Certain aspects of the present disclosure provide methods and apparatus for determining a precise localization of an arrhythmia origin or exit site in a heart of a subject using internal electrocardiograph (ECG) signals sensed and stored by an implantable device implanted in the subject. One example method of analyzing an arrhythmia in a subject generally includes reading, from an implantable device implanted in the subject, a plurality of internal ECG signals sensed and stored by the implantable device while the subject was experiencing an arrhythmia event (e.g., at any time, including while the subject was ambulatory); performing an analysis of the read internal ECG signals; and determining a localization of the arrhythmia associated with the arrhythmia event, based on the analysis.

The invention discloses a sparse Bayesian learning (SBL)-based SSR brain source localization method. An SSR record is divided into multiple data segments, frequency-domain information of the data segments is extracted through FFT, and a data matrix is constructed. An automatic iteration stop condition and initial values of a sparse support vector and a spontaneous electroencephalography (EEG)-electrical noise joint power vector are set. The posteriori mean and covariance of SSR components are iteratively estimated and the sparse support vector and the spontaneous EEG-electrical noise joint power vector are updated accordingly. When the iteration ends, the ultimate sparse support vector is used to give a source localization result. An SSR source localization problem is modeled in the frequency domain, the joint sparsity of signals in multiple data segments is involved, and a brain source localization method applicable to various SSRs is given in an SBL framework.

A system screens, diagnoses, or monitors sleep disordered breathing of a patient. The system may include a nasal cannula, a conduit connected to the nasal cannula at a first end, an adaptor configured to receive a second end of the conduit and/or a portable computing device. The adaptor may be configured to position the second end of the conduit in proximity with a microphone of the portable computing device. Optionally, a processor may generate an indicator to guide placement of the adaptor for use. Such positioning may, in use, permit the microphone to generate a patient breathing sound signal via the adaptor for processor(s) of the device. The processor(s) may then process the breathing sound signal. The process may include detecting SDB events from an extracted and/or de-rectified loudness signal. The process may include computing a metric of severity of a respiratory condition of the patient using detected SDB events.

A system for assessing and monitoring a fetal electrocardiogram (ECG) and heart rate in a pregnant mother comprises wearable mechanical-electronic sensors, e.g., embedded in a wrist or arm band, which can measure mechanical pulse signals from the mother, and an abdomen patch which can measure the combined ECG signals of the fetus and mother. In another embodiment, the sensors in the wrist or arm band measure the combined fetal/maternal ECG signals, and the mother's mechanical pulse signals. By signal processing and gating out the maternal ECG signals as correlated with the mechanical maternal pulse signals, the fetal ECG and heart rate can be measured and monitored. These measurements may be displayed on the wrist or arm band device, or wirelessly through a remote device, mobile phone or computer. Sensors in the abdominal patch may also measure uterine electromyogram, uterine contractions, and fetal movements, to be correlated with the fetal ECG.

The present disclosure relates generally to pacing of cardiac tissue, and more particularly to adjusting delivery of His bundle or bundle branch pacing in a cardiac pacing system to achieve synchronized ventricular activation. A leadless pacing device (LPD) may include a plurality of electrodes comprising a bundle pacing electrode leadlessly connected to the housing, which may be implanted proximate to or in the His bundle or bundle branch of the patient's heart. An electrical pulse generator may generate and deliver electrical His-bundle or bundle-branch stimulation pulses using the bundle pacing electrode based on sensing one or both of an atrial event and a ventricular event. The LPD may receive communication from another implantable device, such as a subcutaneously implanted device, and deliver His-bundle or bundle-branch pacing in response to the communication.

A method, device, and system for evaluating a vessel of a patient, and in particular the hemodynamic impact of a stenosis within the vessel of a patient. Proximal and distal pressure measurements are made using first and second instrument while the first instrument is moved longitudinally through the vessel from a first position to a second position and the second instrument remains in a fixed longitudinal position within the vessel. A series of pressure ratio values are calculated, and a pressure ratio curve is generated. One or more stepped change in the pressure ratio curve are then identified and/or located using an Automatic Step Detection (ASD) process and/or algorithm. The ASD includes identifying a general position of a starting point of the stepped change by identifying a change in the pressure ratio values within a first window along the pressure ratio curve that is at or above a first threshold change value, and identifying an optimized position of the starting point by identifying a change in the pressure ratio values within a second window along the pressure ratio curve that is at or above a second threshold change value, wherein the second window is smaller than the first window, and the second threshold change value is smaller than the first threshold change value.

A microcirculation detection system including a heating device, a photosensitive array and a processing unit is provided. The heating device is used to heat a skin area. The photosensitive array is used to detect outgoing light from the skin area, and output a plurality of brightness variation signals respectively at different time points within a heating period. The processing unit is used to calculate a change of an array energy distribution varied within the heating period according to the plurality of brightness variation signals to accordingly identify a microcirculation state.

Provided are a monitoring device, a method for setting reference baseline and a readable storage medium. Whether the monitoring device has a system error is determined due to at least one event, according to at least one of the moving state of the target object, the connection state of the target object and the signal acquisition device, the environmental information where the target object is located, and the analysis result of the monitoring device. When the monitoring device has a system error, the current reference baseline of the monitoring device is updated according to the preset rules, and then the monitoring is continued based on the new reference baseline. In this way, the current reference baseline of the monitoring device can be adjusted in time, false alarms and missed alarms caused by the system error can be avoided and the accuracy of the monitoring device can be improved.

Systems and methods for tracking EEG data and providing enhanced seizure detection and prediction are disclosed. The systems and methods use input sensors for receiving and collecting data from a plurality of EEG channels in association with a subject and processing said data to calculate and average Lyapunov exponents for a composite EEG data set. The systems and methods convert the average Lyapunov exponents into graphical representations that are displayed against a time axis. The graphical output adjusts in real-time according to the input data obtained from EEG channels. The systems and methods utilize pattern recognition to output alarms based upon input data and recommend diagnoses related to seizures.

Various examples of methods and systems are provided for real-time signal processing. In one example, a method for processing data to select a pattern includes receiving data via a sensor, evaluating the data including waveforms over a time domain, averaging the waveforms to obtain a mean waveform, selecting a pattern based on the mean waveform, and generating a notification regarding the selected pattern. The pattern can include a start time, a hold time, and an end time. In another example, a system includes one or more sensors that detect the data and a mobile platform that evaluates the data, averages the waveforms to obtain the mean waveform and selects a pattern based on the mean waveform. A user interface can be used to communicate the notification regarding the selected pattern. The patterns can include breathing patterns, which can be used to reduce stress in a subject being monitored by the sensor.