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 wearable cardiac monitoring device comprises a processor; a electrocardiogram (ECG) signal collecting unit; a photoelectric signal collecting unit; and a power source configured to provide power to the processor, the ECG signal collecting unit and the photoelectric signal collecting unit simultaneously; wherein the processor determines whether the current mode is at a ECG collecting mode or a photoelectric collecting mode; wherein the ECG signal collecting unit collects user's ECG signals in the ECG collecting mode, and the photoelectric signal collecting unit collects photoelectric signals of the user's measured part under the illumination of light.

A wearable cardiac monitoring device comprises a processor; a electrocardiogram (ECG) signal collecting unit; a photoelectric signal collecting unit; and a power source configured to provide power to the processor, the ECG signal collecting unit and the photoelectric signal collecting unit simultaneously; wherein the processor determines whether the current mode is at a ECG collecting mode or a photoelectric collecting mode; wherein the ECG signal collecting unit collects user's ECG signals in the ECG collecting mode, and the photoelectric signal collecting unit collects photoelectric signals of the user's measured part under the illumination of light.

Systems and methods for monitoring chronic over-pacing (COP) to the heart are discussed herein. In an embodiment, a system includes a receiver circuit to receive information about pacing rates of a plurality of paced heart beats, and a pacing analyzer circuit to generate a pacing rate distribution using pacing rates of the plurality of the paced heart beats. The pacing rate distribution includes a pacing rate histogram. The pacing analyzer circuit may recognize a morphological pattern from the pacing rate distribution, and detect a COP indication using the extracted feature. A programmer circuit adjusts one or more therapy parameters in response to the detected. COP indication.

Systems and methods for monitoring chronic over-pacing (COP) to the heart are discussed herein. In an embodiment, a system includes a receiver circuit to receive information about pacing rates of a plurality of paced heart beats, and a pacing analyzer circuit to generate a pacing rate distribution using pacing rates of the plurality of the paced heart beats. The pacing rate distribution includes a pacing rate histogram. The pacing analyzer circuit may recognize a morphological pattern from the pacing rate distribution, and detect a COP indication using the extracted feature. A programmer circuit adjusts one or more therapy parameters in response to the detected. COP indication.

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.

Techniques are disclosed for detecting arrhythmia episodes for a patient. A medical device may receive one or more sensor values indicative of motion of a patient. The medical device may determine, based at least in part on the one or more sensor values, an activity level of the patient. The medical device may determine a heart rate threshold for triggering detection of an arrhythmia episode based at least in part on the activity level of the patient. The medical device may determine whether to trigger detection of the arrhythmia episode for the patient based at least in part on comparing a heart rate of the patient with the heart rate threshold. The medical device may, in response to triggering detection of the arrhythmia episode, collect information associated with the arrhythmia episode.

Apparatuses, systems and methods are provided that may include a system for patient monitoring and defibrillation. The system may include at least two defibrillation electrodes. The system may further include a first unit for physiological monitoring of a patient, including ECG monitoring circuitry for monitoring ECG of the patient. The first unit may store CPR chest compression data. The system may further include a second unit, separate from the first unit, which may communicatively couple with the first unit, for providing defibrillation pulses to the patient. The second unit may include a processor, communicatively coupled with the at least two defibrillation electrodes, for providing defibrillation pulses to the patient via the at least two defibrillation electrodes.

Apparatuses, systems and methods are provided that may include a system for patient monitoring and defibrillation. The system may include at least two defibrillation electrodes. The system may further include a first unit for physiological monitoring of a patient, including ECG monitoring circuitry for monitoring ECG of the patient. The first unit may store CPR chest compression data. The system may further include a second unit, separate from the first unit, which may communicatively couple with the first unit, for providing defibrillation pulses to the patient. The second unit may include a processor, communicatively coupled with the at least two defibrillation electrodes, for providing defibrillation pulses to the patient via the at least two defibrillation electrodes.

The present disclosure relates to a wearable device with a bridge portion and systems/methods relating to the device. Preferred embodiments may include two flexible wings and a bridge connecting the two wings. In some embodiments, the upper surface of the bridge can be non-adhesive and uncoupled to the flexible wing such that the flexible wing can be decoupled from the bridge when the adhesive is adhered to the surface of a user. The bridge can be narrower in some portions, and extend around the housing of the monitor. The bridge can extend beneath the housing and bisect the two flexible wings.