Atrial Fibrillation (AF) is the most common arrhythmia type that affects patients today. Detecting and classifying a patient's electrocardiogram (ECG) beats, especially the supraventricular ectopic beats (SVEB) class, can help assess if the patient has high possibilities of AF/atrial flutter in the future. Detecting the SVEB class considered more difficult than the other classes. Related works show low classification (prediction) performance, in terms of sensitivity, F1 score, and G score, for detecting the SVEB class in a single-lead ECG. This work focuses on designing an arrhythmia beats detection method using single-lead ECG data with a patient-specific training model design, and does classification based on the AAMI standards. This work aims at achieving high classification performance in the SVEB class and still meets the real-time ECG classification requirement. The proposed method uses Empirical Mode Decomposition (EMD) with resampling (EMDR), which resamples only the first Intrinsic Mode Function (i.e., IMF 1) as a main input, for the proposed EMDR-LSTM (Long Short-Term Memory) architecture. In contrast to the related works that use two separate models with one or two LSTM layers for each input, we designed a novel LSTM model architecture that only uses a single model with one LSTM layer for each input. The proposed LSTM architecture is suited for our preprocessing method, EMDR, and can enhance the SVEB classification performance. To the best of our knowledge, the proposed EMDR-LSTM is the first one that uses resamples first IMF in LSTM that classifies arrhythmia using single-lead ECG data based on the AAMI standards. Compared to representative related works, experiment results show that the proposed EMDR-LSTM achieves the highest classification performance, in terms of the following performance metrics: accuracy, sensitivity, positive predictivity, and F1 and G scores, for the SVEB class in all datasets used. In addition, although the proposed EMDR-LSTM has higher preprocessing cost and higher computational complexity in terms of MACs (multiply-accumulate operations), it has lower standard deviations of the performance metrics and lower inference time, which are important performance metrics for real time or time-critical applications, e.g., ECG medical monitoring applications, compared to the representative related works.