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- W4384346125 abstract "The demand for health monitoring devices is continuously increasing, particularly to measure vital signs, which are crucial to early detection and further diagnosis of diseases. This research uses a non-contact mm-wave sensor to measure signals of respiratory patterns from a programmed patient simulator, including four diseased patterns and normal control patterns. The study compares and contrasts the performance of deep learning and machine learning approaches in respiratory disease classification. The number of peaks and valleys, short-term energy, and instantaneous frequency were extracted from the sensor records into the features for training machine learning models. A simple deep-learning architecture with a one-dimension convolutional layer was also applied for classification without the need for feature extraction. The performance of machine learning and deep learning models for respiratory disease classification are evaluated using the F1-score. The results of machine learning models with varied window size, sliding size, and number of samples yield similar performance, attaining the F1-score values of up to 1.000. In contrast, deep learning model performance improves with the increasing number of samples regardless of the window and sliding sizes, without the need to perform feature extraction." @default.
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- W4384346125 date "2023-01-01" @default.
- W4384346125 modified "2023-09-25" @default.
- W4384346125 title "Respiratory Disease Classification Using Chest Movement Patterns Measured by Non-contact Sensor" @default.
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- W4384346125 doi "https://doi.org/10.1007/978-3-031-36822-6_34" @default.
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