Abstract: The present invention relates to a method for estimating blood pressure from photoplethysmography (PPG) signals. The blood pressure estimation method using the CFR model according to an embodiment of the present invention is characterized in that it comprises the steps of extracting a plurality of blood flow characteristics from PPG signals for training, calculating systolic and diastolic blood pressures from ambulatory blood pressures for training, labeling the systolic and diastolic blood pressures with the plurality of blood flow characteristics to train a cascade forest regression model, and inputting the PPG signals of a target user into the trained cascade forest regression model to determine the systolic and diastolic blood pressures of the target user.

Abstract: The present disclosure relates to a method of reconstructing an arterial blood pressure (ABP) signal corresponding to the morphological feature of a combination signal of ECG and PPG on the basis of the morphological feature. The method of reconstructing an ABP signal according to an embodiment of the present disclosure includes: generating an ECG-PPG signal by combining ECG and PPG signals corresponding to an ABP signal; generating a plurality of ECG-PPG signals for learning by applying a plurality of noises, which is different in intensity, to the ECG-PPG signal; training a neural network model to receive the ECG-PPG signals for learning and output the ABP signal; and reconstructing an ABP signal of a target user by inputting an ECG-PPG signal of the target user into the neural network model.

Abstract: The present disclosure relates to a method of extracting spatiotemporal features of electrocardiogram (ECG) and photoplethysmography (PPG) signals corresponding to each other using a neural network and of estimating blood pressure on the basis of the spatiotemporal features. The method includes: generating a target signal of a plurality of channels by respectively combining ECG signals and PPG signals corresponding to each other; extracting a first feature composed of a plurality of channels by inputting the target signal of a plurality of channels into a 1D convolution layer; generating a channel-wise weight vector by compressing the first feature; computing a second feature composed of a plurality of channels by applying the channel-wise weight vector to the first feature; extracting a third feature by inputting the second feature into a CNN model; and determining systolic and diastolic blood pressures by inputting the third feature into an LSTM model.

Abstract: A mental stress classification apparatus using an ensemble model includes a processor and a storage medium on which one or more programs configured to be executable by the processor are recorded. The processor is configured to extract a feature vector of an electrocardiogram (ECG) signal and classify the extracted feature vector using the ensemble model, wherein the ensemble model is a mixture model of a support vector machine and a naive Bayes.

Abstract: The present invention relates to a method for estimating blood pressure from photoplethysmography (PPG) signals. The blood pressure estimation method using the CFR model according to an embodiment of the present invention is characterized in that it comprises the steps of extracting a plurality of blood flow characteristics from PPG signals for training, calculating systolic and diastolic blood pressures from ambulatory blood pressures for training, labeling the systolic and diastolic blood pressures with the plurality of blood flow characteristics to train a cascade forest regression model, and inputting the PPG signals of a target user into the trained cascade forest regression model to determine the systolic and diastolic blood pressures of the target user.

Abstract: An apparatus for transmitting power wirelessly using capacitive coupling, provided in a wearable device, includes: a transmission electrode including a plurality of unit electrode pairs, each unit electrode pair being formed by a transmission signal electrode and a transmission ground electrode; and a control module configured to select a unit electrode pair, forming capacitive coupling with the reception electrode, among the plurality of unit electrode pairs provided in the reception electrode, to wirelessly transmit power to a reception electrode included in an implantable device.

Abstract: An apparatus for classifying heart disease using a MobileNet according to an embodiment of the present invention may comprise: an input unit for receiving a time-domain electrocardiogram signal; a wavelet transform unit for transforming the timedomain electrocardiogram signal into a frequency-domain electrocardiogram signal by using a wavelet transform; and a neural network for classifying the frequency-domain electrocardiogram signal as one of atrial fibrillation (AFIB), left bundle branch block beat (LBBB), normal sinus rhythm (NSR), or premature ventricular contraction (PVC), wherein the neural network may be a MobileNet trained using a training data set.

Abstract: An apparatus for classifying stress includes an electrocardiogram (ECG) measurement module configured to measure an ECG signal in a first state, an idle state, and an ECG signal in a second state in which noise, having various magnitudes, is generated, a feature point extraction module configured to extract a feature point of each of the measured ECG signal in the first state and the measured ECG signal in the second state, and a clustering module configured to perform K-means clustering on the ECG signals in the first and second states based on the extracted feature points to classify stress.

Abstract: An apparatus for predicting blood pressure non-compressively includes a sequence folding layer configured to convert a sequence image of non-pressurized biosignals into an arrayed image; a CNN layer configured to generate a feature map by performing a convolution operation on an arrayed image; a sequence unfolding layer configured to convert the generated feature map into a sequence image; a flatten layer configured to convert the converted sequence image into one-dimensional data; a long-short-term memory network layer configured to extract feature values from the converted one-dimensional data using weights; a fully-connected layer configured to perform image classification using feature values extracted from the long-short memory network layer; and a regression layer configured to predict systolic blood pressure (SBP) and diastolic blood pressure (DBP) for the classified image.

Abstract: An apparatus for classifying mental stress includes a sequence folding layer configured to convert a sequence image of an electrocardiogram signal into an image in an array form; a CNN layer configured to generate a feature map by performing a convolution operation on the image in an array form; a sequence unfolding layer configured to convert the generated feature map into a sequence image; a flatten layer configured to convert the converted sequence image into one-dimensional data; and a long-short term memory network layer configured to extract feature values using a weighted value on the converted one-dimensional data; and a classification module configured to classify stress according to the extracted feature values.

Abstract: A capsule-type endoscope including a battery, a camera configured to capture internal organ images, a light source configured to irradiate light to capture the internal organ images, and a light source driving power line supplying power, stored in the battery, to the light source. The light source driving power line is a loop antenna coil for reception of a control signal from an external coupled loop antenna coil for transmission.

Abstract: Provided is an electromyogram (EMG) signal-based user authentication apparatus and method. The apparatus includes an EMG signal receiver configured to receive an EMG signal measured using an EMG sensor, a pre-processor configured to remove a partial signal from the received EMG signal according to a preset frequency band, and a controller configured to authenticate a user by comparing a pre-stored EMG signal with the EMG signal in which the partial signal has been removed.

Abstract: An apparatus for classifying stress includes an electrocardiogram (ECG) measurement module configured to measure an ECG signal in a first state, an idle state, and an ECG signal in a second state in which noise, having various magnitudes, is generated, a feature point extraction module configured to extract a feature point of each of the measured ECG signal in the first state and the measured ECG signal in the second state, and a clustering module configured to perform K-means clustering on the ECG signals in the first and second states based on the extracted feature points to classify stress.

Abstract: An apparatus for measurement of an electrocardiogram signal using wireless communications includes a submodule measuring a first signal by a heartbeat signal provided via a first electrode when a flag signal is wirelessly received, and a main module wirelessly transmitting the flag signal according to a predetermined sampling period, and measuring a second signal by a heartbeat signal provided through a second electrode. The main module is configured to synchronize a measurement time of the second signal with a measurement time of the first signal by measuring the second signal in consideration of a wireless transmission time of the flag signal.

Abstract: A Carbon NanoTube (CNT) composite electrode includes a lower electrode having a through-hole formed therein, and configured to be attached to the skin of a subject body to detect a biosignal, an upper electrode provided on one surface of the lower electrode to form an enclosed space between the lower electrode and the upper electrode, and configured to receive the biosignal detected by the lower electrode, and an air discharge portion formed in at least one of the lower electrode and the upper electrode, and discharging air present in the enclosed space and the through-hole externally so as to be configured to allow the upper electrode and the lower electrode to be attached to the skin of the subject body via vacuum suction.

Abstract: Provided is an electromyogram (EMG) signal-based user authentication apparatus and method. The apparatus includes an EMG signal receiver configured to receive an EMG signal measured using an EMG sensor, a pre-processor configured to remove a partial signal from the received EMG signal according to a preset frequency band, and a controller configured to authenticate a user by comparing a pre-stored EMG signal with the EMG signal in which the partial signal has been removed.

Abstract: An apparatus for monitoring a biosignal of a cyclist includes an electrocardiogram measuring sensor measuring an electrocardiogram signal of a cyclist, an electromyogram measuring sensor measuring an electromyogram signal of the cyclist, and a determination module determining muscle fatigue of the cyclist, based on at least one of a change in a heart rate of the measured electrocardiogram signal and a change in a frequency component of the measured electromyogram signal.

Abstract: An apparatus for measurement of an electrocardiogram signal using wireless communications includes a submodule measuring a first signal by a heartbeat signal provided via a first electrode when a flag signal is wirelessly received, and a main module wirelessly transmitting the flag signal according to a predetermined sampling period, and measuring a second signal by a heartbeat signal provided through a second electrode. The main module is configured to synchronize a measurement time of the second signal with a measurement time of the first signal by measuring the second signal in consideration of a wireless transmission time of the flag signal.

Abstract: A Carbon NanoTube (CNT) composite electrode includes a lower electrode having a through-hole formed therein, and configured to be attached to the skin of a subject body to detect a biosignal, an upper electrode provided on one surface of the lower electrode to form an enclosed space between the lower electrode and the upper electrode, and configured to receive the biosignal detected by the lower electrode, and an air discharge portion formed in at least one of the lower electrode and the upper electrode, and discharging air present in the enclosed space and the through-hole externally so as to be configured to allow the upper electrode and the lower electrode to be attached to the skin of the subject body via vacuum suction.

Abstract: An apparatus for monitoring a biosignal of a cyclist includes an electrocardiogram measuring sensor measuring an electrocardiogram signal of a cyclist, an electromyogram measuring sensor measuring an electromyogram signal of the cyclist, and a determination module determining muscle fatigue of the cyclist, based on at least one of a change in a heart rate of the measured electrocardiogram signal and a change in a frequency component of the measured electromyogram signal.