constitute the System Architecture components shown in Figure 5.2, which is shown again on the next page.

Image 5.2:​ System Architecture - Level 1

The blocks that were already covered in Section 6.1.1 and in Section 5.4 will be skipped. Please refer to that section for an explanation.