Abstract
This paper proposes an approach to the classification of respiration states based on a neural network model by visualizing respiratory signals using a spectrogram. The analysis and processing of human biosignals are still considered some of the most crucial and fundamental research areas in both signal processing and medical applications. Recently, learning-based algorithms in signal and image processing for medical applications have shown significant improvement from both quantitative and qualitative perspectives. Human respiration is still considered an important factor for diagnosis, and it plays a key role in preventing fatal diseases in practice. This paper chiefly deals with a contactless-based approach for the acquisition of respiration data using an ultra-wideband (UWB) radar sensor because it is simple and easy for use in an experimental setup and shows high accuracy in distance estimation. This paper proposes the classification of respiratory states by using a feature visualization scheme, a spectrogram, and a neural network model. The proposed method shows competitive and promising results in the classification of respiratory states. The experimental results also show that the method provides better accuracy (precision: 0.86 and specificity: 0.90) than conventional methods that use expensive equipment for respiration measurement.
Highlights
Biosignals have played an important role in the diagnosis and treatment of diseases, and they have played a key role in preventing further severe diseases
Representative biosignals are divided into two types [2]: Electrical signals generated by the human body and can be measured with an electrocardiogram (ECG), which includes the electrical activity of the heart; an electroencephalogram (EEG), which is based on the brain; and an electromyogram (EMG), based on nerves and muscles
We present the structure of a convolutional neural network (CNN) and the hyperparameters used for the feature visualization of respiration states using signal processing techniques and image classification
Summary
Biosignals have played an important role in the diagnosis and treatment of diseases, and they have played a key role in preventing further severe diseases. Representative biosignals are divided into two types [2]: Electrical signals generated by the human body and can be measured with an electrocardiogram (ECG), which includes the electrical activity of the heart; an electroencephalogram (EEG), which is based on the brain; and an electromyogram (EMG), based on nerves and muscles. The respiratory status is an important factor that can represent the status of respiratory organs or related ones It plays an important role in carbon dioxide emission and energy generation. There are internal respiration processes in which oxygen transfer takes place in cells through interactions within the body and the production of carbon dioxide occurs, and there are external respiration processes in which oxygen is obtained and carbon dioxide is released through interactions between the environments inside and outside the human body. Apnea causes ventilatory disorders resulting from changes in the amount of ventilation in the alveoli and airways, and it is accompanied by chronic alveolar hypoventilation, hypertension, and cardiac arrhythmias
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