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:
Disclosed are a method for generating personal identification information using an electrocardiogram and a method for identifying a person using the personal identification information. The methods dramatically increase an identification rate by using two-dimensional image data converted from an electrocardiogram signal as personal identification information, and enable real-time identification by reducing a calculation amount by converting only a single electrocardiogram cycle into the two-dimensional image data.
Abstract:
There is provided a microneedle bio-sampling apparatus including: an bio-sampling body enabling a microneedle to be inserted into human skin; and a blocking unit installed in the bio-sampling body and preventing the microneedle from moving to the skin again to prevent repeated bio-sampling by the microneedle. Since the microneedle bio-sampling apparatus is configured to prevent repeated bio-sampling of a single-use microneedle, secondary infection to other part of a human body or a different person due to blood, human anatomy, or the like, that may remain in a microneedle after the microneedle is used once otherwise in case of repeated bio-sampling of a microneedle, can be prevented.
Abstract:
Disclosed are a method for generating an electrocardiogram for personal identification and a method for identifying a person using the electrocardiogram. The electrocardiogram generation method generates a normalized electrocardiogram by extracting single-cycle electrocardiogram signals meaningful for personal identification from an electrocardiogram of a person and by connecting the extracted single-cycle electrocardiogram signals arranged in temporal order. Therefore, the electrocardiogram generation method dramatically increases identification accuracy in personal identification.
Abstract:
There are provided a diagnostic module for diagnosing a disease and a disease diagnosis apparatus including the same. The disease diagnosis apparatus includes a patch including one or more diagnostic module attachable-detachable recesses, one or more diagnostic modules detachably attached to the diagnostic module attachable-detachable recesses to collect and analyze blood, and a processor processing analysis results.