A system and method for synchronizing patient medical parameters and dialysis parameters. The system and related method allow for the determination of the effect of dialysis on patient health. The invention also allows for the determination of whether observed patient health effects are due to specific dialysis parameters and for making necessary changes to the dialysis parameters in order to improve patient health.

According to at least one aspect, a wearable cardiac device is provided. The wearable cardiac device includes a garment worn about a torso of a patient, at least one sensing electrode to monitor cardiac activity of the patient, and a controller including a plurality of separate and distinct modules distributed about and/or integrated into the garment. The plurality of separate and distinct modules includes, for example, an operations module coupled to the at least one sensing electrode and configured to detect at least one cardiac condition of the patient and/or a communications module coupled to the operations module to communicate with an external device. In some examples, the wearable cardiac device may be configured as a treatment device and include an energy storage module coupled to at least one therapy electrode and configured to store energy for at least one therapeutic shock to be applied to the patient.

The invention provides a sensor for measuring both impedance and ECG waveforms that is configured to be worn around a patient's neck. The sensor features 1) an ECG system that includes an analog ECG circuit, in electrical contact with at least two ECG electrodes, that generates an analog ECG waveform; and 2) an impedance system that includes an analog impedance circuit, in electrical contact with at least two (and typically four) impedance electrodes, that generates an analog impedance waveform. Also included in the neck-worn system are a digital processing system featuring a microprocessor, and an analog-to-digital converter. During a measurement, the digital processing system receives and processes the analog ECG and impedance waveforms to measure physiological information from the patient. Finally, a cable that drapes around the patient's neck connects the ECG system, impedance system, and digital processing system.

A patient monitoring system for monitoring cardiac arrhythmias includes an ECG monitor configured to monitor cardiac potentials during cardiac cycles and an arterial blood flow monitor configured to monitor arterial blood flow and generate pulse waveform. The system further includes an arrhythmia detection module that detects the presence of an arrhythmia based on the cardiac potentials and generates an arrhythmia indicator, and an arrhythmia analysis module that assesses the severity of the detected arrhythmia. The arrhythmia analysis module calculates average sinus pulse information based on the pulse waveform data for two or more cardiac cycles occurring when no arrhythmia is detected, and then calculates average arrhythmia pulse information based on pulse waveform data for two or more cardiac cycles occurring after detection of the arrhythmia. The average arrhythmia pulse information is then compared to the average sinus pulse information and an arrhythmia severity indicator is generated based on the comparison.

Apparatus and methods for clearing obstructions from a medical tube are disclosed. In an exemplary embodiment, a spool drive system is disclosed for actuating a guide wire within a medical tube. The spool drive system can be a hand held, disposable device having a spool housing with a spool therein for alternately advancing and withdrawing a guide wire through an inlet of the spool housing. A track can be spaced from and extend about a perimeter of the spool to direct the guide wire onto or off of the spool as the spool is rotated. Other embodiments and devices for actuating the guide wire, and methods therefor, also are disclosed.

Methods and systems are described for detecting the likelihood of patent ductus arteriosus (PDA) in an infant using electrocardiogram and photoplethysmographic pulse signals obtained from the upper body and foot of the infant.

An automatic recognition and classification method for electrocardiogram heartbeat based on artificial intelligence, comprising: processing a received original electrocardiogram digital signal to obtain heartbeat time sequence data and lead heartbeat data; cutting the lead heartbeat data according to the heartbeat time sequence data to generate lead heartbeat analysis data; performing data combination on the lead heartbeat analysis data to obtain a one-dimensional heartbeat analysis array; performing data dimension amplification and conversion according to the one-dimensional heartbeat analysis array to obtain four-dimensional tensor data; and inputting the four-dimensional tensor data to a trained LepuEcgCatNet heartbeat classification model, to obtain heartbeat classification information. The method overcomes the defect that the conventional method only depends on single lead independent analysis for result summary statistics and thus classification errors are more easily obtained, and the accuracy of the electrocardiogram heartbeat classification is greatly improved.

The present disclosure provides systems and methods for detecting cardiac activation times of a patient. A system includes a data acquisition system and a processor communicatively coupled thereto. The data acquisition system is configured to detect a plurality of electrograms generated at a plurality of respective electrodes coupled to the patient. The processor is configured to receive the plurality of electrograms from the data acquisition system. The processor is further configured to compute respective energies of the plurality of electrograms. The processor is further configured to detect a cardiac activation time for a first electrogram among the plurality of electrograms based on the respective energy of the first electrogram and the respective energy of a second electrogram that neighbors the first electrogram.

Described herein is a system and method of automatically monitoring QT intervals in a patient based on one or more EKG signals received from attached monitoring devices. Each EKG signal is analyzed to detect attributes of the first and second EKG signals, including QRS onset information, QRS peak information, and T-wave offset information. A QT interval is calculated based on QRS onset information derived from the first EKG signal and T-wave offset information derived from the second EKG signal. The calculated QT interval is compared to thresholds to detect elongation of the QT interval and an alert is generated in response to a detected elongated QT interval.

Systems and methods for measuring signals representative of muscle activity are provided. One method includes detecting an ECG signal through a plurality of electrodes. The ECG signal includes a plurality of ECG sample signals, and each ECG sample signal is a bipolar signal associated with two of the plurality of electrodes and includes a cardiac signal component and a myographic signal component. The method further includes filtering each of the ECG sample signals to remove at least a portion of the cardiac signal component and generate a combined myographic power signal for the two of the plurality of electrodes with which the ECG sample signal is associated. Each combined myographic power signal represents a myographic potential between the two electrodes. The method further includes calculating individual myographic power signals for each of the plurality of electrodes by applying the combined myographic power signals within a covariance matrix.