Transformer Electrocardiogram Biometrics Dataset

Many of the publicly available electrocardiogram (ECG) databases either have a low number of people in the database, each with longer recordings, or have more people, each with shorter recordings. As a result, attempting to split a single database into training, testing, and, optionally, validation datasets is challenging. Some models seem to do well with larger training sets, but that leaves only a small set of data for testing. Moreover, if the ECG is segmented by heartbeat, the data are further limited by the number of heartbeats in the recording. Combining multiple databases to increase the dataset is difficult because it needs to reconcile the differences across databases, potentially having to deal with different measuring devices, measuring conditions, sampling rate, type of noise, etc. A dataset generation procedure using blind segmentation as a data augmentation technique is used to generate huge amount of training and validation dataset. This procedure is not limited by the number of heartbeats in the ECG recording. Multiple ECG databases are combined to increase the total number of subjects and to provide more ECG variations. A total of 10 databases were used to generate the training and validation datasets. The huge amount of data with wide variations trained a generalized model.

众多公开可用的心电图（ECG）数据库要么人数较少，每位受试者的记录较长，要么人数较多，每位受试者的记录较短。因此，试图将单个数据库分割为训练集、测试集，以及可选的验证集，面临着重大的挑战。一些模型在较大的训练集上表现良好，但这却导致可用于测试的数据量极少。此外，若心电图数据按心跳进行分割，数据将受到记录中心跳数量的进一步限制。通过将盲分割作为数据增强技术的一种方法，实施了一种数据集生成程序，用以生成大量的训练集和验证集。此程序不受心电图记录中心跳数量的限制。通过结合多个心电图数据库，增加了受试者的总数，并提供了更多的心电图变体。总共使用了10个数据库来生成训练集和验证集。大量具有广泛变异性的数据训练出了一个通用的模型。