## 2 Literature Review
### ECG and PPG Arrhythmia Classification  
The University of California, Los Angeles [38], extracted and processed ECG heartbeats from the MIT-BIH and PTB (Pulmonary Tuberculosis) diagnostic databases to classify five different arrhythmias and predict myocardial infarctions using a deep learning convolutional neural network with residual connections. Their implementation focused on using statistical methods on individual heartbeats and utilizing a transferable approach for prediction tasks. The researchers in [12] created a 1D convolutional neural network using the MIT-BIH database, with an analytical approach based on the analysis of 10 s ECG signal fragments to classify different classes of arrhythmia. The preprocessing methods involve normalizing, standardizing, and rescaling raw data. The system requires ECG readings from a user, which are then uploaded to the cloud using a smartphone and then processed by the proposed 1D-CNN. In [14], a personalized health monitoring system was designed that can detect early occurrences of arrhythmia from a dedicated device's ECG signals using 1D convolutional neural networks and the MIT-BIH database. A systematic method of using ABS filters, synthesizing abnormal beats from a person's ECG beats, forming a training dataset for one person using their real and abnormal beats, and then using the neural network was followed. The solution was designed to be used in a client-server paradigm. Deep PPG [39] takes in a 30 s PPG recording and passes it through a 50-layer convolutional neural network to predict the presence of atrial fibrillation. Samsung wrist-wearable devices with a sampling frequency of 20 Hz were used, and a dataset of 510,556 PPG records was collected from 81 patients, along with a simultaneous collection of ECG patch references. The model achieved an AUC (Area Under the Curve) of 95% on the test set and was shown to be robust to motion artifacts. [PERSON] et al. [40] developed a convolutional deep neural network for ECG classification. They used 91,232 single-lead ECGs from 53,549 patients, recorded using an ambulatory ECG device. The network architecture contains 34 layers and consists of 16 residual blocks, with two convolutional layers per block. It receives raw ECG data as input and outputs one prediction per 256 samples or every 1.28 s. The model classifies 12 rhythm classes, and the results showed that, on average, it outperforms cardiologists by achieving similar specificity but higher sensitivity. In [41], [PERSON] et al. employed a combination of time-frequency analysis and deep learning for AF classification with PPG signals. The continuous wavelet transformation (CWT) is used to process PPG signals and convert PPG data into a time-frequency chromatograph, which is then used as the input to the model. The model architecture is a combination of a 2D-CNN and an LSTM network. It achieved accuracy, sensitivity, specificity, and F1 scores above 98%. In [42], a deep learning model called BayesBeat was proposed for AF detection using PPG signals. The dataset used includes more than 500k PPG signal segments from 175 individuals. The proposed model is based on Bayesian deep learning due to its robustness to noise, which it achieves by learning the distribution of network weights as opposed to fixed value weights. The network consists of nine Bayesian convolutional layers and two fully connected layers. The 1D Bayesian convolutional layer transforms an input into output distributions, and this model provides certainty estimates of the classification, making it useful for real-world applications. The results showed state-of-the-art performance and a relatively memory-efficient model. [PERSON] et al. [43] proposed one of the first methods involving the cross-domain generalizability of AF detection models. The model is a bidirectional recurrent neural network that is first pre-trained on single-lead ECG data, then fine-tuned on PPG data, andadapted for PPG classification using transfer learning.