Project DetailsThe goal of this work is to develop a system that classifies ECG signals into two categories: arrhythmias (ARR) and normal sinus rhythm (NSR). Upon receiving an ECG signal from a subject, the system operates as follows:
The temporal signal is divided into windows, resulting in a time series of windows. A Wavelet Transform is applied to each window to obtain a time-frequency representation for the time segment within the window. Features are extracted from the windows using a convolutional network trained for this task, yielding a time series of features. Predictions are made on this time series using an LSTM network, providing a prediction of the subject's cardiac condition.
This architecture was developed independently and not based on any specific paper. The dataset used for this project is the well-known MIT BIH dataset. The system achieved the following performance on a test set composed of 1,000 randomly selected subjects, on which it was not trained: a recall of 96% and a precision of 98.6%.
