Method for building a heart rhythm classification model

A method for building a heart rhythm classification model that is used to classify a heart rhythm, the method implemented by a computer device and comprising steps of:

• A) providing a neural network model that includes first to Mth bidirectional long short-term memory (LSTM) layers, M being a positive integer greater than one,
• wherein each of the first to Mth bidirectional LSTM layers includes a first set of LSTM neurons for forward data input and a second set of LSTM neurons for reverse data input, and
• wherein, for an mth bidirectional LSTM layer where m is an arbitrary one of integers from two to M, each of the LSTM neurons is connected to all of the LSTM neurons of an (m−1)th bidirectional LSTM layer for receiving outputs thereof;
• B) receiving a plurality of 12-lead electrocardiogram (ECG) datasets, wherein each of the 12-lead ECG datasets is acquired by performing 12-lead electrocardiography on a respective person, corresponds to one of a plurality of predetermined class labels that respectively correspond to a plurality of predetermined heart conditions, and includes first to Nth data points that are ordered according to a time sequence the first to Nth data points are obtained, wherein N is a positive integer greater than one;
• C) for each of the 12-lead ECG datasets, using the neural network model to generate a classification result that indicates, for each of the predetermined heart conditions, whether the 12-lead ECG dataset corresponds to the predetermined heart condition, wherein step C) includes:
• feeding the 12-lead ECG dataset into the first set of LSTM neurons of the first bidirectional LSTM layer in a forward sequence from the first data point to the Nth data point of the 12-lead ECG dataset, and feeding the 12-lead ECG dataset into the second set of LSTM neurons of the first bidirectional LSTM layer in a reverse sequence from the Nth data point to the first data point of the 12-lead ECG dataset;
• D) acquiring classification accuracies respectively for the predetermined heart conditions based on the classification results respectively obtained for the 12-lead ECG datasets and the predetermined class labels;
• and
• E) when any one of the classification accuracies acquired in step D) is lower than a respective predetermined first threshold, adjusting parameters of the neural network model, and repeating steps C) and D) using the neural network model thus adjusted;
• and
• F) making the neural network model serve as the heart rhythm classification model when each of the classification accuracies acquired in step D) is equal to or higher than the respective predetermined first threshold.