A system (1) for estimating the brain blood volume and/or brain blood flow and/or depth of anesthesia of a patient, comprises at least one excitation electrode (110E) to be placed on the head (20) of a patient (2) for applying an excitation signal, at least one sensing electrode (110S) to be placed on the head (20) of the patient (2) for sensing a measurement signal caused by the excitation signal, and a processor device (12) for processing said measurement signal (VC) sensed by the at least one sensing electrode (110S) for determining an output indicative of the brain blood volume and/or the brain blood flow. Herein, the processor device (12) is constituted to reduce noise in the measurement signal (VC) by applying a non-linear noise-reduction algorithm. In this way a system for estimating the brain blood volume and/or the brain blood flow of a patient is provided which may lead to an increased accuracy and hence more exact estimates.

A modeling method for screening surgical patients, used in analysis modeling for heart rate variability (HRV). Low-cost, portable and wearable signal acquisition equipment is utilized to acquire an electrocardiography (ECG) signal of an epileptic 24 hours before surgery; a multiscale entropy (MSE) of the ECG is calculated by means of a programmed HRV analysis method, wherein characteristic parameters representing heart rate complexity are extracted on the basis of an MSE curve, and a medical refractory epileptic suitable for vagus nerve stimulation (VNS) surgery is accurately and efficiently screened, thus avoiding unnecessary expenditures and avoiding delaying an optimal opportunity for treatment. Meanwhile, the curative effects of the VNS treatment may be wholly improved by means of clearly selecting VNS surgical indication patients according to the characteristic parameters of the MSE complexity of the ECG.

A modeling method for screening surgical patients, used in analysis modeling for heart rate variability (HRV). Low-cost, portable and wearable signal acquisition equipment is utilized to acquire an electrocardiography (ECG) signal of an epileptic 24 hours before surgery; a multiscale entropy (MSE) of the ECG is calculated by means of a programmed HRV analysis method, wherein characteristic parameters representing heart rate complexity are extracted on the basis of an MSE curve, and a medical refractory epileptic suitable for vagus nerve stimulation (VNS) surgery is accurately and efficiently screened, thus avoiding unnecessary expenditures and avoiding delaying an optimal opportunity for treatment. Meanwhile, the curative effects of the VNS treatment may be wholly improved by means of clearly selecting VNS surgical indication patients according to the characteristic parameters of the MSE complexity of the ECG.

An electrocardiogram waveform measurement system is configured to be suitable for measuring an electrocardiogram waveform with high precision using multiple electrodes provided to an article of clothing. A measurement unit performs measurement in a state in which multiple fabric electrodes are grouped into multiple channels. A signal measured by the electrodes for each channel is evaluated in a two-dimensional manner based on the number of times an R wave is detected by an R wave processing unit in a predetermined period of time and a degree of data concentration in a dynamic range acquired by a degree-of-concentration calculation unit. A channel selection unit selects the optimum channel.

An electrocardiogram waveform measurement system is configured to be suitable for measuring an electrocardiogram waveform with high precision using multiple electrodes provided to an article of clothing. A measurement unit performs measurement in a state in which multiple fabric electrodes are grouped into multiple channels. A signal measured by the electrodes for each channel is evaluated in a two-dimensional manner based on the number of times an R wave is detected by an R wave processing unit in a predetermined period of time and a degree of data concentration in a dynamic range acquired by a degree-of-concentration calculation unit. A channel selection unit selects the optimum channel.

The process for classifying anesthetic depth includes: collecting of biological signals, conditioning of said signals, monitoring of activity of the central and autonomic systems, measurement of indexes and classification of patterns in anesthetic depth. The activity includes: i) Awake: Vigil—Ak. and recovery of verbal response—Rc. ii) Light Anesthesia: Light induction anesthesia—Li. Light recovery—Lr, Light dose, increase in drugs or patient movement (La), iii) General anesthesia: General anesthesia—Ga, one minute after the start of the surgery, and iv) Deep anesthesia: identification of the EEG burst-suppression pattern (BSP) associated with deep anesthesia.

The process for classifying anesthetic depth includes: collecting of biological signals, conditioning of said signals, monitoring of activity of the central and autonomic systems, measurement of indexes and classification of patterns in anesthetic depth. The activity includes: i) Awake: Vigil—Ak. and recovery of verbal response—Rc. ii) Light Anesthesia: Light induction anesthesia—Li. Light recovery—Lr, Light dose, increase in drugs or patient movement (La), iii) General anesthesia: General anesthesia—Ga, one minute after the start of the surgery, and iv) Deep anesthesia: identification of the EEG burst-suppression pattern (BSP) associated with deep anesthesia.

A method includes receiving analog body-surface signal from body-surface electrode, and multiple analog unipolar signals from multiple unipolar electrodes of an invasive probe. A first unipolar electrode is assigned to serve as a common electrical ground and a common timing reference for the analog unipolar signals and the analog body-surface signal. The analog unipolar signals are digitized to produce digital unipolar signals sampled relative to a digital ground. Defined are an analog bipolar signal between the first unipolar electrode and a second unipolar electrode of the probe, and digital bipolar signal formed from the first unipolar electrode and the second unipolar electrode. Ground and timing offsets between the analog bipolar signal and the digital bipolar signal are estimated, while the first unipolar electrode is connected to the digital ground. The ground offset and the timing offset are applied in measuring a third unipolar signal, sensed by a third unipolar electrode.

A method includes receiving analog body-surface signal from body-surface electrode, and multiple analog unipolar signals from multiple unipolar electrodes of an invasive probe. A first unipolar electrode is assigned to serve as a common electrical ground and a common timing reference for the analog unipolar signals and the analog body-surface signal. The analog unipolar signals are digitized to produce digital unipolar signals sampled relative to a digital ground. Defined are an analog bipolar signal between the first unipolar electrode and a second unipolar electrode of the probe, and digital bipolar signal formed from the first unipolar electrode and the second unipolar electrode. Ground and timing offsets between the analog bipolar signal and the digital bipolar signal are estimated, while the first unipolar electrode is connected to the digital ground. The ground offset and the timing offset are applied in measuring a third unipolar signal, sensed by a third unipolar electrode.

A data generation method by a computer is disclosed. First waveform data including marking information at a first position on a waveform, and acquiring second waveform data are acquired. A transformation function is specified that transforms the first waveform data to reduce the difference between a first value of a time axis for a first characteristic point in the first waveform data and a second value of the time axis for a second characteristic point, in the second waveform data, corresponding to the first characteristic point. Third waveform data are generated, in which the marking information is applied at a second position corresponding to the first position in the first waveform data, the second position being determined by using the transformation function.