{'fraction_non_alphanumeric': 0.030428769017980636, 'fraction_numerical': 0.004840940525587829, 'mean_word_length': 5.210300429184549, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Improved Model Accuracy Based on a Simple Frame of Temporal-Correlation Representation Method in Sleep Staging

We proposes a new kind of contrast learning method based on Temporal-Correlation characters of electroencephalogram (EEG) used in sleep staging. This method, called simple framework of temporal-correlation representation(simTCR), was studied and tested on Open EEG dataset, sleep-EDF and clinical EEG dataset from polysomnography (PSG). Deep learning used in clinical diagnosis is often faced with problems of deficiency of well labeled labels. And physiological signal, like EEG signal, often shows temporal correlation character, which means the signal maintains one state in a period of time before it transforms into a new state. Therefore, there exist high probability that the nearby frames of EEG signal belong to same categories and shows similar characters. This character can be considered as self-supervised features and a loss function was raised here to pretrain the model. Our result shows that, (1) simTCR can improve the sleep staging accuracy based on EEG signal to 1%-17% in both clinical dataset and Open dataset, sleep-EDF; (2) The improved effect is more obvious when the training data in fine-tuning process is more less; (3) the hyper-parameter batch size plays limited roles in the accuracy improvement; (4) simTCR costs about three magnitudes of more time than the traditional supervised deep learning method.