Building Signal Classification Model for ECG Signals
Hi, I am trying to develop a signal classification model for ECG signals, using the PTB-XL dataset (PTB-XL, a large publicly available electrocardiography dataset v1.0.3). The dataset has a column called scp_codes, which indicates whether the signal is NORM (normal) or abnormal with the specific illness class. Table below shows list of abnormal classes and their distributionThe numbers of individual classes.Number of RecordsClassDescription7185NORMNormal ECG3232CDMyocardial Infarction3064STTCST/T Change2936MIConduction Disturbance815HYPHypertrophyECG is stored in 10 sec clips of signal reading, which is in a wfdb waveform format with a sample rate of 100 Hz. Example plot of data which I read from PTB-XL data looks like as shown below. It is already labeled as NORM or class of abnormality as defined by scp_code attached to the data.598×734 213 KBI am trying to use the labeled signals in this dataset to classify future ECG signals. I have already built a CNN model, but I am hoping to use HuggingFace to increase my accuracy. So far, I am thinking of using the Audio Spectrogram Transformer for audio classification by converting the signal into a spectrogram. Do you think this method could work? Do you know what other models I could use for signal classification?