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WO2019177446A1 - Système d'alertes pour signalement d'épilepsie - Google Patents

Système d'alertes pour signalement d'épilepsie Download PDF

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Publication number
WO2019177446A1
WO2019177446A1 PCT/MX2018/000026 MX2018000026W WO2019177446A1 WO 2019177446 A1 WO2019177446 A1 WO 2019177446A1 MX 2018000026 W MX2018000026 W MX 2018000026W WO 2019177446 A1 WO2019177446 A1 WO 2019177446A1
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Prior art keywords
eeg
sample
signals
signal
epileptic
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PCT/MX2018/000026
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English (en)
Spanish (es)
Inventor
Jesús Guillermo SERVÍN AGUILAR
Luis RIZO DOMÍNGUEZ
Jorge Arturo PARDIÑAS MIR
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Instituto Tecnologico Y De Estudios Superiores De Occidente AC
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Instituto Tecnologico Y De Estudios Superiores De Occidente AC
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Publication of WO2019177446A1 publication Critical patent/WO2019177446A1/fr
Anticipated expiration legal-status Critical
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons

Definitions

  • the present invention is related to the medical industry in general, in particular it relates to the field of medical systems and devices used in the measurement of physiological variables for the detection of problems and for the timely warning of the occurrence of an event, imbalance or physiological involvement. More specifically it refers to an alert system for the announcement of an epilepsy event.
  • the brain activity of a person involves the generation of brain waves derived from the electrical activity of neurons; brain waves can be classified into different types according to their frequency, that is, the time that has a certain periodicity associated with the electrical impulse produced through the neurons, among them we find the Delta waves (from 1 to 3 Hz), the Theta waves (3.5 to 7.5 HZ), Alpha waves (8 to 13 Hz), Beta waves (12 to 33 Hz) and Gamma waves (25 to 100Hz).
  • Electroencephalograph that generates electroencephalography signals (EEG) that can be collected, processed and interpreted for multiple purposes, such as to provide alerts of neurological events that occur in a subject as epileptic seizures, to detect at least some physiological parameter of a subject while the subject sleeps, among others.
  • EEG electroencephalography signals
  • the X series device - EEG Wireless moniforing [February 14, 2018, which can be found on the web: h ttp: // ad vancedbrain monitoring.com] is a portable EEG signal detector, which requires at least have 10 electrodes connected, while e! proposed in this patent only requires 2 electrodes.
  • Pradhan N, et al. Detection heard seizure activi ⁇ y in EEG by an artificial neural network: a preliminary study Compui omiomed Res 1998; 29: 303-13.), Casson AJ, et al. (Algorithm for AEEG data selection leading to wireless and long term epi! Epsy monitoring. Conf Proc IEEE Eng Med Bio! Soc 2007; 2007: 2456-9.), Petersen EB, et al (Generic single-channel detection of absence seizures. Conf Proc IEEE Eng Med Biol Soc 2011; 2011: 4820-3.), Liu Y, ef al. (Automatic seizure detection using wavelet transform and SVM in ⁇ ong-term intracranial EEG.
  • Wilson SB A neural network method ⁇ or automatic and increment! Learning applied to paien-dependent seizure detection ClinNeurophysio! 2005; 116: 1785-95.
  • D'ASessandro M et al. (A mu l ⁇ i-featu re and multi-channel univariate selection process for seizure prediction. Clin Neurophysio! 2005: 116: 506— 16.), use a neuron network! of probability for the detection of epileptic seizures.
  • SVM sin ⁇ vector machine
  • This last article uses statistical comparison methods to detect epileptic seizures.
  • the system includes: a monitoring module adapted to detect and sample a neurological signal; an event detection module coupled to the monitoring module to detect one or more types of predetermined notifiable events based on the neurological signal detected; and an alert module coupled to the event detection module, in which to detect an event notified by the event detection module, said alert module selects a first alert contact from a plurality of contacts contained in a distance of contacts and generates a first alert communication to the first alert contact.
  • the Di document also does not reveal that the stored signal goes through a low-pass filtering process with a cut-off frequency of 40 Hz and order 20 for noise elimination.
  • Document D1 also does not reveal that the filtered signal is passed to a third block of calculation of the dispersion parameter through calculation means in the same microcontroller, which allows the data to be sorted and the values of quantiles 25 and 75 of the filtered EEG signal, which are associated with the previously defined scatter parameter.
  • the statistical quantiles are averaged and used in the estimation of the dispersion parameter that are again filtered to analyze them by means of analysis of the signals processed in order to look for epileptic seizures in real time.
  • Document D1 does not disclose that a sample-by-sample comparison of each of the filtered dispersion values is executed, having two detection thresholds.
  • the first threshold compares the current sample with the previous sample (each sample represents 0.03 second of signal), if the current sample is 7 times larger than the previous one, then the system determines that the patient is suffering an epileptic attack and sends an alert to the last block that is the attack notifier, where an LED is lit red color and an audible alarm sounds.
  • the second threshold is the same as the previous one, by amplitudes, where the current sample is analyzed with the previous one. If the current sample is 7 times smaller than the previous sample then the system determines that the patient left the seizure and is in normal state or recovering from the attack, sending a signal to! Notifier block e! which turns off the audible alarm and changes the color of the LED from red to green.
  • Document US8679034 B2 was also located (which has been called document D 2) of Avner Halperin et al of January 25, 2013, which discloses devices and methods that include detecting at least one parameter of a subject while the subject is sleeping. parameter is analyzed, and a condition of the subject is determined at least in part in response to the analysis The subject is alerted to the condition only after the subject wakes up Other applications are also described.
  • three sensors are proposed which will be in the patient's bed while he sleeps.
  • the sensors are: motion, acoustic and temperature. With the combination of the three sensors they detect the following parameters of the patient: breathing, heartbeat, cough, whether he is excited or restless and his blood pressure.
  • Said document D2 did not reveal, nor suggest sampling of the electroencephalography signals, nor that the electrocardiogram signals can be obtained by means of electrodes arranged in leads F p 1 - F7 (or in their counterpart leads F p 2 - F8, in where said electrodes are connected to a biomedical signal amplifier and an NXP Freedom K64 processing card It does not disclose that the system includes a microcontroller where the dispersion parameter calculation is executed and where the patient's EEG samples are stored in a buffer .
  • Document D2 also did not reveal the stored signal going through a low-pass filtering process with a cut-off frequency of 40 Hz and order 20 for noise elimination.
  • Document D2 also does not disclose that the filtered signal is passed to a third block of calculation of the dispersion parameter through calculation means in the same microcontroller. That allows the data to be sorted and the values of quantiles 25 and 75 of the filtered EEG signal are calculated, which are associated with the previously defined dispersion parameter. The statistical quantiles are averaged and used in the estimation of the dispersion parameter that are again filtered to analyze them by means of analysis of the signals processed in order to look for epileptic seizures in real time.
  • Document D2 does not disclose that a sample-by-sample comparison of each of the filtered dispersion values is executed, having two detection thresholds.
  • the first threshold compares the sample current with the previous sample (each sample represents 0.Q3 second of signal), if the current sample is 7 times greater than the previous one, then the system determines that the patient is suffering an epileptic attack and sends an alert to the last block that is the attack notifier, where a red LED turns on and an audible alarm sounds.
  • the second threshold is the same as the previous one, by amplitudes, where the current sample is analyzed with the previous one.
  • the system determines that the patient left the seizure and is in normal state or recovering from the attack, sending a signal to the notification block e! which turns off the audible alarm and changes the color of the LED from red to green.
  • the main difference in our request is the type of signal that enters our device.
  • document D2 requires motion, acoustic and temperature sensors, the proposed patent is based on the patient's electroencephalography (EEG) signal. Since the signals considered are different, the processing is different in the own proposal compared to the one mentioned in the state of the art. Thus our invention is new on document D2.
  • CN1Q1583311 B (which has been called document D3 for reference) was found by Uri Kramer et al. of September 19, 2006, which unveils a device and a procedure to detect and alert an epileptic seizure.
  • the detector is portable by an active user and does not interfere with normal daily movement.
  • the detector relies on at least one motion sensor and performs a computerized analysis to determine if a seizure is occurring.
  • the parameters of the motion signal are compared with the non-epileptic epileptic movement signal parameters; and the epileptic parameters, and a decision is reached as to whether or not to indicate an alert.
  • the analysis is based on one or more of the following movement signal parameters: the duration of the movement, the frequency of the movement, the amplitude of the signal, the direction of the movement and the ratio of the amplitude over the movement frequency
  • sampling means of the electroencephalography signals are obtained by means of electrodes arranged in the leads F p 1 - F7 (or in their counterpart the leads F p 2 - F8), wherein said electrodes are connected to a biomedical signal amplifier and an NXP Freedom K84 processing card. It does not disclose that the system includes a microcontroller where the dispersion parameter calculation is executed and where the patient's EEG samples are stored in a buffer.
  • Document D3 also does not reveal the signal stored for a low-pass filtering process with a cut-off frequency of 40 Hz and Order 20 for noise elimination.
  • Document D3 also does not disclose that the filtered signal is passed to a third calculation block of the dispersion parameter through calculation means in the same microcontroller. That allows the data to be sorted and the values of quanile ios 25 and 75 of the filtered EEG signal are calculated, which are associated with the previously defined dispersion parameter. The statistical quan ⁇ iles are averaged and are used in the estimation of the dispersion parameter that are again filtered to analyze them by means of analysis of the signals processed in order to look for epileptic seizures in real time.
  • Document D3 does not disclose that a sample-by-sample comparison of each of the filtered dispersion values is executed, having two detection thresholds.
  • the first threshold compares the current sample with the previous sample (each sample represents 003 second of signal), if the current sample is 7 times larger than the previous one, then the system determines that the patient is suffering an epileptic attack and sends an alert to the last block that is the notifier of attacks, where a red LED is lit and an audible alarm sounds And where the second threshold is the same as the previous one, by amplitudes, where the current sample is analyzed with the previous one.
  • the system determines that the patient left the epileptic attack and is in normal state or recovering from the attack, sending a signal to the notifying block which turns off the audible alarm and changes the color of the LED from red to green.
  • the invention of said document D3 also detects epileptic events (such as that proposed in our invention), it is based on the detection of sudden movements of the patient by a portable device.
  • Our invention is an alert system for epilepsy warning based on the detection of the patient's EEG signal and the quantification of statistical signal parameters.
  • our invention is new on document D3. Given the need for an alert system for the highly reliable epilepsy warning of rapid real-time detection of epileptic seizures using the dispersion parameter, the present invention was developed.
  • the main objective of the present invention is to make available a system integrated by a device whose function is to alert for the warning of an epileptic attack, both auditively (with a sound), and visually (with a red LED ) in real time; where e!
  • the system can be implemented in any person who suffers epileptic seizures, having as a priority those whose trigger factor is visually.
  • Another objective of the invention is to provide such an alert system for epilepsy warning that, in addition, is less sensitive to noise by the stochastic processing of the EEG signal, which is also robust and that guarantees to alert effectively and in real time when a patient suffers an epileptic seizure,
  • the alert system for epilepsy warning in accordance with the present invention, consists of means for sampling electroencephalography (EEG) signals to a person suffering from epileptic seizures, through preferably gold electrodes arranged in leads F p 1 - F7 (or in their counterpart leads F p 2 - F8); where the EEG signal has little amplitude (in the range of micro volts), so a conductive gel should be used before placing the gold electrodes preferably on the skin.
  • Said electrodes are connected to a biomedical signal amplifier and to a processing card preferably an NXP Freedom K64 card.
  • the system includes a microcontroller where the dispersion parameter calculation is executed and where the patient's EEG samples are stored in a buffer. These samples will be stored in an internal memory, the internal buffer (approximately 0.39 seconds or 1000 samples being the equivalent).
  • This system also includes a low-pass filtering module of EEG signals with a cut-off frequency of 40 Hz and order 20, which will help eliminate noise caused by electrodes, environmental factors or voltages caused by the human body at the time Take sample.
  • Said microcontroller also includes means for calculating the dispersion parameter of the filtered EEG signal where the data is ordered and the values of quantiles 25 and 75 of the signal are calculated! of filtered EEG, which are associated with the previously defined dispersion parameter. Statistical quantiles are averaged and used in the estimation of the dispersion parameter. This process is repeated consecutively, until complete the sign! EEG or until the device is removed from the user. The following equation 1 is associated with the calculation of the dispersion estimator d.
  • Equation 1 Characteristic formula to calculate the dispersion parameter.
  • the dispersion values within! The system is entered into a low-pass filter module with a cut-off frequency of 0.5 Hz and a filter order with a factor of 20, which will leave the dispersion values ready to be analyzed reliably.
  • the analyzer makes a sample-by-sample comparison of each of the filtered dispersion values, having two detection thresholds.
  • the first threshold compares the current sample with the previous sample (each sample represents 0.03 second of signal), if the current sample is 7 times larger than the previous one, then the system determines that the user is suffering an epileptic attack and sends an alert to attack notifier, where a red LED turns on and an audible alarm sounds.
  • the second threshold is the same as the previous one, by amplitudes, where analyze the current sample with the previous one If the current sample is
  • the system determines that the user left the epileptic attack and is in normal state or recovering from the attack, sending a signal to the notifying block which turns off the audible alarm and changes the color of the red LED green color
  • the process is repeated constantly until the system is turned off or the electrodes are removed from the user.
  • the alert system for the epilepsy warning analyzes and processes EEG signals in order to find epileptic seizures using the parameter of the analyzed EEG signal.
  • the main advantage of the system of the present invention is that it is less sensitive to noise by the stochastic processing of the EEG signal, having a robust device, ensuring that it will be alerted effectively and in real time when a user suffers an epileptic attack.
  • the computational expenditure consumed by the system is minimal and the amount of memory required to function properly is reduced, using only 0.39 seconds or 1,000 samples of the EEG signal for processing.
  • the system has only one analysis channel, the user does not take much time to put on and take off the electrode that will be censoring the EEG signal, in addition to being more comfortable for the patient.
  • FIGURES Figure 1 shows a block diagram of! device that integrates the alert system for the epilepsy warning, in accordance with the present invention.
  • Figures 2 and 3 illustrate a side view and a top view, respectively, of the head of a user showing the placement and arrangement of the electrodes for taking the EEG signals, in accordance with the alert system for the epilepsy warning of The present invention.
  • Figure 4 shows a flow chart of the process that follows the alert system for the epilepsy warning, in accordance with the present invention.
  • Figure 5 illustrates a block diagram of the epilepsy event detector that integrates the alert system for the epilepsy warning, in accordance with the present invention.
  • Figure 8 shows a block diagram and its graphs of the process of sampling of the electroencephalography (EEG) signals, their processing, conditioning and analysis for the alert of the occurrence of an epilepsy attack.
  • Figure 7 shows a graph illustrating the spikes of the dispersion values associated with an epileptic attack.
  • EEG electroencephalography
  • the device that is integrated into the alert system for the epilepsy warning in accordance with The present invention consists of a sampling module of the electroencephalography (EEG) signals (1) where sampling means of the electroencephalography (EEG) signals (2) are integrated consisting of preferably gold electrodes arranged in the leads F p 1 - F7 or in their counterparts leads F p 2 - F8 (see figures 2 and 3) of the head of a user (3); where the EEG signal has little amplitude (in the range of micro volts), so a conductive gel should be used before placing the gold electrodes preferably on the skin.
  • EEG electroencephalography
  • Said device also includes a module for treatment, conditioning and processing (4) of the electroencephalography (EEG) signals, defined by a dispersion estimator (5) consisting of a biomedical signal amplifier (8) where said means of connection are connected sampling of the electroencephalography (EEG) signals (2) and a processing card (7) preferably an NXP Freedom K64 card that integrates a microcontroller (8) where the scatter parameter calculation is executed and where it is stored in a buffer the EEG samples of the patient, these samples will be stored in an internal memory (9) of the same device, the internal buffer (approximately 0.39 seconds or 1000 samples being the equivalent).
  • a dispersion estimator (5) consisting of a biomedical signal amplifier (8) where said means of connection are connected sampling of the electroencephalography (EEG) signals (2) and a processing card (7) preferably an NXP Freedom K64 card that integrates a microcontroller (8) where the scatter parameter calculation is executed and where it is stored in a buffer the EEG samples of the patient, these samples will
  • Said treatment, conditioning and processing modules (4) also includes a pass-through filtering module.
  • Said treatment, conditioning and processing module (4) also includes means for calculating the dispersion parameter of the filtered EEG signal where the data is ordered and the values of quantile ios 25 and 75 of the filtered EEG signal are calculated, which are associated with the previously defined dispersion parameter. Statistical quantiles are averaged and used in the estimation of the dispersion parameter. This process is repeated consecutively, until the EEG signal is completed or until the device is removed from the user. The following equation 1 is associated with the calculation of! scatter parameter.
  • Equation 1 Characteristic formula to calculate the dispersion parameter.
  • Said treatment, conditioning and processing module (4) also includes an epileptic attack detector (12) that performs a signal analysis through means of analyzing the processed signals, with the aim of searching for epileptic attacks in real time.
  • the analyzer makes a sample-by-sample comparison of each of the filtered dispersion values, having two detection thresholds. The first threshold compares the current sample with the previous sample (each sample represents 0.03 second of signal), if the current sample is 7 times larger than the previous one, then the system determines that the user is suffering an epileptic attack and sends an alert to attack notifier
  • the device includes an epileptic attack notification module (13) that notifies the occurrence of an epileptic attack on the user's head (3) through an LED (14) that is switched on at red color and also sounds an audible alarm (15) integrated in the device
  • the second threshold is the same as the previous one, by amplitudes, where the current sample is analyzed with the previous one. If the current sample is 7 times smaller than the previous sample then the system determines that the user left the epileptic attack and is in normal state or recovering from the attack, sending a signal to the notifying block which turns off the audible alarm (15) and Change the color of the LED (14) from red to green.
  • an epileptic seizure consisting of a first stage (a) where "samples of an electroencephalography (EEG) signal are taken" a a person suffering from epileptic seizures, through preferably gold electrodes arranged in leads F p 1 - F7 or Fp2 - F8 where the user must be awake and relaxed, and using a conductive gel before placing the electrodes preferably gold on the skin
  • EEG electroencephalography
  • the signal goes to the second stage (b), which is the “filtering of EEG signals” through a low-pass filtering module (10) with a cut-off frequency of 40 Hz and order 20, which will help eliminate the noise caused by the electrodes, environmental factors or voltages caused by the human body when taking the sample.
  • the filtered EEG signal passes to a third stage (c) which is the "calculation of the dispersion parameter" where the data is sorted and the values of quantiles 25 and 75 of the filtered EEG signal are calculated, which are associated with the previously defined dispersion parameter. Statistical quantiles are averaged and used in the estimation of the dispersion parameter. This process is repeated consecutively, until the signal is completed. EEG or until the device is removed from! patient.
  • the dispersion values within the system are entered into a low-pass filter module (11) with a cutoff frequency of 05 Hz and a filtering order with a factor of 20, which will leave the dispersion values ready to be analyzed reliably.
  • the system performs a “signal analysis to look for epileptic attacks” through means of analysis of the processed signals, with the aim of searching for epileptic attacks in real time.
  • the analyzer makes a sample-by-sample comparison of each of the filtered dispersion values, having two detection thresholds.
  • the first threshold ⁇ e 1 ⁇ compares the current sample with the previous sample (each sample represents 003 second of signal) if the current sample is 7 times larger than the previous one, then the system determines that the patient is suffering an epileptic attack (e 1 ') and sends an alert to the last block that is the attack notifier (f), where a red LED turns on and an audible alarm sounds.
  • the second threshold (e 2) is the same as the previous one, by amplitudes, where the current sample is analyzed with the previous one. If the current sample is 7 times smaller than the previous sample then the system determines that the user left the epileptic seizure (e 2 ') and is in normal state or recovering from! attack by sending a signal to the notifying block and which turns off the audible alarm and changes the color of the LED from red to green (g).
  • the alert system for the epilepsy warning analyzes and processes EEG signals in order to find epileptic seizures using the dispersion parameter of the analyzed EEG signal.
  • the main advantage of the system of the present invention is that it is less sensitive to noise by stochastic processing of the EEG signal, having a robust device, ensuring that it will be alerted effectively and in real time when a patient suffers an epileptic attack.
  • the system has only one analysis channel, the patient does not take much time to put on and take off the electrode that will be sensing the EEG signal, in addition to being more comfortable for the patient. If the system must process stored information, it is able to process 30 minutes of information in 0.17 seconds, with 100% alerts of epileptic attacks, making the system fast and efficient.
  • Figure 7 shows a graph illustrating the peaks of the gamma values associated with an epileptic attack.

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Abstract

L'invention concerne un système d'alertes pour signalement d'épilepsie, caractérisé en ce qu'il comprend des moyens d'échantillonnage des signaux d'électroencéphalographie (EEG) chez une personne souffrant de crises d'épilepsie, reliés à un amplificateur de signaux biomédicaux et à une carte de traitement avec un microcontrôleur et une mémoire où sont stockés les échantillons d'EEG du patient; un module de filtrage passe-bas de signaux d'EEG à fréquence de coupure de 40 Hz et ordre de 20 pour éliminer le bruit; ledit microcontrôleur comprenant en outre des moyens de calcul du paramètre de dispersion du signal d'EEG filtré où sont ordonnées les données et calculées les valeurs des quantiles 25 et 75 du signal EEG filtré, qui sont associés au paramètre de dispersion défini précédemment; les valeurs de dispersion à l'intérieur du système étant introduites dans un module de filtrage passe-bas à fréquence de coupure de 0,5 Hz et un ordre de filtrage à facteur de 20; un analyseur qui analyse les signaux traités à la recherche de crises d'épilepsie en temps réel; ledit analyseur effectuant une comparaison échantillon par échantillon de chacune des valeurs de dispersion filtrées, avec deux seuils de détection correspondant à la survenue d'une crise d'épilepsie ou à la fin de celle-ci, des alertes visuelles et/ou sonores étant mises en oeuvre.
PCT/MX2018/000026 2018-03-14 2018-03-15 Système d'alertes pour signalement d'épilepsie Ceased WO2019177446A1 (fr)

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MXMX/A/2018/003153 2018-03-14
MX2018003153A MX2018003153A (es) 2018-03-14 2018-03-14 Sistema de alertas para el aviso de epilepsia.

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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6473639B1 (en) * 2000-03-02 2002-10-29 Neuropace, Inc. Neurological event detection procedure using processed display channel based algorithms and devices incorporating these procedures
US6931274B2 (en) * 1997-09-23 2005-08-16 Tru-Test Corporation Limited Processing EEG signals to predict brain damage
CA2968645A1 (fr) * 2015-01-06 2016-07-14 David Burton Systemes de surveillance pouvant etre mobiles et portes

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6931274B2 (en) * 1997-09-23 2005-08-16 Tru-Test Corporation Limited Processing EEG signals to predict brain damage
US6473639B1 (en) * 2000-03-02 2002-10-29 Neuropace, Inc. Neurological event detection procedure using processed display channel based algorithms and devices incorporating these procedures
CA2968645A1 (fr) * 2015-01-06 2016-07-14 David Burton Systemes de surveillance pouvant etre mobiles et portes

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