Invented by Rosana Esteller, Felice Sun, NeuroPace Inc

The market for methods, systems, and methods for automatically identifying detection parameters of an implantable medical device is rapidly growing. With the advancements in technology, implantable medical devices have become more sophisticated, and the need for accurate and reliable detection parameters has become more critical. Implantable medical devices such as pacemakers, defibrillators, and neurostimulators are used to treat a variety of medical conditions. These devices are designed to monitor the patient’s health and provide appropriate therapy when necessary. However, the effectiveness of these devices depends on the accuracy of the detection parameters. Traditionally, detection parameters were manually set by the healthcare provider during the implantation procedure. However, this method is time-consuming and can lead to errors. With the development of automated detection parameter identification systems, the process has become more efficient and accurate. The market for these systems is driven by the increasing prevalence of chronic diseases such as cardiovascular diseases, neurological disorders, and diabetes. The aging population is also a significant factor contributing to the growth of this market. As the number of elderly patients with chronic diseases increases, the demand for implantable medical devices also increases, leading to a higher demand for accurate detection parameters. The market for these systems is also driven by the increasing adoption of electronic health records (EHRs) and telemedicine. These technologies allow healthcare providers to remotely monitor patients with implantable medical devices, making it essential to have accurate detection parameters. The market for these systems is segmented based on the type of implantable medical device, technology, and geography. The major players in this market include Medtronic, Abbott Laboratories, Boston Scientific Corporation, and St. Jude Medical. In conclusion, the market for methods, systems, and methods for automatically identifying detection parameters of an implantable medical device is growing rapidly. The increasing prevalence of chronic diseases, the aging population, and the adoption of EHRs and telemedicine are the major factors driving the growth of this market. With the advancements in technology, the accuracy and reliability of detection parameters are expected to improve, leading to better patient outcomes.

The NeuroPace Inc invention works as follows

An initial parameter set for one or more detection devices is automatically determined and then adjusted to make each detection device more or less sensitive in terms of signal characteristics within a particular region of interest. The detection tool(s), which are based on physiological signals (such as EEG), can be programmed to run within an implanted device. It will then look for rhythmic, spike, or power changes. The user can select a detection tool and then have parameter values calculated in a logical order and/or pairs, based on an activity type graphic that is selected via GUI. The simulations displayed allow the user to see what will be detected by a parameter set derived from the graphic. They can then adjust the sensitivity or restart the process as desired.

Background for Methods, systems and methods for automatically identifying detection parameters of an implantable medical device.

Systems and methodologies that use algorithms to identify when physiological data from patients show certain features or correspond with certain physiological states are useful in diagnosing, monitoring, and treating patients. The patient’s doctor may not be able to determine the parameters that will allow the algorithm to function as expected and produce the desired outcome. These systems and methods could be made more intuitive for physicians to use in relation to particular patients or groups of patients.

Implantable medical devices are being investigated that monitor electrographic signals from patients and record portions of them (or some digital representation thereof) when they exhibit certain characteristics. This could be when certain conditions are met and/or when certain thresholds or levels are exceeded. These medical systems could have an implantable component that can determine the characteristics of the monitored signals. A component implantable to the medical system may also be able to determine if the monitored signals correspond with a specific physiological state. This could be based on the condition of the implantable medical devices (e.g. when amplifiers or amps reach saturation, or how frequently they reach saturation). Diagnostics refers to the conditions of an implantable medical devices, which are different from the physiological signals that the implantable medical devices may be configured to monitor. or ?device diagnostics?. One or more diagnostics can be used to determine the physiological state of a patient. The patient could be considered to have experienced an electrographic event, such as a seizure, if certain amplifiers within the implanted medical devices become saturated.

An implantable component of an implantable device system that can process signals and run one or more algorithms on data is generally referred to as an “active implantable medical devices.” It can be distinguished from, for instance, a passive implantable part such as a catheter.

One or more algorithms that are implemented by the implantable part may be used to determine when characteristics are displayed in the monitored signals. Also, one or more diagnostics of the device should be used to detect a condition or condition that should be noted or taken into consideration. Each algorithm can operate on one or several channels of patient data or one or more diagnostics. A algorithm can be called a “detection tool”, a “detector”, or a “event detector”.

A?detection channel? may be defined as a set of sensors that the implant uses and the associated algorithms. This is for data obtained from patients. One or more event detectors may be associated with a given detection channel. A given channel may have one or more event detectors.

It is important to consider the power required to run an algorithm when it is being implemented primarily by an implantable part using an implanted power source (e.g., primary cell batteries or rechargeable batteries). “Algorithms are often designed and/or chosen based on how much power they will be used to run them.

NeuroPace, Inc. developed a responsive Neurostimulation System manufactured under the trademark RNS SYSTEM. This system includes a neurostimulator implantable component. The RNS SYSTEM Neurostimulator, as the name suggests, is capable of delivering electrical stimulation to a patient. However, this neurostimulator can also process signals from the patient or device diagnostics. This data may then be used by implant to determine if one of several actions is to be taken.

The RNS SYSTEM neurostimulator can be implanted through a hole in the cranial bone of a patient (sometimes called a “defect”). The cranium. Alternately, the neurostimulator part of an implantable responsive nervestimulation system can be placed elsewhere in the patient’s body, such as between cranium, scalp, or pectoral. One or more leads can connect to the neurostimulator to multiple electrodes that are implanted in the brain of the patient. For example, one lead may be connected to the neurostimulator and four electrodes may be attached to the distal end.

The neurostimulator can be configured to sense the electrographic signals from patients at a predetermined sampling speed and to receive them on one or more channels. The neurostimulator, or another component of the neurostimulation device, may filter or condition the signals (e.g. amplify and/or digitize). One or more algorithms, tools, or detectors may operate on the signals from each channel to identify patterns in the data. This could be used to detect characteristics (e.g. characteristics that correspond to an electrographic onset epileptic seizure). The RNS SYSTEM (and other neurostimulators), power is provided by an implant battery. Therefore, the algorithms, tools, and detectors used to process the data are chosen to use as little power as possible. They may also be classified as low computational complexity (or?LCC?). algorithms.

The RNS SYSTEM includes a half-wave detector, a linear length detector, and an area detector. The half-wave detector can be described as a tool for waveform morphology and the area and line detectors as “signal change” detectors. detectors.

A half wave detector can be configured to produce an output when the power of part of a signal is within a certain frequency range. Below, we will describe in detail how a half-wave detector can be configured to work in the context a responsive neurostimulation (or another diagnostic implantable medical devices system). It is important to note that a half-wave detector may be interested in the frequency content of the signal but it operates in the domain of time rather than the domain or frequency. “For this reason at least, the half-wave detector is considered an LCC algorithm in comparison to one that involves a transformation into the frequency domain such as FFTs.

The line-length detector can be used to determine how the frequency or amplitude of part of a signal in a certain time window varies compared to a longer-term trend of line length for the signal. Line length detectors are sometimes described as a simplified version of the fractal dimensions of a waveform. The line length detector’s result is intended to approximate the power of a signal in relation to a trend. Line length detectors are used to detect, for example. When a part of a signal within a time window deviates from the trend, it may exhibit a change of frequency, amplitude, or both. A change in amplitude, or frequency, suggests that something is different in the patient. For example, a rise in power could indicate the beginning of an electrographic seizure.

An area detector’s objective is to produce an output that corresponds with how much the integral of a signal (or the area under a curvature) varies within a specific time window relative to a long-term trend in the area of that signal. The area detector can be referred to by some as a representation for the energy in a waveform. The area detector works in a similar way to the line-length detector. It is designed to detect conditions where the signal deviates significantly from a long-term trend, indicating that something is wrong or abnormal.

Each of these LCC algorithms, the line length detector or the area detector, is considered to be one algorithm. Each requires very little power relative to the other more complicated algorithms that would give a measure of how much the waveform is changing in terms of energy or amplitude.

The half-wave detector, line length detector and area detector may all be considered algorithms of low complexity. However, a number of parameters and types of parameters are needed to ensure that each algorithm produces the best possible result.

A system can be configured to control the operation of an algorithm (e.g. what physiological data it will detect) can be programmed by the user. The ‘user’ is usually a physician diagnosing or treating the patient for whom the implantable medical device has been placed. In most cases, the?user? will be a physician who diagnoses or treats the patient for whom the active implantable medical devices are implanted. The system may also be set up so that one or more programmable parameters is defaulted to. A system may also be configured so that one or more of the programmable parameters is set to “default?

For engineers, scientists or anyone else interested in the operation of algorithms at a more detailed level, it may be easy to identify and specify the parameters for a tool. The typical user, such as a busy neurosurgeon or neurologist with multiple patients, may not be able to fully understand the parameters and how they relate to the patient’s condition. This user group may benefit from a system that allows them to choose what to see graphically, either on a display of the implant or a database and then automatically calculates the parameters for each tool based on their selections.

The half wave tool (or half-wave detector) can help you understand the complexity of choosing parameters and values for a tool. Half wave tools may need at least seven parameters. Other parameters may also be required for optimal performance. Here is an example of a semi-wave detector. There are seven parameters that can be specified. Each parameter can be associated with one to more than 1000 discrete values.

It will be appreciated that the user does not need to know what the “qualified analysis window” count is. A user might find it easier to see a feature or pattern in a waveform within the graphical content from a signal sensed by a patient. Then, the user can select the tool they want to use to search for it. This feature would allow a system to detect graphical patterns. A system with such a?graphical detection? feature would allow the user to set up the system to detect something, (e.g., a pattern in an electrographic sign) without having to understand how each parameter and associated value will impact the operation or outcome. A user might be able to see a display of an EEG signal and determine that there are areas of high rhythmic activity. He or she may then want to set up one or more detection tools that will detect the same pattern whenever it occurs. This may be possible for a user who doesn’t need to know what the detection parameters are or how they relate to the pattern they want to detect.

In some neurostimulation system, there may be at least three detection tools, or algorithms, that can be configured by the user. These include a half wave detector and a line length detector. Each algorithm is associated with a set possible operating parameters.

The neurostimulator can also be set up to record signals from the patient, or digital representations of them, and to record device diagnostics. This is information about the state of the neurostimulator at specific times (e.g. whether or not an amplifier goes into or out of saturation when it receives an input signal). One or more device diagnostics can be used as a proxy to what is going on with the patient (e.g., a seizure).

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