We investigated a system by which an electrocardiogram can be obtained without the individual's awareness. The practice of this concept involves placing the electrodes in a bed set composed of electrically conductive textiles. Electrocardiograms were successfully obtained during sleep. However, during the periods of subject's movement in bed, the waveform became unrecognizable.
An automated system is developed to monitor cardiopulmonary functions during sleep using electrically conductive textiles. The system obviates the need to attach transducers or electrodes to the body surface, and the subject can follow his or her normal daily routine, wearing regular pajamas to bed. Part of the bed sheet consists of electrically conductive textiles under the positions of the head, torso and legs. Respiratory activity and electrocardiograms of diagnostic quality are observed by means of the electrodes while the subject is sleeping. Respiration is sensed by means of electrical capacitance in/around the thorax. Data acquisition and storage are fully automated; thus, the subject's awareness of being monitored is greatly reduced. This system could detect disorders of cardiopulmonary functions at an early stage, if used daily in the home with the concept of chronodiagnosis.
In this research, we proposed that high frequency component of HFECG was applicable biometric feature for new identification system. We developed identification method by using neural network (NN), and aimed at the improvement of the classification rate. Preprocessing prior to NN is performed by justification on time axis and normalization on amplitude. As a result, an average of 99% classification rate was obtained from 9 subjects. We also made an attempt to identify in shorter time by shifting of the HFECG by a few samples to NN.
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