Hi Aisha,
The main difference between using LSTM (Long Short-Term Memory) only and using CNN_LSTM (Convolutional Neural Network with LSTM) for classifying ECG signals lies in the architecture and the way information is processed.
When using LSTM alone, the input ECG signal is fed into the LSTM network, which processes the sequential data over time steps. LSTM is effective in capturing long-term dependencies and modeling temporal relationships in the ECG signal. It can learn patterns and features from the entire sequence of the signal, making it suitable for tasks like heartbeat classification or arrhythmia detection.
In the case of ECG signals, CNN layers can be used to extract local features, such as morphological characteristics of individual heartbeats or specific patterns within the signal. The output of the CNN layers is then fed into the LSTM layers, which capture the temporal dependencies and long-term patterns in the extracted features. By combining CNNs and LSTMs, the model can effectively capture both local and temporal features, enhancing the classification performance for ECG signals.
In summary, using LSTM only focuses on modeling the temporal dependencies in the ECG signal, while using CNN_LSTM combines the spatial analysis capabilities of CNNs with the temporal modeling capabilities of LSTMs. This combination allows for a more comprehensive analysis of ECG signals, extracting both local and global features, and potentially improving the accuracy of ECG signal classification tasks. However, The choice between LSTM only and CNN_LSTM depends on the specific requirements of the ECG classification problem and the characteristics of the dataset.
