Invented by Felice Sun, Rosana Esteller, Adam Lee, NeuroPace Inc
Implantable medical devices are designed to monitor and regulate the functions of the body. They are implanted under the skin and are connected to the heart, brain, or other organs. These devices are equipped with sensors that detect changes in the body’s functions and send signals to the device to adjust the therapy accordingly.
The detection parameters of these devices are critical to their proper functioning. These parameters include the threshold levels for sensing and pacing, the sensitivity of the sensors, and the duration of the therapy. These parameters need to be adjusted based on the patient’s condition and can change over time.
Traditionally, the detection parameters of implantable medical devices are adjusted manually by a healthcare professional. This process can be time-consuming, and there is a risk of human error. Moreover, the manual adjustment of these parameters can be uncomfortable for the patient.
To overcome these challenges, there is a growing market for methods, systems, and methods for automatically identifying detection parameters of an implantable medical device. These systems use advanced algorithms and machine learning techniques to analyze the data collected by the device and adjust the detection parameters accordingly.
One example of such a system is the Medtronic CareLink system. This system allows healthcare professionals to remotely monitor and adjust the detection parameters of Medtronic’s implantable medical devices. The system uses advanced algorithms to analyze the data collected by the device and provides recommendations for adjusting the detection parameters.
Another example is the Boston Scientific Precision Spectra system. This system uses machine learning techniques to analyze the data collected by the device and automatically adjust the detection parameters. The system can also detect changes in the patient’s condition and adjust the therapy accordingly.
The market for methods, systems, and methods for automatically identifying detection parameters of an implantable medical device is expected to grow significantly in the coming years. The increasing demand for implantable medical devices and the need for efficient and accurate methods for adjusting the detection parameters are driving this growth.
In conclusion, the market for methods, systems, and methods for automatically identifying detection parameters of an implantable medical device is a rapidly growing market. These systems use advanced algorithms and machine learning techniques to analyze the data collected by the device and adjust the detection parameters accordingly. The increasing demand for implantable medical devices and the need for efficient and accurate methods for adjusting the detection parameters are driving this growth.
The NeuroPace Inc invention works as follows
An initial set is generated to operate one or more detection tools. These parameters can then be adjusted in order that each tool is more sensitive to signals in the area of interest. The detection tool(s), which can be used to detect physiological signals from patients (such as EEG signals), may be programmed to run in an implanted device with the parameters to monitor for rhythmic activity, spiking and power changes in the sensed signal. The parameter values can be derived using a logic sequence or in pairs. A graphical representation of the activity type may be used to select a detection tool. Users may click and drag on the graphic via a GUI. A user can view simulations to determine what will be detected using a derived parameter list and adjust the sensitivity or restart as needed.
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. investigates a responsive neurostimulation device manufactured under the trademark ‘RNS SYSTEM?” It includes an implantable component that contains a neurostimulator. The implantable component includes a neurostimulator.
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.
In the RNS SYSTEM the algorithms or tools consist of a half wave detector (a line length detector) and an area detector (an area detector). The half wave detector can be described as a waveform analysis tool. Line length and area detectors may also be used to reduce data or to increase power. detectors.
A half-wave detector might be designed to produce an output when a certain frequency range falls within the power spectrum of a signal. Below are some examples of how a half-wave detector might be used in a responsive neurostimulation or other diagnostic implantable medical system system. It is important to note that even though half-wave detectors are concerned with frequency content, they operate in the time domain and not the frequency domain. The half wave detector can be considered an LCC algorithm, relative to one that uses transformations into frequency domain such as FFTs or fast Fourier transforms (or FFTs) for at least this reason.
The line length detector’s objective may be to produce an output that corresponds with how much frequency or amplitude of a part of a signal is changing relative to, for instance, a long-term line length trend for that particular signal. Sometimes, the line length detector can be described as an approximate simplification of a waveform’s fractal dimension. The line length detector’s result is an approximation in power relative to a trend. The line length detector can detect, for example, a change in frequency or amplitude swings. When a part of a signal within a time window is not in line with the trend and shows a change of frequency, amplitude swings, or both, it indicates that something is wrong. For example, if the power goes up, the line length detector might detect an onset or precursor to an electrical seizure.
The area detector’s objective is to produce an output that indicates how much the integral (or area below a curve), of a signal is changing within a given time window relative to, for instance, a long-term area trend for that signal. An area detector can also be called a representation of waveform energy. The area detector works in the same way as the line length detector. It detects signals that are significantly different from the long-term trend and indicates 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.
Even though half wave detectors, line length detectors and area detectors are all considered to be relatively simple algorithms, there may still be significant numbers and types of parameters that must be specified to ensure the best possible results.
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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