Abstract: In embodiments, a wearable cardioverter defibrillator (WCD) system includes electrodes that render an ECG signal of the patient, and a processor that receives ECG data are derived from the rendered ECG signal. The processor may filter the received ECG data with a matched difference filter to detect QRS complexes, and compute a heart rate from the detected QRS complexes. The matched difference filter itself can have coefficient values associated with a baseline QRS complex, which improves detection.

Abstract: Technologies and implementations for a wearable healthcare system including an information management module (IMM). The IMM may be configured to detect proximity of a healthcare device. The IMM may be configured to determine a type of the detected healthcare device. Once the type of healthcare device is determined, the IMM may be configured to communicate information regarding a medical device, where the communicated information may be adapted to correspond to the determined type of healthcare device.

Abstract: A medical data management system is provided that facilitates expedited review of health data for patient care. The medical data management system includes devices for collecting the health data, associating the health data with health events, optimizing storage of health data, and displaying comprehensive patient data for efficient and rapid review. The system includes a medical monitoring device that uses sensors to acquire health data and logic to specify data blocks of the data that characterize health episodes. Data management device(s) stitch the data blocks according to timelines and store the stitched data blocks as a full episode. The stitched data blocks are rendered for display, as a data presentation with timelines and graphic representations.

Abstract: A medical wearable matching system is provided that pre-assesses a fit of a wearable article of medical device for a patient. The matching system may facilitate fitting and selection of a class of wearable articles for a patient. The matching system may also evaluate potential effects of the fit on operations of the medical device. Patient specific data, such as measurements, are fed into a fit prediction artificial intelligence (“AI”) model. The fitting AI model is trained with fitting data of previous patients to predict a class of wearable medical article that is likely, above a threshold amount, to provide a target fit for the patient. The patient specific information may also be fed into an adverse potential AI model. The adverse potential AI model is trained with patient experience data describing adverse operations of a wearable article worn by prior patients.

Abstract: A wearable cardioverter defibrillator (WCD) comprises a plurality of electrocardiography (ECG) electrodes. An episode is opened responsive to a string of consecutive segments meeting one or more shock criteria, and a shockable rhythm is confirmed for the episode responsive to a subsequent string of consecutive segments meeting one or more confirmation criteria.

Abstract: Systems are provided to perform automated identification and dispatching of resources (e.g., patient service responders) to provide individualization of medical products and service to specific patients. Such systems can use decision-based tree methods. In some examples, the systems can learn from historical data (using AI technology, for example) to autonomously match patient's medical and personal needs and preferences with responder options and the requisite inventory. Such systems can be scalable for a growing number of patients and responders and can improve patient quality of care, treatment, and satisfaction. The automated systems and methods can alleviate burdens on medical network and resources including reduction of redundancies and costs. It can also help reduce, and if needed, quickly address any errors, human and/or technical, and aid with quality assurance.

Abstract: A Wearable Medical System (WMS) includes one or more pacing capabilities. The WMS may detect when the patient's heart rhythm starts to deteriorate, but not necessarily in a way that requires defibrillation. In particular, the WMS may detect bradycardia of one or more types, and then confirm the detection before pacing to treat the detected bradycardia.

Abstract: In one example, an apparatus of a wearable cardioverter defibrillator (WCD) system comprises a support structure wearable by a patient, a plurality of electrocardiogram (ECG) electrodes to obtain an ECG signal, a processor to receive and analyze the ECG signal of the patient, wherein the processor is configured to monitor four or more channels of the ECG signal, a high voltage subsystem coupled with defibrillation electrodes configured to be coupled with patient, wherein processor is configured to cause the high voltage subsystem to apply a therapeutic shock to the patient through the defibrillation electrodes in response to a shockable event detected by the processor from the ECG signal. The processor measures a peak-to-peak amplitude of QRS complexes of the ECG signal, and detects asystole in the patient when the peak-to-peak amplitude of one or more QRS complexes is less than an asystole threshold.

Abstract: A wearable medical device comprising monitoring circuitry to monitor one or more patient parameters of a patient and defibrillation circuitry to provide one or more defibrillation shocks to the patient responsive to a control signal from the monitoring circuitry. The defibrillation circuitry comprises a defibrillation capacitor to provide energy for the one or more defibrillation shocks. The wearable medical device also comprises a power source to provide power to the monitoring circuitry and the defibrillation circuitry. The power source comprises a low current power source (LCPS) to provide power to the monitoring circuitry, and a high current power source (HCPS) to provide power to the defibrillation circuitry.

Abstract: A wearable cardioverter defibrillator (WCD) system includes a support structure that the patient may wear, and one or more sensors that may acquire patient physiological signals, such as ECG and others. A processor of the WCD system may determine diagnostics from the patient physiological signals. These diagnostics include a six-second ECG portion, heart rates as histograms, heart rates against QRS width, heart rate trends, clinical event counters, diagnostics relating to heart rate variability and about the atrial arrhythmia burden of the patient. In some embodiments, the WCD system includes a user interface with a screen that displays these diagnostics. In some examples, the WCD system exports these diagnostics for viewing by a different screen. When viewed, these diagnostics permit more detailed analysis of the state of the patient.

Abstract: Disclosed are embodiments directed to security methods applied to connections between components in a distributed (networked) system including medical and non-medical devices, providing secure authentication, authorization, patient and device data transfer, and patient data association and privacy for components of the system.

Abstract: In one example, a method to aggregate information from one or more medical devices comprises collecting information from one or more medical devices used by a patient, aggregating the information from the one or more medical devices into a patient record, storing the patient record to be accessible by a care station server, and providing access to the patient record to via the care station server. Other examples and related methods and apparatuses are also disclosed herein.

Abstract: Embodiments of a wearable monitoring device system can include one or more dry ECG electrodes and a processor that can be configured with one or more algorithms for detecting atrial fibrillation (AF) from sensed ECG signals sensed by the one or more dry ECG electrodes, and optionally other signals. In some embodiments, the algorithms include one or more AF detection algorithms and optionally a noise detection algorithm. In some embodiments, the wearable monitoring device or a remote system that receives data from the wearable medical device may calculate and/or characterize AF burden from ECG signals sensed by the one or more dry ECG electrodes.

Abstract: A method to monitor a patient's heart health is described. The method may include processing at least one electrocardiogram (ECG) signal and diagnosing an episode of nonsustained ventricular tachycardia (NSVT) based at least in part on the processing of the at least one ECG signal. In some embodiments, the NSVT episode may satisfy an NSVT time duration and a QRS criterion. The NSVT time duration may be between 5 seconds and 15 seconds. In some instances, the QRS criterion may be a temporary QRS template of two sequential incoming QRS complexes. In some embodiments, the method may determine a similarity between the temporary QRS template and at least two subsequent QRS complexes by calculating a feature correlation coefficient between the QRS template and the subsequent QRS complexes.

Abstract: A wearable cardioverter defibrillator (WCD) system includes a support structure that the patient may wear, and one or more sensors that may acquire patient physiological signals, such as ECG and others. A processor of the WCD system may determine diagnostics from the patient physiological signals. These diagnostics include a six-second ECG portion, heart rates as histograms, heart rates against QRS width, heart rate trends, clinical event counters, diagnostics relating to heart rate variability and about the atrial arrhythmia burden of the patient. In some embodiments, the WCD system includes a user interface with a screen that displays these diagnostics. In some embodiments, the WCD system exports these diagnostics for viewing by a different screen. When viewed, these diagnostics permit more detailed analysis of the state of the patient.

Abstract: Technologies and implementations for a wearable healthcare system including an information management module (IMM). The IMM may be configured to detect proximity of a healthcare device. The IMM may be configured to determine a type of the detected healthcare device. Once the type of healthcare device is determined, the IMM may be configured to communicate information regarding a medical device, where the communicated information may be adapted to correspond to the determined type of healthcare device.

Abstract: In one example, a method to aggregate information from one or more medical devices comprises collecting information from one or more medical devices used by a patient, aggregating the information from the one or more medical devices into a patient record, storing the patient record to be accessible by a care station server, and providing access to the patient record to via the care station server. Other examples and related methods and apparatuses are also disclosed herein.

Abstract: An example wearable medical device (WMD) system implementing the disclosed techniques includes a support structure, the support structure configured to be worn by a subject. The WMD system also includes a consciousness detection module configured to determine whether the subject wearing the support structure is unconscious. The WMD system further includes an interactive bystander module configured to, responsive to a determination that the subject is unconscious, generate a first voice prompt inquiring regarding a presence of a bystander. Further, responsive to a determination that the bystander is present, the interactive bystander module is configured to generate a second voice prompt inquiring whether the bystander is willing to perform a medical procedure to assist the subject.

Abstract: In one example, a cardiac monitoring system comprises a processor to receive a segment of an electrocardiogram (ECG) signal of a patient, and a memory to store the segment of the ECG. The processor is configured to identify QRS complexes in the segment of the ECG signal, generate a supraventricular (SV) template for SV complexes in the QRS complexes, identify SV complexes in the QRS complexes using the template, identify normal sinus rhythm (NSR) complexes in the segment of the ECG signal, obtain an atrial template for atrial waveforms in the NSR complexes, measure a range of a P-wave of the atrial waveforms from the NSR complexes, save the measured P-waves, and classify the identified SV complexes as either atrial fibrillation (AF) or supraventricular tachycardia (SVT) using the atrial template. Other examples and related methods are also disclosed herein.

Abstract: A method to determine reliable electrocardiogram (ECG) signal is described. The method includes receiving at least one ECG signal for a period of time from a patient. The method also includes analyzing the at least one ECG signal to determine a first heart rate using a first method and analyzing the at least one ECG signal to determine a second heart rate using a second method different from the first method. The method includes comparing the first and second heart rates to each other and classifying the at least one ECG signal as reliable when a reliability threshold is satisfied.