Method for classifying type of heartbeat and apparatus using the same

An apparatus for detecting abnormal heart rhythm from classified types of heartbeats, comprising:

• a single lead configured to produce an electrocardiogram waveform, wherein the electrocardiogram waveform is used without any data preprocessing;
• a storage unit configured to store a dataset including multiple heartbeat waveform data;
• a learning model generation unit configured to generate, from the dataset, input data regarding a heartbeat waveform for training and generate a learning model to which the generated input data is input and from which a heartbeat type of the heartbeat waveform for training is output;
• a training unit configured to train the learning model by determining a loss weight of each batch sampled from the dataset and determining a loss function based on the loss weight of each batch;
• and
• a classification unit configured to input a heartbeat waveform for test to the learning model and classify a heartbeat type of the heartbeat waveform for test,
• wherein the loss weight of each batch is calculated based on the following equation:
• c
• ⁢
• w
• i
• ,
• class
• k
• =
• 1
• -
• Σ
• j
• =
• 1
• M
• ⁢
• 1
• y
• i
• ,
• j
• =
• class
• k
• M
• +
• ɛ
• wherein M is a batch size, E is a predetermined constant, cwi,class k is a loss weight of a kth class in an ith batch,
• wherein the classification unit is also configured to use the electrocardiogram waveform produced by the single lead to detect an abnormal heart rhythm based on the heartbeat type output from the trained learning model.