A medical system is provided. The medical system includes a medical device and a mobile computing device. The medical device includes at least one physiologic sensor configured to acquire physiological signals from a patient, at least one processor coupled to the at least one physiologic sensor, and at least one optical code encoded with encrypted data. The mobile computing device includes a camera and one or more processors coupled to the camera and configured to acquire one or more images of the at least one optical code, decode the one or more images of the at least one optical code to generate a copy of the encrypted data, decrypt the copy of the encrypted data to generate decrypted data, and process the decrypted data to establish an operable connection between the mobile computing device and the medical device.

Some embodiments of a system or method for treating heart tissue can include a control system and catheter device operated in a manner to intermittently occlude a heart vessel for controlled periods of time that provide redistribution of blood flow. In particular embodiments, the system and methods may be configured to monitor at least one input signal detected at a coronary sinus and thereby execute a process for determining a satisfactory time period for the occlusion of the coronary sinus. In further embodiments, after the occlusion of the coronary sinus is released, the control system can be configured to select the duration of the release phase before the starting the next occlusion cycle.

A method of processing cardiac ultrasound data for determining information about a mechanical wave in the heart. The method comprises receiving data representative of a time series of three-dimensional data frames, generated from ultrasound signals from a human or animal heart, each frame comprising a set of voxels, each voxel value representing an acceleration component of a respective location in the heart at a common time. The method also comprises identifying, for each voxel, a frame of the series in which the voxel value is at a maximum. A three-dimensional time-propagation data set is generated by assigning each voxel a value representative of the time of the respective frame in the time series for which the corresponding voxel is at a maximum. The method then comprises generating data representative of a three-dimensional velocity vector field by calculating time derivatives from the three-dimensional time-propagation data set.

Systems, devices, and methods for electroporation ablation therapy are disclosed, with a protection device for isolating electronic circuitry, devices, and/or other components from a set of electrodes during a cardiac ablation procedure. A system can include a first set of electrodes disposable near cardiac tissue of a heart and a second set of electrodes disposable in contact with patient anatomy. The system can further include a signal generator configured to generate a pulse waveform, where the signal generator coupled to the first set of electrodes and configured to repeatedly deliver the pulse waveform to the first set of electrodes. The system can further include a protection device configured to selectively couple and decouple an electronic device to the second set of electrodes.

A system that synchronizes waveforms received over a network from one or more devices, such as medical devices. Because of network delays or losses, waveforms can arrive at varying rates and times. Precise post-synchronization of the received data, to within a few milliseconds, is needed for accurate analysis. Applications include automatic classification of waveforms, such as detection of myocardial infraction from heart monitor waveforms. Synchronization uses sequence numbers assigned by each device, but must also account for sequence number wraparounds. Waveforms may also be synchronized across devices, by calculating the bias between within-device synchronized times and a common time source or common disturbance. Waveform data may also be stored data in a database or data warehouse; embodiments may index the data using a key with a date-time prefix and a hash code suffix, to support distributed indexing while reducing the chance of hash collisions to a very small probability.

According to one embodiment, there is provided an X-ray diagnostic apparatus which comprises an X-ray generation unit configured to irradiate an object with X-rays; an X-ray detection unit configured to detect X-rays applied by the X-ray generation unit and transmitted through the object; an image generation unit configured to generate an X-ray image based on X-rays detected by the X-ray detection unit; a recording unit configured to record pressure data acquired by using a pressure sensor provided on a guide wire; a measurement position setting unit configured to set a measurement position for a pressure by the pressure sensor using the X-ray image; and a display unit configured to display the X-ray image almost in real time and superimpose and display the measurement position set by the measurement position setting unit.

A system that synchronizes waveforms received over a network from one or more devices, such as medical devices. Because of network delays or losses, waveforms can arrive at varying rates and times. Precise post-synchronization of the received data, to within a few milliseconds, is needed for accurate analysis. Applications include automatic classification of waveforms, such as detection of myocardial infraction from heart monitor waveforms. Synchronization uses sequence numbers assigned by each device, but must also account for sequence number wraparounds. Waveforms may also be synchronized across devices, by calculating the bias between within-device synchronized times and a common time source or common disturbance. Waveform data may also be stored data in a database or data warehouse; embodiments may index the data using a key with a date-time prefix and a hash code suffix, to support distributed indexing while reducing the chance of hash collisions to a very small probability.

To provide an electrocardiogram analysis system capable of determining the need for an electric shock to a patient undergoing cardiopulmonary resuscitation (CPR) with a higher accuracy. An electrocardiogram analysis system includes electrocardiogram (ECG) signal acquiring means 11, ECG signal sampling means 12, ECG spectrogram transforming means 13, impedance signal acquiring means 21, impedance signal sampling means 22, impedance spectrogram transforming means 23, a convolutional neural network (CNN) 4 including an input layer 4I, an output layer 4O, sample data accumulation means 4L, and sample data input means 4T, and electric shock indication reporting means 5. The CNN is a priori provided with sample data including sample ECG spectrograms and sample impedance spectrograms obtained from a large number of subjects, and sample response data on the need for an electric shock, and is optimized by self-learning the sample data.

Various techniques related to post-treatment parameters of patients are described. An example method includes identifying a segment of an electrocardiogram (ECG) of an individual, wherein the segment is detected during a time interval that begins when an electrical shock is output to the individual's heart. The method further includes identifying a parameter of the electrical shock and generating a report including the segment of the ECG and indicating the parameter of the electrical shock. The report is output or transmitted to an external device.

Various techniques related to post-treatment parameters of patients are described. An example method includes identifying a segment of an electrocardiogram (ECG) of an individual, wherein the segment is detected during a time interval that begins when an electrical shock is output to the individual's heart. The method further includes identifying a parameter of the electrical shock and generating a report including the segment of the ECG and indicating the parameter of the electrical shock. The report is output or transmitted to an external device.