## 1 Introduction
The model introduces a compressed version with fewer parameters, but the relationship between the reconstructed and the actual ECG beat on the BIDMC dataset [18] is about 0.89. In [17], the authors propose a deep learning model for beat-based autoencoders. These studies focus on performing ECG reconstruction on a beat-by-beat basis, conduct period segmentation of the signal during data preprocessing, and utilize a peak-to-peak segmentation technique to minimize errors in PPG onset monitoring. However, the varying lengths after segmentation necessitate resampling of the beat data, which could lead to mistakes. Several studies have proposed P2E-WGAN [19], CardioGAN [20], UNet-BiLSTM [21], bidirectional long short-term memory (BiLSTM) [22], and PPG2 ECGps [23] models to reconstruct fixed-length ECG signals. In these studies, the signal is segmented at a fixed length during data preprocessing to prevent errors caused by beat segmentation. Both P2E-WGAN [19] and CardioGAN [20] models are based on generative adversarial network (GAN) structures. The former is a conditional generative adversarial network model that operates directly on one-dimensional time series data to reconstruct ECG signals from PPGs. However, the MIMIC II dataset's [24] relationship between the reconstructed and the actual ECG is only 0.835. The latter utilizes an attention-based generator, bi-time domain and frequency discriminators, and CycleGAN [25] to obtain ECG signals and develop a representation that connects medical knowledge to the PPG-to-ECG reconstruction task. The model uses an attention-based network, which increases the model parameters' mathematics and is unsuitable for wearable devices. The UNet-BiLSTM model [21] performs segmentation after aligning the signal in the preprocessing process. However, signal alignment requires peak detection of the signal, and the accuracy of peak detection depends on the accuracy of the peak detection algorithm. Some errors may occur in this process. The BiLSTM model [22] and PPG2 ECGps [23] model were proposed for specific subjects. In [17], the authors categorize the training process as subject-dependent, partially subject-independent, and completely subject-independent based on the level of subject dependency. The subject-dependent is a subject-specific model, i.e., a model designed for a single person. The partially subject-independent and completely subject-independent are group models, with the difference being the division of the dataset. Partially subject-independent refers to using a portion of all subject records for training and another for testing. Movement on one set of subjects and testing on a completely different set of subjects highly depend on the subject matter. The UNet-BiLSTM model [21] was to reconstruct ECG signals in the partially subject-independent model. It only studies the performance of ECG reconstruction from PPG when the test set and training set are from the same subjects. However, it does not study the performance of ECG reconstruction from PPG when the test set and training set are from different subjects. This study proposes a neural network model called CNN-BiLSTM, based on a 1D convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM), for reconstructing ECGs from PPGs. This neural network is designed for a subject-independent rather than a subject-specific model. This study validates the model's effectiveness of the proposed model in a completely subject-dependent model. This study aims to verify the proposed model's performance without including subject information, hoping to reconstruct better ECG signals even when facing unknown subjects.