# Detection of Sleep Apnea from Electrocardiogram and Pulse Oximetry Signals Using Random Forest
## 2 Related Works  
Over past studies, various physiological signals (e.g., ECG, EEG, SpO2, snoring or airflow) have been used to develop SA detection algorithms [11], the most widely used of which are ECG signal and SpO2 signal.  
For ECG signal-based methods, the shallow characteristic signals of the ECG are usually analyzed in the time domain, frequency domain or nonlinear domain. The time intervals between successive heartbeats are sequentially combined to form the RR interval signal [12]. HRV analysis refers to the analysis of changes in the RR interval signal. [PERSON] et al. [9] proposed a method for detecting sleep apnea based on HRV analysis. Their method was successfully applied to clinical PSG data and the performance was comparable to portable monitoring devices in sleep laboratories. ECG-derived respiratory (EDR) signals reflecting respiratory activity can be used as complementary information to HRV [13]. [PERSON] et al. [14] analyzed the EDR signal and RR interval with wavelet transform and used SVM classifier to identify OSA patients. In their work, more than 90% of subjects in the test set were correctly classified. Further, [PERSON] et al. [15] extracted a complete feature set containing 111 features from RR and EDR time series using time-frequency analysis methods. [PERSON] et al. [16] developed a SA detection model using Hermite basis functions. [PERSON] mainly considered the morphological changes occurring in the QRS wave complex of the ECG.  
The occurrence of apnea is usually accompanied by a decrease in oxygen saturation, hence the SpO2 signal has been used in several studies. Some of these studies employed statistical methods to quantify the variation in oxygen saturation over time. For example, [PERSON] et al. [17] used time spent below a certain level saturation (TSA), the saturation variability index and other indicators to evaluate AHI, and compared the diagnostic performance of SA under different metrics. The oxygen desaturation index (ODI) is defined as the number of oxyhemoglobin desaturation below a certain threshold [18]. [PERSON] et al. [19] found that the use of ODI improved the accuracy of moderate and severe OSA detection. However, the ODI index is more suitable for prolonged SpO2 signals. In addition, some studies have explored nonlinear parameters. [PERSON] et al. [20] used central tendency measure (CTM) and Lempel-Ziv (LZ) complexity to identify OSA and showed that the sensitivity obtained using CTM and LZ complexity, respectively, was 90.1% and 86.5%. [PERSON] et al. [21] performed a time series analysis of the SpO2 signal by approximate entropy and obtained a sensitivity of 82.09% and a specificity of 86.96% on training set.  
To conclude our brief review of SA detection algorithms, we have found that screening for SA using either ECG or SpO2 signals is effective, but the majority of the previous studies focused only on a single data modality. However, several machine learning tasks in other fields (e.g., medical image analysis, sentiment recognition, etc.) have demonstrated that fusing information from multiple data modalities can enhance the robustness of a model [22]. Therefore, our proposed multimodal approach for the detection of SA is more advanced.