A multi-sensor warning system that analyzes environmental triggers and predicts the risk of neurological attacks by creating a location-based risk map.

Abstract

The invention relates to a system for monitoring environmental and behavioral factors that may trigger neurological seizures in individuals prone to epilepsy and similar neurological attacks, using multiple sensors; analyzing the obtained data together; performing a location-based risk assessment by correlating it with the user's location; and providing early warning to the user depending on the determined risk level.

Description

1 TARIFF ANALYZING ENVIRONMENTAL TRIGGERS AND LOCATION-BASED RISK A MULTIPLE SYSTEM THAT PREDICTS THE RISK OF NEUROLOGICAL ATTACKS BY CREATING A MAP OF THEM. SENSOR-BASED WARNING SYSTEM TECHNICAL FIELD The invention suggests that neurological seizures occur in individuals prone to epilepsy and similar neurological attacks. Multiple sensors of environmental and behavioral factors that can trigger its formation through monitoring, joint analysis of the obtained data, user Location-based risk assessment, correlated with the location. 10 Depending on how it is implemented and the determined risk level, early warning is given to the user. It relates to a system for providing. PREVIOUS TECHNIQUE The prediction of seizures occurring in epilepsy and similar neurological disorders is 15. detection, identification, or control of an attack after it occurs Different technical solutions are being developed for this purpose. A significant aspect of known systems... In this section, historical seizure data are used to predict seizure occurrence, and The user's physiological data is being utilized, and in some systems, the following occurs: After a seizure is detected, intervention is carried out for the user. 20 Predicting epileptic seizures in patent document number US10506988B2 In order to do this, past seizure data, physiological signals and some environmental factors are used. A probability-based model is constructed using the variables in question. The system includes seizure timing and physiological data, as well as information about future seizures. It is used in calculating the probability and takes into account 25 factors such as temperature, humidity and atmospheric pressure. Some environmental variables can also be included in the calculation. However, The subject approach is primarily statistical estimation using historical seizure data. It is based on the mechanism available in the user's environment. environmental triggers are detected in real time via multiple sensors. 30 It does not focus on evaluation. Patent document number US10220207B2 describes the detection of seizure occurrence. Preventing seizures by applying nerve stimulation following the procedure, or 2 The aim is to bring it under control. In such solutions, from the onset of the seizure... The detection and intervention procedures carried out afterwards come to the forefront. In systems with the known state of the art, environmental triggers and the user some limitations regarding the joint assessment of behavioral status It is located. In particular, the frequency and intensity of ambient light, sleep duration and 5 Changes in circadian rhythm, stress levels, physical activity, and by correlating fatigue levels and medication regimen The evaluation remains limited. In addition, the user's specific location... Triggering factors perceived in the environment or geographical location are related to that location. 10. associating and using this association to identify environments at risk for neurological attacks. Creating a location-based risk map showing the risks is common in known systems. It is not used as such. It depends on a single trigger parameter exceeding a predetermined limit. In approaches that generate direct warnings, however, a single environmental or behavioral factor... 15 the change does not represent a real risk of neurological attack for the user However, it is possible to trigger an alarm. This situation is a false alarm. it can increase the occurrence and cause unnecessary psychological stress on the user. It can bring. THE PURPOSE OF THE INVENTION 20 The aim of the invention is to treat neurological disorders in individuals prone to epilepsy and similar neurological seizures. Multiple sensors and environmental and behavioral factors that can trigger attack occurrence monitoring through tracking modules and the combined analysis of the obtained data The goal is to develop an early warning system that allows for evaluation. Another aim of the invention is to reduce the risk of developing 25 cases that depend on the identification of only a single triggering factor. Instead of generating alerts, it uses sleep and circadian rhythms, light intensity, and flicker. frequency, stress level, physical activity and fatigue status, and medication use pattern The aim is to enable the related data to be evaluated together in a central decision-making module. Another aim of the invention is to analyze parameters obtained from different sensors and modules. processing them together taking into account their respective weighting coefficients and the user's 30 Calculating a neurological attack risk score that represents the conditions in which the individual is situated. to provide. Another purpose of the invention is to classify risk as low, medium, or high according to the calculated risk score. determining the risk level and the predetermined threshold value of the risk level 3 If it exceeds this limit, the user will receive visual, auditory, or vibrational notifications. The aim is to stimulate them. Another aim of the invention is to enable the user to utilize environmental data obtained from the sensors. potential neurological attack triggers by associating them with location information 5. Determining the locations of these sites and representing these locations on a risk map The aim is to ensure that it is done. Another purpose of the invention is to enable the user to identify a risky area on their risk map. If located in that place, the user's risk information regarding that location the combined evaluation of existing environmental and behavioral data and the user The aim is to provide an early warning about this. In this way, the diagnosis and the neurological attack are 10 Unlike detection or intervention carried out after an event occurs, Environmental and behavioral conditions that can trigger a neurological attack before the attack occurs The aim is to inform the user by identifying and determining this information. LIST OF FIGURES 15 Figure 1. The subject of the invention: a multi-sensor neurological seizure risk analysis and warning system. It is a schematic view showing the data flow between the components. The corresponding numbers in the figures are: 1. Sleep and circadian rhythm sensor 20 2. Light and flicker sensor 3. Stress analysis sensor 4. Physical activity and fatigue sensor 5. Drug tracking module 6. Central decision module 25 7. Risk map module 8. User alert system 9. Location determination module DETAILED DESCRIPTION OF THE INVENTION 30 The invention aims to prevent neurological seizures in a user prone to epilepsy or similar neurological attacks. Identifying environmental and behavioral factors that may contribute to its formation, the joint evaluation of the data obtained from different sources and 4 Based on the calculated risk level, the user is warned before an attack occurs. It relates to a multi-sensor neurological attack risk analysis system structured for this purpose. The system described in this invention obtains data regarding the user's sleep and circadian rhythm. The sleep and circadian rhythm sensor (1) detects the light in the user's environment. The light and flicker sensor (2) which obtains data on the user’s stress level The stress analysis sensor (3) that obtains the data, the user’s physical activity and fatigue Physical activity and fatigue sensor (4) which determines the status of the user's medication use The drug tracking module (5) which provides data on the regulation of the said data together central decision module (6) that evaluates risk information associated with location risk map module (7), user alert system (8) which conveys risk information to the user and 10 It includes a location module (9) that provides the user's location information. Sleep and circadian rhythm sensor (1) monitors the user's sleep duration and sleep It enables the acquisition of data regarding sleep and circadian rhythm patterns. sensor (1) to detect changes in the user's movement status with at least one It includes an accelerometer, and the 15 readings obtained by the accelerometer at specific time intervals Movement data is recorded along with time information. The obtained movement data, by the central decision module (6) when the user is moving and stationary determining the intervals and the sleep-wake detection algorithm using this data estimating sleep and wakefulness periods through processing with It is used. In this way, the user's bedtime, wake-up time and 20 Sleep parameters such as total sleep duration are obtained and the aforementioned Changes occurring in the parameters are determined. The sleep and Analyzing the timing and recurring patterns of activity data over the days. by altering the user's sleep-wake cycle and rest-activity pattern Changes are being identified and these changes relate to the circadian rhythm. It is used in the assessment. Thus, only the decrease in total sleep duration is considered. No, it's the user's sleep and wake-up times and daily sleep Changes in timing are also taken into account in risk assessment. It is available for use. Data obtained regarding sleep duration is processed by the central decision module (6) 30 is processed as a risk parameter. The risk parameter is processed by sensors and / or the user. Measured data provided by [the organization / individual] indicates the user's risk of neurological seizures. a specific measurable indicator to be used in the evaluation It refers to the data value obtained through transformation. In this context, the user's daily sleep duration, bedtime, and wake-up time sleep parameters are based on the user's previous sleep patterns or a defined reference range. Risk parameters are determined by taking into account deviations from the values. It can be converted. For example, the user's daily sleep duration can be set or converted. Sleep duration falling below the user-specific reference sleep duration, or difficulty falling asleep and waking up (5) Significant changes in sleep patterns during sleep times compared to previous sleep patterns, central It can be evaluated as a risk parameter by the decision module (6). Thus not only the measured sleep duration, but also changes in sleep duration and sleep timing. The resulting changes can also be used in assessing the risk of neurological attacks. The light and flicker sensor (2) adjusts the lighting level in the user's environment to 10 and measures the time-dependent changes in light intensity of the light source. Environment Illumination level is measured in lux (lx) via a photometric light sensor. The measurements are transferred to the system. For flicker detection, the optical sensor from the light source is used. The signal is sampled over time to create a light intensity time series; Periodic variations of the obtained signal are analyzed in the time and / or frequency domain. 15 The flicker frequency is determined in Hertz (Hz) by means of this method. This method is temporal. It is consistent with the CIE technical approach to measuring light modulation. the level of illumination obtained, the flicker frequency, and the change in light intensity as needed Its size is transferred to the central decision module, along with other environmental and user-related data. It is used in the creation of light-related risk parameters along with data. 20 In the evaluation of photosensitivity epilepsy, the literature specifically mentions frequencies between 10–30 Hz. While it is known that intermittent photic stimulation within this range has been studied, risk assessment It is not done solely based on frequency; light intensity and the characteristics of temporal modulation are also considered. and exposure conditions are evaluated together. Within the scope of the system, ambient noise level is also included in the environmental risk analysis. 25 This can be determined. The noise level obtained from the environment, the sound pressure generated in the environment. This is determined through a microphone-based sound sensor that detects changes, and The sound signal is digitized and the sound level value is expressed in decibels. is converted. The resulting noise level is converted by the central decision module (6). They are being processed for evaluation. In an example evaluation, 30 below 50 dB Environments with low noise levels, environments between 50 dB and 70 dB are considered medium noise. Environments at and above 70 dB are considered high noise levels. This can be assessed. Sudden loud noises or sustained loud noises. 6 Levels are included in neurological attack risk analysis as one of the environmental risk parameters. is being done. Stress analysis sensor (3) biometric data related to the user's stress response It obtains heart rate and heart rate variability (HRV) via the sensor. 5 showing cardiovascular indicators and / or changes in skin conductivity Electrodermal activity (EDA) data can be measured. In PPG-based measurement, from the tissue... By detecting the time-dependent change in the reflected light, a pulse signal is obtained, and this Data regarding heart rate and heart intervals can be extracted from the signal. EDA In the measurement, between at least two electrodes placed on the user's skin Electrical conductivity is measured over time. Through electrodes, 10... A low-level electrical stimulus is applied and passes between the electrodes. Skin conductivity is determined by measuring electrical current. The obtained skin conductivity... The signal is recorded as a time series; slow changes in that signal tonic component and phasic component representing short-term and rapid conductivity changes. They are separated from each other. Skin conductivity level (SCL) from the tonic component, phase 15 Skin conductivity responses (SCRs) are determined from the component; regarding these SCRs... by measuring peak amplitude, rise time, and the number of responses within a specific time interval Physiological indicators associated with stress response are being established. The resulting biometric data... The data is transferred to the central decision module (6) and neurological attack together with other data. It is used in assessing the risk. In addition, 20 by the user. Stress data entered into the system can also be included in the risk assessment. The user, The stress level detected through the application interface is defined on the interface. by selecting from the criteria and relating the time interval during which the stress was experienced and the source of the stress. Information can be entered into the system manually. This data is timestamped. recorded and combined with biometric data obtained from sensors, central decision-making 25 It is evaluated in the module. Physical activity and fatigue sensor (4) monitors the user's daily physical activity The sensor enables the determination of the level and state of physical fatigue. motion data is detected via an accelerometer that senses the user's movements in three axes. obtaining and daily activity 30 through the central decision module (6) from these data parameters related to intensity, duration of movement and exercise intensity These inferences can be made from the motion moments detected by the accelerometer. total time, amount of movement during hours within total waking hours, and the system 7 the ratio of movements that conform to recorded exercise patterns to the total number of active hours. It is calculated in this way. Long periods of physical activity are also used to obtain the results. by evaluating movement data in terms of duration and intensity This data can be determined. This data is transferred to the central decision module (6) and sleep As a result of evaluating the irregularity and the intensity of physical activity together, 5 It is determined whether the user is experiencing extreme fatigue. The determined fatigue status is also the risk used by the central decision module (6). It constitutes one of the parameters. Drug tracking module (5) relates to the user's antiepileptic drug usage regimen. It tracks the data. Data regarding the user's medication use is available on application interface 10. information such as drug name, dosage, frequency of use, and expected time of use. This allows access to the system and tracking of medication usage times. (Predicted) failure of the user to consent to and report taking medication at the time of medication use In this case, it will be assessed that the relevant dose was not taken or was not taken on time. The issue at hand is a central decision regarding drug use as a risk parameter. 15 It is transmitted to module (6). Sleep and circadian rhythm sensor (1), light and flicker sensor (2), stress analyzer by sensor (3), physical activity and fatigue sensor (4) and drug monitoring module (5) The data obtained are transferred to the central decision module (6). Central decision module (6), raw or measured data obtained can be used as parameters in risk analysis 20 This is achieved by converting the analog electrical signals obtained by the sensors into sensor signals. analog-to-digital converter by sampling at a specific sampling frequency within it It is converted into digital data via (ADC); providing digital output. The data obtained from the sensors is then directly processed as numerical data for central decision-making. It is transferred to the module (6). Digitized or obtained directly digitally 25 The central decision module (6) uses the sensor data obtained from the signal to determine the signal. reducing unwanted noise and interference components and analyzing the signal to be analyzed. Signal preprocessing is applied to clarify the components. As part of the processing, an appropriate filtering operation is performed depending on the nature of the sensor data. is being implemented; within this scope, signal components in a specific frequency range are 30 To pass through and attenuate components outside this range, low-pass and high-pass technologies are used. At least one of the pass-through or band-pass filters can be used. Filtered time The series of data is processed within specified time intervals to determine the duration of movement, movement 8 intensity, light intensity, change in light intensity, flicker frequency, heart rate, heart pulse intervals, heart rate variability (HRV), skin conductivity level (SCL), skin conductivity Peak amplitude, rise time, number of responses, and sound level characteristics of the speaker response (SCR). The characteristics obtained are extracted. The resulting features are related to environmental, physiological or behavioral risks. It is used in the creation of the parameter and the central decision module (6) risk 5 This data is transferred to the scoring system. In this context, sleep and circadian rhythm data are used to determine sleep patterns. duration, time of going to sleep, time of waking up, and whether one is active or inactive time intervals; duration of movement, daily data from physical activity and fatigue Activity intensity and exercise intensity; illumination level and light from light data. The magnitude of the change in intensity and flicker frequency; heart rate from PPG data, 10 Heart intervals and heart rate variability (HRV); skin conductivity from EDA data. level (SCL), peak amplitude, rise time and specific skin conductivity responses (SCR) the number of responses in a time interval; and from audio data, noise level in decibels. The level is extracted. The obtained characteristics are related to environmental, physiological or behavioral factors. It is used in the creation of the risk parameter and the central decision module (6) 15 This is reflected in the risk scoring. Central decision module (6) receives parameters from the sources mentioned above. Instead of evaluating these parameters in order to generate separate alarms, Working together, they create a neurological attack risk score. The risk score... During its creation, each risk parameter is assigned a predetermined weight. 20 A coefficient is applied. Thus, from the user's perspective, multiple triggers can be used simultaneously. This allows for the identification of the combined effect of the conditions under which it emerged at a particular time. Central Risk score calculation performed in the decision module (6); R = Σ(wᵢ × Pᵢ) 25 It is expressed according to the relationship. Here, R is the calculated neurological attack risk score, and Pᵢ relevant environmental or behavioral risk processed by central decision module (6) `` represents the parameter and wᵢ represents the weighting coefficient determined for the risk parameter in question. Thus, the risk score is calculated not from a single sensor value, but from different sensors and monitoring data. 30 from the combined evaluation of parameters provided by the modules is created. The calculated risk score is based on the user's past physiological, environmental and Risk threshold determined according to a reference risk profile created from behavioral data. The reference profile is compared with the user's sleep patterns and stress levels. 9 indicators include physical activity, medication regimen, and records of previous neurological attacks. It is created by taking this into consideration, and new measurements are evaluated according to this profile. Based on the assessment, the user's situation is classified as low risk or medium risk. or classified as high risk. As a result of this assessment... The user's circumstances are categorized as low risk, medium risk, or high risk (5). It is classified. The calculated risk level is based on a predetermined warning threshold. If it exceeds its value, the central decision module (6) user warning system (8) It enables its operation. Location determination module (9), regarding the geographical location of the user This module obtains the data. Based on GPS, it determines the user's location. It determines and transfers the location information to the risk map module (7). Risk map module (7) is determined by central decision module (6) Location provided by the environmental risk information and location determination module (9) It correlates the data. Thus, it can determine light intensity and flicker at a specific location. frequency and, if applicable, noise level, as well as other environmental triggers. If its presence is detected, the risk information in question will be shared with the relevant location. can be associated. The risk map module (7) provides risk data associated with location. using location data to indicate areas containing potential triggering environmental factors. It creates a risk map based on the user's current situation. Thus, the system only focuses on the user's current situation. not the individual situation, but the user's current situation or the risk information previously available to them. 20 It can also evaluate the environmental characteristics of the associated environment. The user in the risk map module of the location determined by the location determination module (9) (7) If associated with an area identified as risky, the central decision module (6) risk information regarding the location in question with the user’s current sensor and monitoring data They evaluate them together. This way, it is not only the user's location that matters, but also... 25 due to, or because a risky value is determined by only a single sensor Instead of issuing a direct alarm, location information and current environmental and behavioral factors are used. The factors are being considered together. User alert system (8), risk determined by central decision module (6) It provides notifications to the user according to their level. These notifications are visual and auditory. 30 or can be created in a vibrating manner and a smartwatch or mobile application This can be communicated to the user. The risk value exceeds the warning threshold. The notification generated in this situation informs the user that they are experiencing a neurological attack under the current conditions. Information is provided indicating that they are at increased risk in this regard. Notification within this scope, listening to the user, assessing their medication status, or It can be warned to change its environmental conditions. The system that is the subject of the invention primarily focuses on the user and the user's needs during its operation. Data regarding the environment in which it is located is obtained. Sleep and circadian rhythm sensor (1) Light and flicker sensors determine the user's sleep duration and sleep timing. 5 (2) provides data on ambient light intensity and flicker frequency, stress The analysis sensor (3) obtains data on stress level, physical activity and The fatigue sensor (4) determines the user’s activity and fatigue status and the medication The monitoring module (5) provides data on the medication usage regimen. The obtained word The subject data is processed in the central decision module (6) and 10 for each parameter A common risk score is generated using a determined weighting coefficient. The risk score is determined by predetermined risk levels and warning thresholds. is compared. At the same time, the positioning module (9) obtained user location, environmental risk data in risk map module (7) It is associated with. The calculated risk value reaching a level that requires a warning is 15. In this case, a notification is sent to the user by the user alert system (8). Thanks to this working principle, the system provides information about the physiological aspects of the neurological attack itself. Instead of identifying the symptoms to determine when an attack has started, the focus is on observing the formation of the attack. It assesses environmental and behavioral conditions that may trigger an attack before the attack occurs. By using different parameters together, risk assessment can be done in a single 20 It is being decoupled from the trigger and location information is being included in the risk analysis. This includes an additional assessment of the risk posed by the user's environment. is provided. 30

Claims

11 REQUESTS 1. Neurological seizure in a user prone to epilepsy or similar neurological attacks. environmental and behavioral factors that may contribute to its formation determination and, depending on these factors, the risk of neurological seizures is 5 a structured neurological attack risk analysis system for the purpose of evaluation Its characteristic is; - obtains data on the user's sleep duration and sleep timing. at least one sleep and circadian rhythm sensor (1), - the lighting level and light intensity in the user's environment is 10 at least one light and flicker that provides data on its time-dependent change. sensor (2), - cardiovascular including heart rate and heart rate variability (HRV) indicators and / or at least two placed on the user's skin by measuring the electrical conductivity between the electrodes as a function of time, volume 15 electrodermal activity (EDA) data showing changes in conductivity obtaining at least one stress analysis sensor (3), - data regarding the user's physical activity level and fatigue status obtaining at least one physical activity and fatigue sensor (4), - at least one drug providing data on the user's medication regimen 20 tracking module (5), - by the sensors (1, 2, 3, 4) and the drug tracking module (5) receiving the provided data, signal pre-converting the digitized sensor data to apply processing, environmental, physiological and removing behavioral characteristics, reducing the risk of neurological attacks based on the removed characteristics is 25%. converting each into risk parameters to be used in the assessment a predetermined weighting coefficient for a risk parameter by implementing and processing these risk parameters together To create a neurological attack risk score, and to assign this risk score to the user. 30 created from past physiological, environmental and behavioral data Comparing with risk threshold values ​​determined according to the reference risk profile, As a result of this comparison, the user's risk level is low. classifying risk as medium risk or high risk and calculating risk 12 if the level exceeds the predetermined warning threshold value At least one configured to operate the user alert system (8) central decision module (6), - at least one location providing data on the user's geographic location. Determination module (9), 5 - environmental risk information determined by the central decision module (6) with the location data provided by the location determination module (9) at least one risk map that generates location-based risk information by associating risks. module (7) and, - Neurological attack risk determined by central decision module (6) 10 at least one user providing notifications to the user depending on their level It is characterized by having a warning system (8).

2. It is a neurological seizure risk analysis system according to Claim 1, and its feature is that the user at least one accelerometer and a probe to determine changes in the state of motion The subject is the motion data obtained by the accelerometer combined with time information. 15 Sleep and circadian rhythm sensor configured to record together (1) It is characterized by its inclusion.

3. According to Claim 2, it is a neurological seizure risk analysis system, the feature of which is; sleep and by processing motion data obtained by the circadian rhythm sensor (1) To determine the time intervals when the user is active and inactive, and to specify 20 By processing the subject data with a sleep-wake detection algorithm, the user central decision system structured to predict sleep and wakefulness periods It is characterized by containing module (6).

4. It is a neurological seizure risk analysis system according to claim 3, and its feature is that it analyzes the user's sleep. The duration, at least one of which is the user's 25 hours, is the time of going to sleep and the time of waking up. based on previous sleep patterns or established reference values structured to evaluate deviation as a risk parameter It is characterized by containing a central decision module (6).

5. According to Claim 1, it is a neurological seizure risk analysis system, the feature of which is; ambient lighting. At least one photometric light configured to determine its level in lux 30 It is characterized by containing a light sensor and a flicker sensor (2).

6. According to claim 5, it is a neurological seizure risk analysis system, the feature of which is; the environment by sampling the optical signal obtained from the light source as a function of time to create a light intensity time series and the periodic elements in that time series 13 By analyzing the changes in the time and / or frequency domain, the flicker frequency can be determined. with a light and flicker sensor (2) configured to detect It is characteristic.

7. According to claim 6, it is a neurological seizure risk analysis system, characterized by its flicker frequency. together with the ambient illumination level, the magnitude of the change in light intensity, and 5 a light-related risk by considering at least one of the exposure conditions together central decision module structured to create parameters (6) It is characterized by its inclusion.

8. It is a neurological seizure risk analysis system according to Claim 1, and its feature is; the environment in which it occurs. to detect sound pressure changes and convert the resulting sound signal to 10 decibels at least one configured to convert noise level data to noise level data. microphone-based sound sensor and the noise level data in question are environmental. central decision structured to evaluate as a risk parameter It is characterized by containing module (6).

9. According to Claim 1, it is a neurological attack risk analysis system, the feature of which is; 15 reflected from the tissue. Obtaining a pulse signal by detecting the time-dependent change of light and speech The subject is at least the heart rate and heart rate variability from the pulse signal. PPG-based stress analysis sensor configured to identify one (3) It is characterized by its inclusion.

10. According to Claim 1, it is a neurological seizure risk analysis system, the feature of which is; stress analysis 20 The electrodermal activity (EDA) data obtained by the sensor (3) over time to record as a series and the slowdown in the electrodermal activity data in question representing short-term and rapid conductivity changes with a changing tonic component By separating the phasic component from the tonic component, the skin's conductivity level is reduced. (SCL), the peak amplitude of skin conductivity responses (SCR) from the phasic component, 25 rise time and at least one of the number of responses within a specific time interval with the inclusion of a central decision module (6) structured to determine It is characteristic.

11. It is a neurological seizure risk analysis system according to Claim 1, and its feature is that it is defined by the user. 30 regarding perceived stress level, time period during which stress was experienced, and source of stress. to ensure that at least one of the data points is manually entered into the system a structured user interface and the data in question with a timestamp by recording along with biometric data obtained from sensors 14 with a central decision module (6) structured for evaluation It is characteristic.

12. It is a neurological seizure risk analysis system according to Claim 1, and its feature is that the user at least one accelerometer configured to detect their movements in three axes physical activity and fatigue sensor (4) and accelerometer 5 by processing the motion data obtained, the sum of the motion moments duration, amount of movement hours within total waking hours, and the system total movement hours of movements that conform to recorded exercise patterns within the scope Daily activity intensity, movement duration, and using at least one of the following ratios. 10 centrally structured to determine at least one of the exercise intensities It is characterized by containing a decision module (6).

13. According to claim 12, it is a neurological seizure risk analysis system, the feature of which is; physical activity and the physical activity intensity determined by the fatigue sensor (4) sleep disorder determined by sleep and circadian rhythm sensor (1) by evaluating them together, a risk assessment regarding the user's fatigue level is 15 central decision module structured to create parameters (6) It is characterized by its inclusion.

14. According to Claim 1, it is a neurological seizure risk analysis system, the feature of which is; the user's medication. from the information on name, dosage, frequency of use and expected time of use to record at least one and the user takes the medication at the prescribed medication time 20 If the patient does not approve or report the purchase, the situation will be reported to the drug company. central decision module as a risk parameter for its use (6) It is characterized by containing a drug tracking module (5) structured for transmission. According to claim 15, it is a neurological seizure risk analysis system, and its feature is; sensors (1, 2, 3, 4) the obtained numerical time series data, the nature of the sensor data 25 depending on the system, at least low-pass, high-pass, or band-pass filters. implementing one and filtering data such as motion duration, motion intensity, light intensity, change in light intensity, flicker frequency, heart rate, heartbeat intervals, heart rate variability (HRV), skin conductivity level (SCL), skin Peak amplitude, rise time, response number, and sound of conductivity responses (SCR) 30 a central decision module structured to remove at least one of the levels (6) is characterized by its inclusion. According to Claim 16, it is a neurological seizure risk analysis system, the feature of which is; neurological seizure risk your score R = Σ(wᵢ × Pᵢ) central decision module structured to calculate according to the relationship (6) It is characterized by its inclusion.

17. It is a neurological seizure risk analysis system according to Claim 16, and its characteristic is; The reference risk profile includes the user's past sleep patterns, stress indicators, 5 from physical activity, medication regimen, and previous neurological attack records It is characterized by being created by taking only a few people into consideration. According to Claim 18, it is a neurological seizure risk analysis system, and its characteristic feature is the analogous data it obtains. to sample electrical signals at a specific sampling frequency and the analog electrical signals are converted to at least one analog-to-digital converter (ADC). sensors configured to convert data into digital data via (1, 2, It is characterized by containing 3, 4). According to Claim 19, it is a neurological seizure risk analysis system, the feature of which is; the user It is configured to determine its geographical location using GPS. It is characterized by containing a location determination module (9). 15 According to Claim 20, 1, it is a neurological seizure risk analysis system, the feature of which is; light intensity, environmental risk information regarding at least one of the flicker frequency and noise level. to correlate with the relevant geographic location and potential environmental triggers to create a location-based risk map showing the areas where it is located It is characterized by containing a structured risk map module (7). 20 21. It is a neurological seizure risk analysis system according to claim 20, and its feature is that it allows the user to... Risk map of location determined by location determination module (9) If it is associated with an area identified as risky in module (7), then the word the user's current environmental, physiological and risk information regarding the location in question 25 to be evaluated together with at least one of the behavioral risk parameters. It is characterized by containing a structured central decision module (6). According to Claim 22, Section 1, it is a neurological seizure risk analysis system, the feature of which is that it provides the user with The notification provided will be in the form of at least one of the following: visual, auditory, or vibrating notifications. It includes a user alert system (8) structured to create It is characterized by 30 23. According to Claim 22, it is a neurological seizure risk analysis system, the characteristic of which is; created to deliver the notification to the user via a mobile application and / or smartwatch It is characterized by having a user alert system (8) structured for this purpose. 16 24. A neurological seizure risk analysis system according to claim 22 or 23, the characteristic of which is; The user is informed that the risk of neurological attacks is increased, as well as being advised on rest and medication. evaluating the use case or environmental conditions. User alert configured to deliver at least one warning about changes. It is characterized by including the system (8). 5 15 25

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