# Classification of Respiratory States Using Spectrogram with Convolutional Neural Network
## 1 Introduction
Eye tracking using a Charge Coupled Device (CCD) video camera, image processing to determine drowsiness using face direction and eyelid movement, and visualization and analysis of audio signal characteristics using MFCC (Mel-Frequency Cepstral Coefficient) based on signal processing techniques have been proposed [7]. Feature visualization is used for the classification of speech signals and vibration analysis. Existing apnea diagnosis studies analyze characteristics by decomposing data with continuous values into frequency components in the time domain [8]. On the other hand, the feature visualization method visualizes regional features in the time domain by periodically dividing the data in it. Compared with the time and frequency domains, which express the features in one dimension, it is possible to analyze the local features in two dimensions [9; 10]. Radar acquires data with relatively low noise in environments of low-light conditions, in rainy and cloudy circumstances, or other conditions which usual image sensors suffer from. Radar applies to diverse fields such as defense, medicine, vehicle, surveillance, etc., and it achieves a high precision in distance measurement [11]. Even though respiratory status is important, it has gained less attention compared to other biosignals. Research on the diagnosis of apnea using ECG-based electrical signals acquired by PSG (Polysomnography) that can provide various information about human biosignals has long been of interest in the research and industrial fields. As deep learning algorithms have shown significant improvements in detection, recognition, and classification in the areas of signal processing, image processing, etc., recent studies on the detection of the apnea status of humans based on a neural network model--e.g., long short-term memory and deep neural network--have been performed [12; 13]. Existing work has focused on the classification of respiratory signals in the frequency domain, and recently, a learning-based classification using an artificial neural network has been proposed [14]. In this paper, we present a learning-based respiration state classification algorithm using a feature visualization method that visualizes and classifies frequency changes in respiratory data. This paper classifies the respiration states using a feature visualization method and an artificial neural network model. Respiratory signal is acquired by UWB radar sensor, and the signal is transformed into the frequency domain. In the Fourier domain, the signal that is defined in one-dimensional (1D) space is transformed into 2D space using a spectogram. Analyzing biosignals in the image domain has been introduced in a recent work. In the work by [PERSON] et al. [15], the EEG signal is represented in the time-frequency domain using a cross wavelet transform followed by the convolutional neural network (CNN)-based feature extraction and classification of EEG signals. EEG is a popularly used tool that provides assistance in the detection of epileptic seizures by analyzing EEG signals [16]. In the work by [PERSON] et al. [17], a new deep learning approach has been proposed to predict ventricular tachyarrhythmia (VTA) using ECG signals that are converted into binary images. Deviating from the existing research method using electrical biosignals, non-electrical biosignals are acquired using an Ultra Wide Band (UWB) radar sensor. The aim of this study is to propose a learning-based respiration status classification algorithm using a feature visualization method that classifies respiration conditions, particularly detecting abnormal respiration such as in apnea. The rest of the paper is organized as follows. Section 2 describes the necessity of this study and related work on the detection of abnormal respiration. In addition, the difference between the biosignals obtained via the contact method and the non-contact method is explained. This section presents the accuracy according to the data set and classifier used in the previous study.