# Classification of Respiratory States Using Spectrogram with Convolutional Neural Network
## 2 Related Research
Recently, Bi-LSTM showed an accuracy of 82% using the data set provided by NIH [26]. This has also been shown from related studies using various classifiers such as SVM, LDA, TVAM, and ELM [30; 31; 32; 33; 34]. In the time domain, every waveform is actually a sum of sinusoids. Existing studies analyze the singularity by decomposing the global time for the feature into the frequency domain. When the length of the signal feature is long, a singularity that occurs with a low frequency in a local area may not appear in the results of analyzing the singularity for the entire signal. In mechanical engineering and acoustics, data characteristics are divided into local units and analyzed, and features appearing in the region are predicted and diagnosed. A spectrogram decomposes singularities by dividing the time domain into regions. The \(x\)-axis is the time domain and the \(y\)-axis is the frequency domain that visualize the characteristics of the data. In mechanical engineering, it has been used for predicting and diagnosing engine failures and failures of vehicle parts [35]. In the Mel spectrogram, the \(x\)-axis is the  
Figure 1: Flowchart of existing research in apnea detection. frequency domain and the \(y\)-axis is the amplitude domain, and it is used in acoustics and phonetics such as in speech and sound recognition, detection, and classification. Human emotions were predicted and diagnosed through voice [36]. In the past, MFCC was used to analyze voice and acoustic signals. However, Mel spectrogram is used to achieve an improved computational processing power compared to the conventional spectrogram of recent computer systems. In this paper, the spectrogram is used to process the signal for the rate of change of the human thorax. The flowchart for visualization and analysis of input data is shown in Figure 2. The input data are characterized as a two-dimensional value on the \(x\)-axis and \(y\)-axis using data visualization tools such as the spectrogram and the Mel spectrogram. Alternative to the methods explained above, contactless-based methods have been of interest for the measurement of respiratory states since contact-based methods have limitations in that long-term and continuous monitoring is sometimes difficult because devices that measure respiration need to be attached to respiratory organs. Severely injured persons, children, or other people suffer from or have difficulties in the attachment of a sensing device to their body. Unless patients are not comfortable with attaching a sensing device to the body, an accurate measurement of respiration may be difficult. Recently, the Coronavirus disease 19 (COVID 19) has inspired a necessity for the measurement of respiratory states, and the early detection and isolation of potential patients has been one of the most important factors that could reduce the severity of the disease. In addition, due to the limited number of hospital wards, contactless-based methods of respiratory measurement have gained a lot of attention in the areas of medicine and medical engineering. In contactless methods, ultra-wideband (UWB) radar is widely used because of its  
Figure 2: Existing research on the visualization method. simplicity, and it enables us to monitor respiratory states in a long-term and continuous manner. However, another recent work has shown a deep learning model using an IR-UWB radar to propose a real-time apnea-hypopnea (AH) event detection [37]. The UWB radar shows robustness against factors in the external environment such as the existence of dust, temperature changes, etc. When using UWB radar sensors for respiration measurement, patients (or persons who need measurements to be taken) do not have inconveniences in attaching the device. The UWB radar sensor provides a high signal-to-noise ratio and accuracy of measurement [38]. If the reflected signal is not pre-processed, various types of filters such as the Kalman filter, bandpass filter, median filter, etc., are employed to enhance the quality of the signals.