# Classification of Respiratory States Using Spectrogram with Convolutional Neural Network
## 1 Introduction  
Biosignals have played an important role in the diagnosis and treatment of diseases, and they have also played a key role in preventing further severe diseases. In practical fields, wearable devices have recently been developed to diagnose cardiac fibrillation or other diseases using biosignals acquired from the human body using specific types of sensors [1]. In general, biosignals are categorized into four groups: heart rate, blood pressure, body temperature, and breathing rate. From among the four aforementioned signals, respiratory signal has gained less attention than the others. 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. There are also non-electrical signals in the body, which include blood flow (hematocele), temperature, and respiration rate. Among these, the respiratory status is an important factor that can represent the status of respiratory organs or related ones. It also plays an important role in carbon dioxide emission and energy generation. Respiration is divided into two phases: internal and external respiration. Internal respiration is an interaction inside the body, and external respiration occurs between the body and the external environment. 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 isaccompanied by chronic alveolar hypoventilation, hypertension, and cardiac arrhythmias. It also increases the long-term mortality rate of patients. In fact, patients with apnea have twice the risk of developing hypertension compared to those with normal respiration activities [3]. There are two main approaches to biosignal acquisition: a contact-based method and a contactless (or non-contact)-based method. Contact-based methods usually employ a device that is attached to the human body, located near a respiratory organ. The contact-based method acquires respiratory signals using a device that measures human respiration with high quality (because the noise is removed or reduced with signal processing techniques) compared to the non-contact based method. The latest technology uses smart steering wheel, smart seat belt, and equipment that acquire electrical signals such as EEG analysis [4]. Although the contact-based methods acquire biosignals with low noise, it is sometimes not suitable for patients such as children, the disabled, or severely damaged persons because it can be difficult to attach a device for respiration acquisition and to monitor respiratory states in a continuous and real-time manner. Contrary to the contact-based methods, non-contact-based methods can be an alternative to the contact-based ones and have advantages in monitoring respiratory states in a continuous and real-time manner in addition to its simplicity and ease of use in the acquisition of measurements. Research on human behavior and health status through biosignals and the use of non-contact-based methods employing the technologies of computer vision, image processing, and signal processing is being extensively performed [5; 6]. The non-contact-based method is easy and simple to use, and measuring devices have become accurate and low-cost compared to those used for the contact-based method. However, the non-contact-based method is still considered less accurate and less stable in practical fields.