Massive amounts of electronic health records, including data on vital signs and electrocardiograms (ECGs), are now accessible due to the big data revolution. These signals are now more easily obtained and are frequently captured as a time series of observations. There is a particular need to provide innovative methods that enable efficient monitoring of these signals and prompt anomaly detection given the proliferation of smart devices with ECG capabilities. However, anomaly identification is still a very difficult task because the majority of created data is not yet categorized. Deep generative models have been used for unsupervised representation learning to develop expressive feature representations of sequences, which can improve the accuracy and ease of use of downstream tasks like anomaly detection. We suggest utilizing an autoencoder to learn representations of ECG sequences in an unsupervised manner. Then, we apply several detection algorithms to identify anomalies based on the learnt representations. We evaluated our method using the UCR time series classification archive's ECG5000 electrocardiogram dataset. Our findings demonstrate that the suggested strategy outperforms previous supervised and unsupervised techniques in identifying anomalies and learning expressive representations of ECG sequences.