Electrocardiogram signal classification for automated delineation using bidirectional long short-term memory

Analysis of electrocardiogram (ECG) signals is challenging due to the complexity of their signal morphology. Any irregularity in a cardiac rhythm can change the ECG waveform. A reliable machine learning model is developed here to provide substantial input to cardiologists and help confirm their diagnoses. To achieve high diagnostic accuracy, nearly all ECG analytics tools require records of the positions and morphologies of various segments of...