The embodiments described herein and/or portions thereof can be formed or constructed of one or more biocompatible materials. In some embodiments, the biocompatible materials can be selected based on one or more properties of the constituent material such as, for example, stiffness, toughness, durometer, bioreactivity, etc. Examples of suitable biocompatible materials include but are not necessarily limited to metals, glasses, ceramics, and/or polymers. Examples of suitable metals include pharmaceutical grade stainless steel, gold, titanium, nickel, iron, platinum, tin, chromium, copper, and/or alloys thereof. A polymer material may be biodegradable or non-biodegradable. Examples of suitable biodegradable polymers include polylactides, polyglycolides, polylactide-co-glycolides, polyanhydrides, polyorthoesters, polyetheresters, polycaprolactones, polyesteramides, poly(butyric acid), poly(valeric acid), polyurethanes, biodegradable polyamides (nylons), and/or blends and copolymers thereof. Examples of non-biodegradable polymers include non-degradable polyamides (nylons), polyesters, polycarbonates, polyacrylates, polymers of ethylene-vinyl acetates and other acyl substituted cellulose acetates, non-degradable polyurethanes, polystyrenes, polyvinyl chloride, polyvinyl fluoride, poly(vinyl imidazole), chlorosulphonate polyolefins, polyethylene oxide, and/or blends and copolymers thereof.

Non-limiting examples of suitable biocompatible polymer materials can include polylactides, polyglycolides, polylactide-co-glycolides, polyethylene-glycols, polyanhydrides, polyorthoesters, polyetheresters, polycaprolactones, polyesteramides, poly(butyric acid), poly(valeric acid), polyurethanes, polyamides (nylons), polyesters, polycarbonates, polyacrylates, polystyrenes, polypropylenes, polyethylenes, polyethylene oxide, polyolefins, polyethersulphones, polysulphones, polyvinylpyrrolidones, polyvinyl chloride, polyvinyl fluoride, poly(vinyl imidazole), polyether urethanes, silicone polyether urethanes, polyetheretherketones (PEEK), polytetrafluoroethylenes (PTFE), polylactones, chlorosulphonate polyolefins, ethylene-vinyl acetates and other acyl substituted cellulose acetates, elastomers, thermoplastics, and/or blends and copolymers thereof.

The embodiments, methods, and/or implementations herein, and/or the various features or advantageous details thereof, are explained more fully with reference to the non-limiting examples illustrated in the accompanying drawings and detailed in the following description. The examples and/or embodiments described herein are intended to facilitate an understanding of structures, functions, and/or aspects of the embodiments, ways in which the embodiments may be practiced, and/or to further enable those skilled in the art to practice the embodiments herein. Similarly, methods and/or ways of using or implementing the embodiments described herein are provided by way of example only and not limitation. Specific uses and/or implementations described herein are not provided to the exclusion of other uses unless the context expressly states otherwise. Descriptions of well-known components, methods, techniques, etc. may be omitted so as to not obscure the embodiments herein. Like numbers refer to like elements throughout.

FIG. 1 schematically depicts an implantable cardioverter defibrillator (ICD) system 100 including an ICD 110 engaging a patient P according to an embodiment. The ICD 110 is implanted (or implantable) in the patient P. The ICD system 100 can be utilized for treating certain conditions or states of a heart H of the patient P via certain electric treatment therapies, while reducing a likelihood of providing undesired, unnecessary or inappropriate treatments (e.g., “false positives”). The ICD 110 includes an ICD generator 120 and an ICD lead 140. The ICD system 100 further includes a first sensor 144a and a second sensor 144b in communication with the ICD 110.

While the ICD system 100 is described herein as including “the first sensor 144a” and “the second sensor 144b,” it should be understood that the first sensor 144a can be a single sensing device or multiple sensing devices that collectively function as the first sensor 144a, and similarly, the second sensor 144b can be a single sensing device or multiple sensing devices that collectively function as the second sensor 144b. In some implementations, multiple sensing devices can allow for sensor data that includes multiple signal vectors (e.g., multiple cardiac electrical and/or mechanical signal vectors). Similarly, while the ICD system 100 is described herein as including “the first sensor 144a” and “the second sensor 144b,” it should be understood that the ICD system 100 can include any number of additional sensors configured to sense and/or detect any suitable characteristic(s) associated with the patient (e.g., cardiac electrical and/or mechanical signals or any suitable non-cardiac signals). In some implementations, each sensor can be configured to sense and/or detect a different characteristic associated with the heart, or more generally, the patient. In some implementations, one or more sensors can be configured to sense or detect the same characteristic, thereby allowing for confirmation/verification of signal data and/or a desired degree of sensitivity and/or specificity in interpreting the signal data.

The ICD 110 is configured to receive signals from the first sensor 144a and the second sensor 144b associated with one or more characteristics of the patient P or the heart H of the patient P. In some embodiments, the ICD 110 can receive multiple signals from one or both of the first sensor 144a and the second sensor 144b. The ICD 110 includes the ICD generator 120, which can include a processor 122, a memory 124, a power system 126, a communication device 128, and a lead interface 130. The ICD generator 120 is configured to determine when to provide treatment and to generate treatment energy that can be sent to the heart H via the ICD lead 140. In some embodiments, the ICD generator 120 can be placed on the pectoralis major muscle of the patient P, behind the pectoral major muscle, on the abdomen, along the left exterior thorax, or elsewhere on or in the body of the patient P.

The processor 122 is configured to execute the operations of the ICD generator 120. The processor 122 can be, for example, a hardware based integrated circuit (IC), or any other suitable processing device configured to run and/or execute a set of instructions or code. For example, the processor 122 can be a general-purpose processor, a central processing unit (CPU), an accelerated processing unit (APU), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a programmable logic array (PLA), a complex programmable logic device (CPLD), a programmable logic controller (PLC), and/or the like. The processor 22 can be operatively coupled to the memory 124 through a system bus (for example, address bus, data bus, and/or control bus).

The memory 124 stores instructions that are executed by the processor 122. The memory 124 can be, for example, a random-access memory (RAM), a memory buffer, a hard drive, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), and/or the like. In some instances, the memory 124 can store, for example, one or more software programs and/or code that can include instructions to cause the processor 122 to perform one or more processes, functions, and/or the like. In some implementations, the memory 124 can include extendable storage units that can be added and used incrementally. In some instances, the memory 124 can be remotely operatively coupled with a compute device (not shown). For example, a remote database device can serve as a memory (or at least a portion of a memory) and be operatively coupled to the compute device (e.g., via a network or the like).

The memory 124 can, for example, include instructions on determining when treatment should be applied to the heart of the patient P. The memory 124 can include instructions that can cause the processor 122 to analyze sensor data received from the first sensor 144a and/or the second sensor 144b and to determine and/or define one or more treatment characteristics, modalities, methods, etc. These instructions can be updated and/or refined based on learnings from previously received sensor data. The memory 124 can further include instructions for managing the power usage of the power system 126, which can be configured to apply or provide electrical power (e.g., shock) according to the treatment characteristics, modalities, methods, etc., determined and/or defined by the processor 122.

The processor 122 is configured to execute instructions stored in the memory 124. More specifically, the memory 124 can, in some embodiments, include instructions associated with preprocessing the signals from the first sensor 144a and the second sensor 144b. For example, the memory 124 can include and/or store instructions that when executed by the processor 122 perform one or more preprocessing steps that can include amplification, filtering, analog to digital conversion, signal recognition and detection, synchronization, and/or the like. The processor 122 is configured to execute instructions, stored and/or included in the memory 124, for determining a cardiac status based on the signals, with or without preprocessing. The cardiac status can be associated with a heartbeat, an output of the heart, sensor data, and/or the like. Determining the cardiac status can include comparing an output from the first sensor 144a and an output from the second sensor 144b to recognize and confirm cardiac activity. For example, if the output from the first sensor 144a appears to indicate an irregular heartbeat, but the output from the second sensor 144b does not appear to indicate an irregular heartbeat, then the output from the first sensor 144a may be determined to be a false positive. Determining false positives can allow the ICD 110 to decrease the number of undesired treatments for the patient P and is more specific than if using an output from just one of the first sensor 144a or the second sensor 144b. If both the first sensor 144a and the second sensor 144b indicate the same and/or complementary cardiac status, that cardiac status is determined and/or confirmed. For example, in some embodiments, the memory 124 can include instructions associated with correlating heart rate data (e.g., from the first sensor 144a) and hemodynamic output or status data (e.g., from the second sensor 144b).

In some embodiments, determining the cardiac status can utilize an artificial intelligence algorithm (e.g., a machine learning model and/or algorithm) to further refine, determine, and/or predict the cardiac status. For example, the machine learning model can be trained to determine the cardiac status based at least in part on cardiac signal data and hemodynamic status data. In some embodiments, the machine learning model and/or algorithm (referred to herein as machine learning “model” or “algorithm” interchangeably) can be configured to filter out or recognize heartbeats and/or other cardiac activity that were determined or missed during an initial determination. In some embodiments, the machine learning algorithm can correlate the outputs from the first sensor 144a and the second sensor 144b to determine a pattern corresponding to a cardiac rate, status, QRS complex, etc. In some embodiments, the machine learning algorithm can learn from the outputs of the first sensor 144a and the second sensor 144b to determine long-term correlation and changes in correlation between the output of the first sensor 144a and the second sensor 144b.

The processor 122 may be configured to execute instructions, stored and/or included in the memory 124, associated with determining if a health event (e.g., an adverse health event that may be undesirable or dangerous to the patient such as, for example, cardiac arrest, ventricular fibrillation, cardiac tamponade, tachycardia, and/or other cardiac electrical or mechanical pathologies) is occurring based on the cardiac status. The health event can correspond to at least one of cardiac attack, tachycardia, fibrillation, arrythmias, and/or the like. In some embodiments, the processor 122 may execute a machine learning algorithm, which may be the same or different than that used in refining, determining, and/or predicting the cardiac status, for determining if a health event is occurring. The memory 124 can further include and/or store instructions for execution by the processor 122 associated with determining the type of health event. For example, the processor 122 determining the type of health event can include correlating the cardiac status with known cardiac statuses associated with health events. In some embodiments, the memory 124 can further include and/or store instructions for determining if the health event is an adverse health event. Similarly stated, the memory 124 can include and/or store instructions for determining and/or distinguishing an adverse health event from, for example, a normal or non-adverse health event that may present and/or may be associated with one or more similar characteristics.

In some embodiments, the processor 122 can determine the type of adverse health event (e.g., any of the adverse health events described herein). In some embodiments, the processor 122 may execute one or more machine learning algorithms, which may be the same or different than the other machine learning algorithms described herein, for determining the type of health event. Based on the type of health event determined, the memory 124 may store and/or include instructions for execution by the processor 122 associated with determining a treatment based on the type of health event. For example, the memory 124 may store and/or include instructions associated with determining the treatment based on the type of adverse health event. For example, if the adverse health event is determined to be tachycardia, then the treatment may correspond to anti-tachycardia pacing. The memory 124 can store and/or include instructions for execution by the processor 122 associated with determining the type of treatment as well as specific parameters for the treatment based on the adverse health event and, optionally, any other information (e.g., patient information, cardiac status, sensor signals, etc.). The memory 124 further stores and/or includes instructions for execution by the processor 122 for applying the treatment to the heart H of the patient P. This can include instructions for commanding and/or at least partially controlling the ICD to deliver the treatment to the heart H of the patient P (e.g., via the power system 126). In some embodiments, the memory 124 can store and/or include instructions that cause the processor 122 to alter or pause treatment during treatment delivery based on outputs from the first sensor 144a and the second sensor 144b. In some embodiments, the memory 124 can store and/or include instructions that cause the processor 122 to operate the generator to generate the treatment energy based on the type, severity, duration, and/or other characteristic(s) of an adverse health event.

The power system 126 is configured to store energy for use by the ICD and to generate treatments for delivery to the patient P. In some embodiments, the power system 126 can include at least one battery (e.g., LiPo, Li-ion, etc.). The at least one battery can be charged when the battery is low on power. In some embodiments, the power system 126 includes a primary cell battery and a rechargeable battery. In some embodiments, the power system 126 can be configured to charge automatically (e.g., via patient P movement) or wirelessly (e.g., via inductive charging). The power system 126 can also be configured to generate a therapy signal (e.g., anti-tachycardia pacing, defibrillator shock, etc.). For example, the therapy signal can include treatment energy for at least one of anti-tachycardia pacing and/or shock therapy. In some implementations, the treatment energy for anti-tachycardia pacing can be low-power or relatively low-power treatment energy and the treatment energy for shock therapy can be high-power or relatively high-power treatment energy. In some embodiments, the operation of the power system 126 is controlled via the processor 122 executing instructions stored in the memory 124. In some embodiments, the processor 122 may execute instructions that result in the power system 126 cycling the power of the first sensor 144a and/or the second sensor 144b to conserve power.

The communication device 128 is configured to allow the ICD generator 120 to communicate with one or more devices such as, for example, an external device, an implantable device, controller, server, etc. (e.g., a compute device, controller, etc. of a medical professional, the patient P, and/or the like). The communication device 128 can be configured to send information via one or more networks using any suitable communication mode (e.g., Bluetooth, Wi-Fi, near field communication (NFC), and/or the like). The communication device 128 can be configured to send information with a wired or wireless communication device. In some embodiments, the communication device 128 may be optional.

In some implementations, the communication device 128 can send information to a user and/or a compute device controlled by a user regarding the ICD 110 such as battery level, information regarding therapies delivered to the heart H of the patient P, device status, and/or the like. The communication device 128 can also send information regarding the patient P, such as real-time or substantially real-time information, data, and/or other signals associated with the heart H (e.g., data from the first sensor 144a and/or the second sensor 144b, and/or any other suitable data). In some embodiments, the communication device 128 can receive signals for augmenting and/or at least partially controlling the operation of the ICD 110. For example, the communication device 128 can receive signals (e.g., from a controller or external compute device) associated with patient information and/or other operational instructions for determining when the ICD generator 120 generates a therapy, power levels associated with therapy, pacing and/or other thresholds for anti-tachycardia therapy, and/or the like. In some embodiments, the communication device 128 can be configured to send information and/or signals to an external compute device and/or server that is/are configured to perform machine learning processes. The communication device 128 can also receive information from the external compute device and/or the server that may include instructions, firmware, updates, etc. based on an output from a machine learning model. An example of an external compute device is described in further detail in reference to FIG. 5.

The ICD lead 140 is operatively coupled to the ICD generator 120 via the lead interface 130. The lead interface 130 is configured to couple (e.g., physically couple or at least operably couple) the ICD generator 120 to the ICD lead 140 to allow data signals and/or electric power (e.g., for therapy) to be transferred therebetween. In some embodiments, the lead interface 130 can be configured to format data signals and/or electric power transferred between the ICD generator 120 and the ICD lead 140 into any suitable format allowing, for example, the processing of sensor data or the like by the ICD generator 120 and/or the application of therapy by the ICD lead 140. The lead interface 130 may be a flexible interface to provide flexibility for the continuous movement of the heart and allowing the ICD lead 140 to maintain contact with the heart.

In some embodiments, the ICD lead 140 can extend away from the ICD generator 120 via a conduit that includes a signal/power carrying wire. The ICD lead 140 is configured to deliver treatment to the patient P and/or the heart H of the patient P. As shown in FIG. 1, the ICD lead 140 includes, is operably coupled to, and/or is in communication with the first sensor 144a and the second sensor 144b via a sensor interface 142. For example, the sensors 144a and/or 144b can be included and/or coupled along one or more portions of the ICD lead 140. The sensor interface 142 is configured to communicate with the sensors 144a, 144b to send and/or receive signals between the sensors 144a and 144b, and the ICD lead 140, which in turn is in communication with the ICD generator 120. Alternatively, in some embodiments, the sensors 144a and 144b can be in communication with the lead interface 130 and/or otherwise can be directly in communication with the ICD generator 120. Additionally, in some embodiments, the sensor interface 142 provides the first sensor 144a and the second sensor 144b with power. In some embodiments, the sensor interface 142 can preprocess signals output by and/or received from the first sensor 144a and the second sensor 144b. In some embodiments, the outputs of the sensor interface 142 and/or the first sensor 144a and/or the second sensor 144b can be calibrated based on the patient P, the position in the body of the patient P, and/or the like. For example, the first sensor 144a and/or the second sensor 144b can be calibrated based on one or more known measurement(s) of patient characteristic(s). Calibration can include calibrating the output of the sensors 144a and 144b to align with known measurements of patient characteristics. For example, a cardiac electrical signal sensor used to measure, for example, cardiac rate can be calibrated based on comparing and adjusting the sensor output to an external cardiac rate measuring device.

The ICD lead 140 can include shocking elements for delivering treatment (e.g., including energy from the generator) to the heart H of the patient P. In some embodiments, the ICD lead 140 is positioned substernal in the patient P. In some embodiments, the ICD lead 140 is positioned in contact with, adjacent to, and/or otherwise in close proximity to the fibrous pericardium of the heart H. In some embodiments, the ICD lead 140 can be positioned endocardial, epicardial, or under the sternum. In some embodiments, the ICD lead 140 can be designed and/or formed to use, traverse, and/or fill (or at least substantially use, traverse, and/or fill) at least a portion of the volume between the sternum and the pericardium of the heart H.

The first sensor 144a and the second sensor 144b are configured to measure different health characteristics associated with the heart of the patient P. In some embodiments, the first sensor 144a and the second sensor 144b can measure continuously, periodically, or when a command is received by the first sensor 144a and the second sensor 144b (e.g., received from the processor 122). In some embodiments, the first sensor 144a is configured to measure cardiac signals. In some embodiments, the first sensor 144a is configured to measure cardiac electrical signals (e.g., heart rate, voltage, P wave, QRS morphology, ST segment, T wave, ECG signals, etc.) In some embodiments, the first sensor 144a can be positioned so that it engages the heart H of the patient P and/or in or on the body of the patient P such that the cardiac signals can be measured (e.g., in contact with or in close proximity to the pericardium).

In some embodiments, the second sensor 144b is a different sensor than the first sensor 144a. In some embodiments, the second sensor 144b is configured to measure cardiac mechanical signal data. In some embodiments, the second sensor 144b is configured to measure hemodynamic status. In some embodiments, the second sensor 144b is a pressure sensor. For example, the second sensor 144b may be a pressure sensor configured to measure hemodynamic pressure. In some embodiments, the second sensor 144b is a pressure sensor configured to measure pressure or changes in pressure in the substernal space associated with cardiac movement. In some embodiments, the second sensor 144b is a pressure sensor in a hermetically sealed housing. In some embodiments, the second sensor 144b can be positioned so that it engages the heart H (e.g., the pericardium) of the patient P or can be positioned in any other place in or on the body of the patient P where the second sensor 144b can measure hemodynamic status. In some embodiments, the second sensor 144b is in the anterior mediastinum to measure hemodynamic status.

In some embodiments, the first sensor 144a and/or the second sensor 144b is/are located in the epicardium of the heart. In some embodiments, the first sensor 144a and/or the second sensor 144b is/are located within the heart. For example, the first sensor 144a and/or the second sensor 144b may be located in the right heart. In some embodiments, the first sensor 144a and the second sensor 144b can be collocated or substantially collocated. In some embodiments, the first sensor 144a and/or the second sensor 144b can be remotely or separately located. For example, in some embodiments, the first sensor 144a can be positioned in contact with or adjacent to the fibrous pericardium of the heart H, while the second sensor 144b can be positioned apart from the heart H in the substernal space. In some embodiments, the first sensor 144a and/or the second sensor 144b measures characteristics of the heart H and/or other portions or body parts of the patient P.

As described in further detail herein, the sensors 144a and 144b can measure, detect, and/or sense one or more characteristics associated with the heart H and can send data associated with those characteristics to the ICD generator 120 (e.g., directly or indirectly via the ICD lead 140). Upon receipt, the ICD generator 120 can analyze, process, aggregate, correlate, etc. the data to determine, for example, a cardiac status of the heart H. Furthermore, based on the determined and/or defined cardiac status, the ICD generator 120 can detect and/or determine the occurrence of health events, such as arrhythmia, tachycardia, and/or the like. In some implementations, the use of data from each of the first sensor 144a and the second sensor 144b can allow the ICD generator 120 to determine the cardiac status of the heart H with greater sensitivity and specificity than when determining cardiac status using cardiac signals, QRS complex morphology, and/or other cardiac electrical signal measurements alone (e.g., determined based on signals from the first sensor 144a), hemodynamic status measurements alone (e.g., determined based on signal from the second sensor 144b), and/or other cardiac characteristics individually.

In some implementations, the process of the ICD generator 120 determining the cardiac status and/or functioning of the heart can include, for example, comparing, correlating, and synchronizing the signal data received from the first sensor 144a and the second sensor 144b. For example, the first sensor 144a can be configured to determine and/or sense cardiac electrical signals and the second sensor 144b can be configured to determine and/or sense cardiac mechanical signals such as, for example, hemodynamic status and/or output. The ICD generator 120 can receive the signal data from the sensors 144a and 144b and can determine the cardiac status and further classify cardiac arrhythmia, based at least in part on comparing, correlating, and/or synchronizing a cardiac electrical signal curve (from the first sensor 144a) and a cardiac mechanical signal curve (from the second sensor 144b). Thus, determining a cardiac status and a classification of cardiac arrhythmia can include the ICD generator 120 determining whether a sensed or determined cardiac rhythm (e.g., based on cardiac electrical signal data received from the first sensor 144a) has an expected corresponding sensed or determined hemodynamic status and/or output (e.g., based on cardiac mechanical signal data received from the second sensor 144b). If a hemodynamic status and/or output is not what is expected, based on the determined cardiac rhythm), the ICD generator 120 may withhold, delay, and/or modify the ICD therapy. In some embodiments, one or more machine learning algorithms may be employed to define, determine, and/or correlate expected hemodynamic status(es) and/or output(s) for a given cardiac rhythm. In some embodiments, one or more machine learning algorithms may be employed to define, determine, correlate, and/or predict a cardiac status based at least in part on one or more relationships and/or a degree of correlation between, for example, data received from the first sensor 144a (e.g., cardiac electrical signal data such as, for example, cardiac rhythm) and data received from the second sensor 144b (e.g., cardiac mechanical signal data such as, for example, hemodynamic status and/or output).

FIG. 2 schematically depicts an ICD system 200 engaging with a patient P according to another embodiment. The ICD system 200 is structurally and/or functionally similar to the ICD system 100 of FIG. 1. The ICD system 200 includes an ICD 210 (e.g., functionally and/or structurally similar to the ICD 110 of FIG. 1) engaging with the heart H of the patient P. The ICD 210 includes an ICD generator 220 (e.g., structurally and/or functionally similar to the ICD generator 120 of FIG. 2) operatively coupled to an ICD lead 240 (e.g., structurally and/or functionally similar to the ICD lead 140 of FIG. 1. The ICD lead 240 includes a sensor interface 242 (e.g., structurally and/or functionally similar to the sensor interface 142 of FIG. 1).

The ICD 210 includes a first sensor 244a (e.g., structurally and/or functionally similar to the first sensor 144a) and a second sensor 244b (e.g., structurally and/or functionally similar to the second sensor 144b). In the embodiment shown in FIG. 2, the first sensor 244a and the second sensor 244b are integrated into the ICD lead 240. In some embodiments, such as depicted in FIG. 1, at least one of the first sensor 244a and/or the second sensor 244b are remote from the lead. The sensor interface 242 may be configured to physically couple the first sensor 244a and the second sensor 244b to the ICD lead 240. The sensor interface 242 may be configured to receive and send the outputs from the first sensor 244a and the second sensor 244b. In some embodiments, the sensor interface 242 may preprocess and/or synchronize the signals from the first sensor 244a and the second sensor 244b.

In some implementations, integrating the first sensor 244a and the second sensor 244b can allow for the sensors 244a and/or 244b to be positioned close to the heart H of the patient P. The first sensor 244a may be positioned along the ICD lead 240 such that cardiac signals can be measured. The second sensor 244b may be positioned along the ICD lead 240 such that hemodynamic status or output can be measured. In some embodiments, the first sensor 244a and/or the second sensor 244b are positioned along a region, portion, or section of the ICD lead 240. For example, the first sensor 244a and/or the second sensor 244b may be positioned along a distal section corresponding to a portion of the ICD lead 240 nearest or proximate (e.g., within 1%, within 2%, within 3%, within 4%, within 5%, within 10%, within 25%, etc.) the distal end of the ICD lead 240. In some embodiments, the first sensor 244a and/or the second sensor 244b is/are positioned at the distal end of the ICD lead 240. In some implementations, integrating the first sensor 244a and the second sensor 244b directly into the ICD lead 240 can potentially decrease the noise in the signals from the first sensor 244a and the second sensor 244b as the signal may have to travel a shorter distance and/or through fewer interfaces.

While the ICD system 200 is described above as including “the first sensor 244a” and “the second sensor 244b,” it should be understood that the first sensor 244a can be a single sensing device or multiple sensing devices that collectively function as the first sensor 244a, and similarly, the second sensor 244b can be a single sensing device or multiple sensing devices that collectively function as the second sensor 244b. In some implementations, multiple sensing devices can allow for sensor data that includes multiple signal vectors (e.g., multiple cardiac electrical and/or mechanical signal vectors). Similarly, while the ICD system 200 is described herein as including “the first sensor 244a” and “the second sensor 244b,” it should be understood that the ICD system 200 can include any number of additional sensors configured to sense and/or detect any suitable characteristic(s) associated with the patient (e.g., cardiac electrical and/or mechanical signals or any suitable non-cardiac signals). In some implementations, each sensor can be configured to sense and/or detect a different characteristic associated with the heart, or more generally, the patient. In some implementations, one or more sensors can be configured to sense or detect the same characteristic, thereby allowing for confirmation/verification of signal data and/or a desired degree of sensitivity and/or specificity in interpreting the signal data.

FIG. 3 is a flow chart depicting a method 300 for determining if a patient is having a health event and whether to apply a corresponding corrective action according to an embodiment. In some embodiments, the method 300 can be executed and/or performed by an ICD system that is functionally and/or structurally similar to the ICD system 100 of FIG. 1 and/or the ICD system 200 of FIG. 2. In some embodiments, the method 300 can be executed by one or more devices. For example, the method 300 can be executed by the ICD system 500 of FIG. 5. The method 300 is configured to use data associated with at least two characteristics of the heart or a function of the heart. In some implementations, the method 300 is configured to use at least two signals from at least two data sources (e.g., sensors) to determine if a patient is having a health event (e.g., cardiac arrest, tachycardia, arrhythmias). Using at least two signals reduces the likelihood that the ICD system determines a health event is occurring when compared to an ICD system that uses only one signal for determining if a health event is occurring. Additionally, the method 300 allows for determining the type of corrective action (e.g., treatment) to be determined based on the health event determined. This allows the ICD system to more accurately treat (e.g., with greater sensitivity and specificity) the health event and reduce the likelihood of sudden cardiac arrest. The method 300 may improve sensitivity and specificity of treatment for ventricular tachycardia or ventricular fibrillation.

The method 300 includes, at 301, receiving signal data from a first sensor (e.g., functionally and/or structurally similar to the first sensor 144a of FIG. 1 and/or the first sensor 244a of FIG. 2) and second signal data from a second sensor (e.g., structurally and/or functionally similar to the second sensor 144b of FIG. 1 and/or the second sensor 244b of FIG. 2). The first signal data and the second signal data are associated with at least one characteristic of a heart of a patient. In some embodiments, the first sensor is a sensor configured to measure or sense cardiac electrical signals and the first signal data is associated with, for example, cardiac signals, QRS complex morphology, and/or the like. In some embodiments, the second sensor is a sensor configured to measure cardiac mechanical signals (e.g., hemodynamic status) and the second signal data is associated with cardiac mechanical signals (e.g., hemodynamic status data or a proxy or derivative thereof) and/or output of the heart of the patient. In some embodiments, 301 may include receiving additional signal data from any number of additional sensors and/or additional data sources (e.g., one or more ex vivo data sources). In some embodiments, 301 may include receiving at least one of the first signal data and/or the second signal data from an external data source (e.g., patient database, etc.).

At 302, the method 300 optionally includes preprocessing the first signal data and/or the second signal data. Preprocessing can include noise reduction, filtering, amplification, normalization, conversion (e.g., analog-to-digital, etc.), synchronization, and/or the like. Preprocessing can prepare for the signals to be received by another component of the ICD system. For example, a sensor interface (e.g., functionally and/or structurally similar to the sensor interface 142 of FIG. 1 and/or the sensor interface 242 of FIG. 2) can preprocess the first signal data and the second signal data so that the first signal data and the second signal data can be received by an ICD generator (e.g., structurally and/or functionally similar to the ICD generator 120 of FIG. 1 and/or the ICD generator 220 of FIG. 2) and/or a compute device (e.g., functionally and/or structurally similar to the compute device 550 of FIG. 5). In some embodiments, the compute device and/or the ICD generator may preprocess the first signal data and/or the second signal data to prepare the data for processing.

At 303, the method 300 includes determining a cardiac status based at least in part on the first signal data and the second data signal. The cardiac status can correspond to the status of the heart (e.g., heartbeat profile, cardiac activity, etc.). In some embodiments, determining a cardiac status can further be based on data from additional source (e.g., additional sensors and/or one more ex vivo data sources). Determining cardiac status can include, for example, comparing and synchronizing the signal data and artifacts in the signal data to determine the cardiac status or functioning of the heart. For example, when the first signal data is associated with cardiac electrical signals (e.g., cardiac signals or used to determine cardiac rate) and the second signal data is associated with hemodynamic status and/or output, determining cardiac status can include synchronizing an electrical signal curve (from the first signal data) and a pressure curve (from the second signal data) to correlate the signal data. As another example, determining the cardiac status can include determining if a rhythm seen in the cardiac electrical signal data has an expected corresponding result in the hemodynamic status and/or output signal data. As such, using the first signal data and the second signal data provides a more sensitivity and specificity in determining cardiac status than when determining cardiac status using just one source of data (e.g., cardiac electrical signals alone).

At 304, the method 300 optionally includes refining the cardiac status using a machine learning model based on the first signal data and the second signal data. Refining can include, for example, improving, tuning, updating, and/or verifying the cardiac status determined at 303. The machine learning model can be trained based on cardiac status associated with data form the first sensor and the second sensor. For example, the machine learning model can be trained based on cardiac status associated with cardiac electrical signal data (e.g., cardia rate) and hemodynamic status and/or output data from the patient, historical data, data from studies, medical professional input, and/or the like. In some embodiments, the machine learning model can be trained based on additional patient data (e.g., activity level, height, weight, age, and/or the like). Training on patient data allows for the machine learning model to gain further insight on the patient that may affect the characteristics of the heart measured by the sensors.

In some embodiments, the cardiac status may be changed, updated, tuned, etc., based at least in part on comparing the cardiac status from 303 to more closely align with empirical data, a status determination by one or more trained cardiologists, updated clinical evidence, and/or any suitable known or accepted ground truth. In some embodiments, the machine learning model may be configured to recognize long-term changes in cardiac status and/or the first signal data and/or the second signal data and adapt to these changes. For example, the machine learning model may recognize that hemodynamic output has been decreasing over weeks, months, or years. Recognizing changes allows the machine learning model to adapt the ICD treatment along with the patient as the patient changes. In some embodiments, the machine learning model may receive feedback data and/or training sets that are used to further train the machine learning model.

At 305, the method 300 includes determining, based on the cardiac status, if a health event is occurring. Determining if a health event is occurring can include, in some embodiments, comparing the cardiac status to an expected cardiac status. For example, if the cardiac status indicates that a heartbeat is higher than it expected, the presence of a health event may be determined. In some embodiments, a machine learning model can be used to determine if a health event is present based at least in part on the cardiac status. For example, the machine learning model can recognize changes in the patient's heart activity that can indicate the presence of a health event, or if no health event is occurring. In some embodiments, an indication that a health event is occurring may be sent to the user. In some embodiments, the machine learning model can recognize when the patient's heart activity is expected and/or predicted to increase. For example, when the patient is exercising, experiencing stress or anxiety, and/or the like.

At 306, the method 300 includes a decision of whether a health event has been detected. If a health event has not been detected, the method 300 returns to 301 to continue monitoring the heart of the patient. If a health event has been detected, the method 300 continues to 307.

At 307, the method 300 optionally includes determining a type of health event. In some embodiments, determining a type of health event can include comparing and matching the cardiac status to known health events. In some embodiments, a machine learning model may be used to match the cardiac status to a health event. In some embodiments, the type of health event can be sent to a user device and/or the like for review by the user. For example, the type of health event can be reviewed by the patient, a medical proxy, a medical professional, and/or the like. At 308, the method 300 optionally includes determining a treatment based on the type of health event determined in 307. The treatment can correspond to the type of health event and/or can be adjusted based on information about the patient and/or the characteristics of the heart (e.g., heartrate, blood pressure, arrhythmia classification, cardiac status, hemodynamic status, etc.) at the time of or leading up to the health event. Determining the treatment based on the type of health event allows for a more accurate treatment for the given health event than when a predetermined treatment is used for any/all detected health events. For example, if, at 307, the health event is determined and/or predicted to be ventricular tachycardia, the ICD treatment may be anti-tachycardia pacing. On the other hand, if the health event is determined and/or predicted to be ventricular fibrillation, the ICD treatment may be shock therapy (e.g., defibrillation shock(s)). In some embodiments, 308 may include determining if the health event is still occurring. If the health event is determined to no longer be occurring, 308 may include determining that a treatment is not desired. Said another way, step 308 can include delaying potential treatment to confirm that the health event is still occurring, which in some instances, may reduce what may otherwise be an “appropriate” shock therapy.

At 309, the method 300 includes applying the treatment to the heart of the patient. The treatment, either predetermined or as determined in 308, is generated by the ICD generator and delivered to the heart via the ICD lead. After the treatment is delivered to the heart, the method 300 return to 301 to continue monitoring the cardiac activity of the heart of the patient. The method 300 repeats as long as the ICD is powered and/or the ICD is implanted in the patient.

While the method 300 is described above as receiving first signal data from the first sensor and second signal data from the second sensor, it should be understood that the first sensor can be a single sensing device or multiple sensing devices that collectively function as the first sensor, and similarly, the second sensor can be a single sensing device or multiple sensing devices that collectively function as the second sensor. In some implementations, multiple sensing devices can allow for sensor data that includes multiple signal vectors (e.g., multiple cardiac electrical and/or mechanical signal vectors).

In some implementations, the method 300 may optionally include receiving signal data from one or more additional sensors (e.g., in addition to the first sensor and the second sensor). In some implementations, each sensor can be configured to sense and/or detect a different characteristic associated with the heart, or more generally, the patient. In some implementations, one or more sensors can be configured to sense or detect the same characteristic, thereby allowing for confirmation/verification of signal data and/or a desired degree of sensitivity and/or specificity in interpreting the signal data. For example, in some implementations, the step at 301 can include receiving signal data from any number of sensors or data sources included in or in communication with the ICD; the optional step at 302 may include preprocessing the signal data from each of the sensors; the step at 303 may include determining the cardiac status based at least in part on each or all of the signal data; and the optional step at 304 may include refining the cardiac status using a machine learning model based on each or all of the signal data.

FIG. 4 depicts a placement of an ICD lead 440 in the chest of a patient according to an embodiment. In some embodiments, the ICD lead 440 is structurally and/or functionally similar the ICD lead 140 of FIG. 1 and/or the ICD lead 240 of FIG. 2. The ICD lead 440 is coupled to an ICD generator (e.g., structurally and/or functionally similar to the ICD generator 120 of FIG. 1 and/or the ICD generator 220 of FIG. 2) to form an ICD (e.g., structurally and/or functionally similar to the ICD 110 of FIG. 1 and/or the ICD 210 of FIG. 2). The position of the ICD lead 440 can be a parameter and/or factor in reducing anti-tachycardia pacing (ATP) thresholds. ATP thresholds that are higher than desired could result in inconsistent pacing, intolerable sensations to the patient during pacing, or non-use of pacing that increases probability of undesirable health events and/or undesirable shocks.

As seen in FIG. 4, the ICD lead 440 is positioned in the substernal space between a sternum S and a pericardium of a heart H of a patient P. The ICD lead 440 is positioned in the substernal space since any space between the electrode and the pericardium can increase pacing thresholds. Furthermore, the ICD lead 440 is configured to facilitate the natural movement of the heart H that occurs with each cardiac cycle. In some embodiments, the ICD lead 440 can facilitate natural movement by using the sternum S as a base and having the ICD lead 440 pressed against or otherwise placed in contact with the fibrous layer of the pericardium. This position allows for the ICD lead 440 to move in multiple directions and absorb and/or move with the motion of the heart H.

FIG. 5 depicts an ICD 510 (e.g., functionally and/or structurally similar to the ICD 110 of FIG. 1 and/or the ICD 210 of FIG. 2) in communication with an external compute device 550 via network(s) 505 according to an embodiment. The network(s) 505 can include one or more network(s) that may be any type of network (e.g., a local area network (LAN), a wide area network (WAN), a virtual network, a telecommunications network) implemented as a wired network and/or wireless network (e.g., Wi-Fi, Bluetooth®, Bluetooth® low energy, Zigbee, etc.) and used to operatively couple to any compute device, including, for example, the compute device 550 and/or the ICD 510. In some embodiments, the components of the ICD 510 can be configured to communicate directly with the network(s) 505. For example, the ICD generator 520 (e.g., structurally and/or functionally similar to the ICD generator 120 of FIG. 1 and/or the ICD generator 220 of FIG. 2) may send and receive information associated with cardiac status, health events, and/or treatment to the compute device 550 (or any other suitable device) via the network(s) 505. As another example, the ICD lead 540 (e.g., structurally and/or functionally similar to the ICD lead 140 of FIG. 1 and/or the ICD lead 240 of FIG. 2) may send and/or receive processed signal data to compute device 550 (or any other suitable device) via the network(s) 505. As another example, the sensor(s) 544 (e.g., structurally and/or functionally similar to the first sensor 144a of FIG. 1, the second sensor 144b of FIG. 1, the first sensor 244a of FIG. 1, and/or the second sensor 244b of FIG. 2) can send and receive information associated with the signals and/or the operation of the sensor(s) 544.

The compute device 550 is an external device configured to receive and send signals, data, and/or information from/to the ICD 510 via the network(s) 505. The compute device 550 may be any suitable device or combination of devices. For example, the compute device 550 can be any suitable electronic device configured to send, receive, process, analyze, store, use, change, define, etc. data, data structures, and/or the like. The components of the compute device(s) can be contained within a single housing or machine or can be distributed within and/or between multiple physical machines, virtual machines, and/or any combination thereof. In some embodiments, the compute device(s) 550 can be physically included in and/or on local machine(s) or device(s) or can be stored, run, executed, and/or otherwise implemented in and/or on remote machine(s) or device(s). For example, the compute device