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 distribution

The numbers of individual classes.

Number of Records Class Description
7185 NORM Normal ECG
3232 CD Myocardial Infarction
3064 STTC ST/T Change
2936 MI Conduction Disturbance
815 HYP Hypertrophy

ECG 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.

I 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?