Abstract: The present disclosure relates to a technique for estimating the inter-beat interval (IBI) of a ballistocardiograph (BCG) signal measured using a non-contact sensor, filtering and correcting inaccurate information in the inter-beat interval estimated through clustering, and outputting a heart rate measurement result based on the filtered and corrected inter-beat interval.

Abstract: Disclosed are a CBT-I treatment system for the treatment of insomnia and an operation method thereof. In accordance with an aspect of the present disclosure, a CBT-I treatment server including a database configured to store a plurality of CBT-I programs for insomnia treatment; a sleep evaluator configured to generate sleep evaluation data of an insomnia patient based on biosignal data including a change in a biosignal during sleep of the insomnia patient; a CBT-I prescriber configured to determine a CBT-I program for insomnia treatment of the insomnia patient from among the plural CBT-I programs based on the sleep evaluation data and to generate a CBT-I prescription including the determined CBT-I program; and a communicator configured to receive a biosignal from a biosignal collection device for generating biosignal data by sensing a change in a biosignal during sleep of the insomnia patient and to transmit the CBT-I prescription to an insomnia patient's terminal is provided.

Abstract: Provided is a data processing apparatus including a signal data processor configured to collect signal data detected through polysomnography, to extract feature data by analyzing a feature of the collected signal data, and to transform the extracted feature data to time series data; and a sleep stage classification model processor configured to input the processed signal data to a pre-generated sleep stage classification model, and to classify a sleep stage corresponding to the signal data. The signal data processor is configured to extract feature data by analyzing a feature of each of an electroencephalographic (EEG) signal, an electro-oculographic (EOG) signal, and an electromyographic (EMG) signal with respect to the signal data, and to transform the extracted feature data to an epoch unit of time series data to input the extracted feature data to the pre-generated sleep stage classification model.

Abstract: Provided is an automatic determination apparatus using a deep learning, the automatic determination apparatus including a signal data processor configured to collect signal data detected through polysomnography, to extract feature data by analyzing a feature of the collected signal data, and to transform the extracted feature data to time series data; and a sleep stage classification model processor configured to input the processed signal data to a pre-generated sleep stage classification model, and to classify a sleep stage corresponding to the signal data. The sleep stage classification model processor is configured to classify the sleep stage based on at least one of an American Academy of Sleep Medicine (AASM) standard and a Rechtschaffen and Kales (R&K) standard using an epoch unit of time series data as the processed signal data.

Abstract: Provided is a data processing apparatus including a signal data processor configured to collect signal data detected through polysomnography, to extract feature data by analyzing a feature of the collected signal data, and to transform the extracted feature data to time series data; and a sleep stage classification model processor configured to input the processed signal data to a pre-generated sleep stage classification model, and to classify a sleep stage corresponding to the signal data. The signal data processor is configured to extract feature data by analyzing a feature of each of an electroencephalographic (EEG) signal, an electro-oculographic (EOG) signal, and an electromyographic (EMG) signal with respect to the signal data, and to transform the extracted feature data to an epoch unit of time series data to input the extracted feature data to the pre-generated sleep stage classification model.