Abstract: A sleep state prediction system includes a phase coherence acquisition device that acquires phase coherence calculated based on a difference in an instantaneous phase between a variation in a heartbeat interval and a breathing pattern; at least one of a body movement information acquisition device that acquires body movement information, a heartbeat information acquisition device that acquires heartbeat information from the animal simultaneously with the variation, and a respiratory information acquisition device that acquires respiratory information; and a sleep state prediction device that includes a sleep state prediction model, which is established through machine learning with sleep stages assessed by polysomnography on the animal as teaching data and at least 2 sets of data out 4 parameters of phase coherence, body movement, heartbeat, and respiration respectively obtained from bio-vibration signals acquired simultaneously during the polysomnography as input data, mandatorily including the phase coheren

Abstract: An apparatus yields signals that are equivalent to ECG signals and allow determination of a heartbeat interval or heart rate from bio-vibration signals including vibrations derived from heartbeats. An ECG meter acquires ECG signals of a sample, and a piezoelectric sensor acquires bio-vibration signals of the sample simultaneously. The bio-vibration signals include beating vibration signals derived from heartbeats. A learning unit of a prediction modeling apparatus establishes a prediction model by machine learning in which ECG signals are used as teaching data, and model input signals obtained by performing a specified processing on the bio-vibration signals are input. The learning unit delivers the prediction model to a prediction unit of a signal processing apparatus. The prediction model predicts and outputs pECG signals upon input of model input signals obtained by performing a specified processing on bio-vibration signals acquired from a subject under prediction with a piezoelectric sensor.

Abstract: [Subject] Non-invasive method for estimating blood pressure without a cuff and a device for the blood pressure estimation [Resolution means] Systolic blood pressure (EBP) is estimated according to EBP=?1·P1+?2·P2+?0 (?1, ?2, and ?0 are coefficients) where parameter P1, which is related to pulse transit time (PTT), and parameter P2, which is related to stroke volume based on pulse waves, are variables.

Abstract: An apparatus yields signals that are equivalent to ECG signals and allow determination of a heartbeat interval or heart rate from bio-vibration signals including vibrations derived from heartbeats. An ECG meter acquires ECG signals of a sample, and a piezoelectric sensor acquires bio-vibration signals of the sample simultaneously. The bio-vibration signals include beating vibration signals derived from heartbeats. A learning unit of a prediction modeling apparatus establishes a prediction model by machine learning in which ECG signals are used as teaching data, and model input signals obtained by performing a specified processing on the bio-vibration signals are input. The learning unit delivers the prediction model to a prediction unit of a signal processing apparatus. The prediction model predicts and outputs pECG signals upon input of model input signals obtained by performing a specified processing on bio-vibration signals acquired from a subject under prediction with a piezoelectric sensor.

Abstract: [Problem] To provide means by which sleep states can be measured, observed, or evaluated easily. [Solution] A sleep state measurement device that includes a phase coherence calculating means for calculating phase coherence on the basis of the instantaneous phase difference between the instantaneous phase of heart rate variability acquired from a sleeping animal and the instantaneous phase of the breathing pattern of the animal for the same time series as the heart rate variability.

Abstract: [Subject] Non-invasive method for estimating blood pressure without a cuff and a device for the blood pressure estimation [Resolution means] Systolic blood pressure (EBP) is estimated according to EBP=?1·P1+?2·P2+?0 (?1, ?2, and ?0 are coefficients) where parameter P1, which is related to pulse transit time (PTT), and parameter P2, which is related to stroke volume based on pulse waves, are variables.

Abstract: [Problem] To provide means by which sleep states can be measured, observed, or evaluated easily. [Solution] A sleep state measurement device that includes a phase coherence calculating means for calculating phase coherence on the basis of the instantaneous phase difference between the instantaneous phase of heart rate variability acquired from a sleeping animal and the instantaneous phase of the breathing pattern of the animal for the same time series as the heart rate variability.