Patent Description:
Epilepsy is the most common syndrome among the chronic neurological diseases. Approximately <NUM> million people worldwide suffer from epilepsy, and nearly <NUM>% of them are still unable to effectively control seizures with the current anti-epileptic drugs and require non-drug adjuvant therapy. Although epilepsy surgery is effective, there are still patients who are not suitable for treatment by brain resection and can only choose neuromodulation therapy to reduce occurrences of severe seizures. Both the conventional epilepsy surgery and the new neuromodulation therapy require accurate analysis and judgement of the brainwaves during a seizure to determine the brain region of the seizure or to let an implanter of the neuromodulation therapy determine onset of the seizure.

The duration of an epileptic seizure can be roughly divided into the following phases according to pathophysiological characteristics and features of the brainwave signals, including inter-ictal, pre-ictal, irregular phase, and bursting phase. The latter three stages belong to the ictal phase of the epileptic seizure. Recent studies have indicated that outputting a corresponding electrical stimulus signal during a particular phase has great influence on the effect of neuromodulation. However, the most recent studies have indicated that the stimulation is not equally effective when provided during other phases of the seizure. An electrical stimulation that lasts a long period of time will increase tolerance in the patient, therefore gradually decreasing the effectiveness of the electrical stimulation on the patient. Moreover, the electrical stimulation is very energy intensive. Therefore, how to give a corresponding electrical stimulation after detecting the epileptic seizure and determining the particular phase remains a huge issue for the artificial intelligence neural stimulator. Attention is drawn to <CIT> describing an implantable neurostimulator adapted to provide adaptive electrical brain stimulation which includes a detection subsystem for isolating an electrographic signal characteristic and a stimulation system for applying an adaptive stimulation signal based at least in part upon the electrographic signal characteristic and correlated with the electrographic signal. Undesired learning of and acclimation to stimulation characteristics are avoided and stimulation efficacy is improved by adapting or otherwise varying the adaptive stimulation signal in relation to the electrographic signal. Attention is further drawn to <CIT> describing a method and apparatus for preventing or terminating seizures, by stimulating a brain with at least two implanted electrodes, each implanted in a different one of at least two regions of the brain, with a frequency to emulate neuronal synchrony. Upon detecting a potential or actual seizure occurrence, the frequency is electrically applied to the brain upon the detection to preempt or terminate the potential or actual seizure occurrence. Parts in the brain, where brain electrical activity is being measured, that have the highest connectivity are determined by phase-locking coherence. The select subset of areas showing synchrony, i.e., only those with the highest coherence that will best permit inducing a large global synchrony, are stimulated to preempt or terminate the potential or actual seizure occurrence.

This disclosure provides a method for generating stimulation parameters, an electrical stimulation control apparatus, and an electrical stimulation system, which is able to generate corresponding stimulation parameters according to a received brainwave signal.

The method for generating the stimulation parameters of the disclosure includes the following steps. A brainwave signal is sensed. The brainwave signal is decomposed to obtain a first sub-signal and a second sub-signal, in which a frequency of the first sub-signal is higher than a frequency of the second sub-signal. The first sub-signal is analyzed to obtain an intrinsic frequency series, in which the intrinsic frequency series includes at least one frequency component. The second sub-signal is converted to a Boolean signal. The intrinsic frequency series and the Boolean signal, which serve as a set of stimulation parameters, are outputted to the stimulator, enabling the stimulator to generate a stimulus signal.

In an embodiment of the disclosure, the step of decomposing the brainwave signal to obtain the first sub-signal and the second sub-signal includes using an empirical mode decomposition (EMD) algorithm to decompose the brainwave signal into the first sub-signal and the second sub-signal.

In an embodiment of the disclosure, the step of analyzing the first sub-signal to obtain the intrinsic frequency series includes executing a spectrum analysis algorithm on the first sub-signal to obtain the intrinsic frequency series, in which the spectrum analysis algorithm is one of a Fourier transform algorithm, a Wavelet transform algorithm, a normalized direct quadrature algorithm and a normalized Hilbert transform algorithm.

In an embodiment of the disclosure, the step of converting the second sub-signal to the Boolean signal includes executing a binarization algorithm on the second sub-signal to obtain the Boolean signal.

In an embodiment of the disclosure, the execution of the binarization algorithm includes calculating a dominant frequency of the second sub-signal and generating the Boolean signal based on the dominant frequency.

In an embodiment of the disclosure, the method for generating the stimulation parameters further includes receiving the brainwave signal and a serial number corresponding to the brainwave signal, and recording the serial number in a specific series of a parameters table. The intrinsic frequency series and a frequency of the Boolean signal serve as a set of stimulation parameters and are recorded to a position in the parameters table corresponding to the serial number after the intrinsic frequency series and the Boolean signal are obtained.

In an embodiment of the disclosure, after the intrinsic frequency series and the frequency of the Boolean signal serving as the set of stimulation parameters are recorded to the position in the parameters table corresponding to the serial number, the method further includes sequentially inputting the set of stimulation parameters recorded in the parameters table to the stimulator based on the specific series, so as to generate the stimulus signal.

The electrical stimulation control apparatus of the disclosure includes the following components. A signal sensing circuit, which is configured to acquire a brainwave signal. A processor, which is coupled to the signal sensing circuit and configured to decompose the brainwave signal to obtain a first sub-signal and a second sub-signal, in which a frequency of the first sub-signal is higher than a frequency of the second sub-signal. The processor is also configured to analyze the first sub-signal to obtain an intrinsic frequency series, in which the intrinsic frequency series includes at least one frequency component. In addition, the processor is configured to convert the second sub-signal to a Boolean signal. A storage apparatus, which is coupled to the processor and configured to store the intrinsic frequency series and the Boolean signal, in which the processor sends the intrinsic frequency series and the Boolean signal to a stimulator, enabling the stimulator to generate a stimulus signal based on the intrinsic frequency series and the Boolean signal.

The electrical stimulation system of the disclosure includes the following components. A signal sensing circuit, which is configured to receive a brainwave signal. A processor, which is coupled to the signal sensing circuit and configured to decompose the brainwave signal to obtain a first sub-signal and a second sub-signal, in which a frequency of the first sub-signal is higher than a frequency of the second sub-signal. The processor is also configured to analyze the first sub-signal to obtain an intrinsic frequency series, in which the intrinsic frequency series includes at least one frequency component. In addition, the processor is configured to convert the second sub-signal to a Boolean signal. A storage apparatus, which is coupled to the processor and configured to store the intrinsic frequency series and the Boolean signal. A stimulator, which is coupled to the processor and configured to receive the intrinsic frequency series and the Boolean signal, and generate a stimulus signal based on the intrinsic frequency series and the Boolean signal.

Based on the above, the disclosure can generate the corresponding stimulation parameters according to the intrinsic frequency of the brainwave signal.

To make the above-mentioned features and advantages more comprehensible, several embodiments accompanied by drawings are described in detail as follows.

<FIG> is a block diagram of an electrical stimulation control apparatus according to an embodiment of the disclosure. With reference to <FIG>, an electrical stimulation control apparatus <NUM> includes at least a signal sensing circuit <NUM>, a processor <NUM> and a storage apparatus <NUM>. The processor <NUM> is coupled to the signal sensing circuit <NUM> and the storage apparatus <NUM>. The electrical stimulation control apparatus <NUM> may be an apparatus with computing capabilities such as a desktop computer, a notebook computer, or a smart phone.

The signal sensing circuit <NUM> may be an integrated circuit or a microchip. Here, a brainwave signal received by the signal sensing circuit <NUM> may be a signal of a particular phase, for example, a signal of a bursting phase. Here, for example, a cranial nerve signal in the brainwave signal at a particular phase (such as the bursting phase) during duration of an epileptic seizure may be identified through a detector (not shown), and the cranial nerve signal is subsequently sent to the electrical stimulation control apparatus <NUM>.

In general, calculation steps of a brainwave signal processing algorithm mainly include feature extraction and classification. After spike detection is performed, an interpretation of the epileptic seizure is converted according to its detection result. Acquired features are then inputted into a judgment model for classification judgment. The judgment model generally needs to be trained and built before it can be used, which is realized by methods such as an artificial neural network (ANN), a support vector machine (SVM), a linear classification model, a fuzzy logic model, or an AutoLeam system.

The processor <NUM> is, for example, a central processing unit (CPU), a physics processing unit (PPU), a programmable microprocessor, an embedded control chip, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), or other similar apparatuses.

The storage apparatus <NUM> is, for example, any type of fixed or removable random access memory (RAM), a read-only memory (ROM), a flash memory, a hard disk, or other similar apparatuses, or a combination of these apparatuses. Multiple program code snippets are stored in the storage apparatus <NUM>, and the code snippets are executed by the processor <NUM> after being installed, so as to implement a method for generating stimulation parameters as described in the following.

<FIG> is a flowchart of a method for generating stimulation parameters according to an embodiment of the disclosure. With reference to <FIG>, in step S205, the signal sensing circuit <NUM> senses the brainwave signal. Next, in step S210, the processor <NUM> decomposes the brainwave signal to obtain a first sub-signal and a second sub-signal. Here, a frequency of the first sub-signal is higher than a frequency of the second sub-signal. For example, an empirical mode decomposition (EMD) algorithm may be used to decompose the brainwave signal into the first sub-signal and the second sub-signal. The first sub-signal is, for example, a dominant frequency signal.

Subsequently, in step S215, the processor <NUM> analyzes the first sub-signal to obtain an intrinsic frequency series. The intrinsic frequency series includes at least one frequency component. In addition, in step S220, the processor <NUM> converts the second sub-signal to a Boolean signal. Here, a binarization algorithm is executed on the second sub-signal to obtain the Boolean signal, and then the Boolean signal serves as a switch signal.

In step S225, the processor <NUM> outputs the intrinsic frequency series and the Boolean signal, which serve as a set of stimulation parameters, to a stimulator, enabling the stimulator to generate a stimulus signal. For example, after obtaining the intrinsic frequency series and the Boolean signal, the processor <NUM> records the intrinsic frequency series and a frequency of the Boolean signal, which serve as the set of stimulation parameters, in a parameters table of the storage apparatus <NUM>. Subsequently, the stimulation parameters are sequentially outputted from the parameters table to the stimulator.

The following is an example to illustrate an electrical stimulation system. <FIG> is a block diagram of an electrical stimulation system according to an embodiment of the disclosure. With reference to <FIG>, an electrical stimulation system <NUM> includes the electrical stimulation control apparatus <NUM> and a stimulator <NUM>. In the electrical stimulation control apparatus <NUM> of the embodiment, a decomposition module <NUM>, a reconstruction module <NUM>, and a spectral analysis module <NUM> are included. The decomposition module <NUM>, the reconstruction module <NUM> and the spectral analysis module <NUM> are executed by the processor <NUM> to obtain stimulation parameters from a brainwave signal X(t). Here, the decomposition module <NUM> and the spectral analysis module <NUM> may generate corresponding electrical stimulation parameters for an intrinsic frequency of the brainwave signal X(t) through calculations, while the reconstruction module <NUM> may adjust the stimulus on and off according to a state of the particular phase. That is, the reconstruction module <NUM> serves as an activation apparatus for stimulation control.

The signal sensing circuit <NUM> receives the brainwave signal X(t) and a serial number "NO. <NUM>" corresponding to the brainwave signal X(t). The signal sensing circuit <NUM> sends the brainwave signal X(t) to the decomposition module <NUM>, and records the serial number "NO. <NUM>" in a specific series of a stimulation parameters table <NUM>.

After receiving the brainwave signal X(t), the decomposition module <NUM> performs a non-steady state decomposition of the brainwave signal X(t) to obtain a first sub-signal C1 and a second sub-signal C2. The first sub-signal C1 is sent to the spectral analysis module <NUM> and the second sub-signal C2 is sent to the reconstruction module <NUM>. The spectral analysis module <NUM> executes a spectrum analysis algorithm on the first sub-signal C1 to obtain an intrinsic frequency series E. The spectrum analysis algorithm is one of a Fourier transform algorithm, a Wavelet transform algorithm, a normalized direct quadrature algorithm and a normalized Hilbert transform algorithm. The intrinsic frequency series E includes at least one frequency component. In the embodiment, the intrinsic frequency series E includes two frequency components, namely <NUM> and <NUM>. Subsequently, the two frequency components included in the intrinsic frequency series E are outputted to a position in the stimulation parameters table <NUM> corresponding to the sequence number "NO.

The reconstruction module <NUM> performs a binarization algorithm on the second sub-signal C2 to obtain a Boolean signal D. For example, the reconstruction module <NUM> calculates a dominant frequency of the second sub-signal C2, and then generates the Boolean signal D based on the dominant frequency. The foregoing description is only an example, and is not limited thereto as any binarization algorithm that can convert the second sub-signal C2 to the Boolean signal D may be used. Here, the Boolean signal D serves as a switch signal. Assuming that the dominant frequency of the second sub-signal C2 is <NUM>, then the Boolean signal D with <NUM> cycles per second is generated, and each cycle includes two Boolean values (true and false). Here, the reconstruction module <NUM> serves as the activation apparatus for stimulation control, and the Boolean signal, which serves as the switch signal, is generated through the reconstruction module <NUM>. Subsequently, a frequency (serving as the ON/OFF (switch) frequency, <NUM>) of the Boolean signal D is outputted to the position corresponding to the serial number "NO. <NUM>" in the stimulation parameters table <NUM>.

In addition, the signal sensing circuit <NUM> may continue to receive another brainwave signal and a serial number "NO. <NUM>" corresponding to the another brainwave signal. The signal sensing circuit <NUM> performs the same processing on the brainwave signal with the serial number "NO. <NUM>" as for the brainwave signal X(t) with the serial number "NO. <NUM>", and outputs stimulation parameters (an ON/OFF frequency and frequency components) corresponding to the serial number "NO. <NUM>" to a position corresponding to the serial number "NO. <NUM>" in the stimulation parameter table <NUM>, and so on. Multiple sets of stimulation parameters may be recorded in the stimulation parameters table <NUM> according to the specific series.

Subsequently, the processor <NUM> sequentially outputs the corresponding set of stimulation parameters to the stimulator <NUM> based on the serial numbers recorded in the specific series, enabling the stimulator <NUM> to generate a stimulus signal F based on the stimulation parameters. That is, the stimulation parameters corresponding to the serial number "NO. <NUM>" are first outputted to the stimulator <NUM> to generate the stimulus signal F, and then the stimulation parameters corresponding to the serial number "NO. <NUM>" are outputted to the stimulator <NUM> to generate another stimulus signal, and so on, until the stimulation parameters corresponding to the last serial number in the specific series of the stimulation parameters table <NUM> are outputted to the stimulator <NUM> to generate a stimulus signal.

Claim 1:
A method for generating stimulation parameters, comprising:
sensing (S205) a brainwave signal (X(t));
decomposing (S210) the brainwave signal (X(t)) to obtain a first sub-signal (C1) and a second sub-signal (C2), wherein a frequency of the first sub-signal (C1) is higher than a frequency of the second sub-signal (C2);
analyzing (S215) the first sub-signal (C1) to obtain an intrinsic frequency series (E), wherein the intrinsic frequency series (E) comprises at least one frequency component;
converting (S220) the second sub-signal (C2) to a Boolean signal (D), wherein the Boolean signal (D) is serving as a switch signal for a stimulator (<NUM>); and
outputting (S225) the intrinsic frequency series (E) and the Boolean signal (D), which serve as a set of stimulation parameters, to the stimulator (<NUM>), enabling the stimulator (<NUM>) to generate a stimulus signal.