Smart phone apps for respiratory sound analysis
Wheezy breaths are key indicators of possible airway obstruction disorders such as asthma along with cough severity and abnormal breathing patterns[1]. The Stethoscope is usually the main form of listening to possibly obstructed lung airways or abnormal heart rate. However, some of such adventitious...
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| Format: | Final Year Project |
| Language: | English |
| Published: |
2018
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| Online Access: | http://hdl.handle.net/10356/75091 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | Wheezy breaths are key indicators of possible airway obstruction disorders such as asthma along with cough severity and abnormal breathing patterns[1]. The Stethoscope is usually the main form of listening to possibly obstructed lung airways or abnormal heart rate. However, some of such adventitious sounds observed to computerised sound data may not be audible to the human ear when using the stethoscope due to these data not prevalent in our more sensitive frequency band width. Therefore, in order to increase the accuracy of auscultation, digitalisation and machine learning methods has been researched on in recent studies. With increasing digitalisation of the modern world, possibilities has widened with readily available methods to diagnose symptoms that require frequent monitoring. The availability of such a technology would aid parents in detecting wheezing in their child at the comfort of their homes. The objective of this project will be to develop an android mobile application to diagnose if wheezing exists in a recording of human breathing. Two linear analysis methods of detecting wheezing symptoms, namely Kurtosis and Mean Crossing Irregularity, are evaluated for their accuracy. After which, the best method for wheeze detection is used in the mobile application. The deliverables are visualisations of the two algorithms accuracy in wheeze
detection and an android application for wheeze detection. The main function of the mobile application is to record an episode of breathing and display diagnosis of wheezing detection using a feature extracted earlier in the research comparison. The feature used is Mean Crossing Irregularity whereby the deviation from the mean is divided by the average value of a data set. |
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