CN105520731A - HBC (human body communication) based wearable type medical equipment capable of preventing epileptic seizure - Google Patents
HBC (human body communication) based wearable type medical equipment capable of preventing epileptic seizure Download PDFInfo
- Publication number
- CN105520731A CN105520731A CN201610034365.5A CN201610034365A CN105520731A CN 105520731 A CN105520731 A CN 105520731A CN 201610034365 A CN201610034365 A CN 201610034365A CN 105520731 A CN105520731 A CN 105520731A
- Authority
- CN
- China
- Prior art keywords
- signal
- input
- epilepsy
- main control
- eeg
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/369—Electroencephalography [EEG]
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/0002—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network
- A61B5/0004—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network characterised by the type of physiological signal transmitted
- A61B5/0006—ECG or EEG signals
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/40—Detecting, measuring or recording for evaluating the nervous system
- A61B5/4076—Diagnosing or monitoring particular conditions of the nervous system
- A61B5/4094—Diagnosing or monitoring seizure diseases, e.g. epilepsy
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
- A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
- A61B5/6813—Specially adapted to be attached to a specific body part
- A61B5/6814—Head
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7225—Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/74—Details of notification to user or communication with user or patient ; user input means
- A61B5/7405—Details of notification to user or communication with user or patient ; user input means using sound
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/74—Details of notification to user or communication with user or patient ; user input means
- A61B5/746—Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms
Landscapes
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Engineering & Computer Science (AREA)
- Molecular Biology (AREA)
- Animal Behavior & Ethology (AREA)
- Veterinary Medicine (AREA)
- Biophysics (AREA)
- Pathology (AREA)
- Public Health (AREA)
- Biomedical Technology (AREA)
- Heart & Thoracic Surgery (AREA)
- Medical Informatics (AREA)
- General Health & Medical Sciences (AREA)
- Surgery (AREA)
- Physics & Mathematics (AREA)
- Physiology (AREA)
- Neurology (AREA)
- Neurosurgery (AREA)
- Signal Processing (AREA)
- Psychiatry (AREA)
- Power Engineering (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Psychology (AREA)
- Computer Networks & Wireless Communication (AREA)
- Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)
- Measuring And Recording Apparatus For Diagnosis (AREA)
Abstract
HBC (human body communication) based wearable type medical equipment capable of preventing epileptic seizure is disclosed. The HBC based wearable type medical equipment comprises a main control computer, wherein the first input end of the main control computer is connected with the output end of a signal transmission system; the first input end of the signal transmission system is matched with a human head; the output end of the main control computer is connected with the input end of a controller; the output end of the controller is connected with the input end of a switch module; the output end of the switch module is connected with the first input end of an alarm apparatus; and the main control computer, the signal transmission system and the second input end of the alarm apparatus are connected with three output ends of a DC/DC multi-path output power supply module respectively. The signal transmission system takes the human head as a communication path for transmitting electroencephalogram data to form a HBC-technology-based BAN communication link; the wearable type medical equipment can give out sound and vibration alarms before epileptic seizure is occurring on a patient so as to remind the patient to take necessary preventive measures; and the wearable type medical equipment is convenient to wear and capable of reminding the patient in time.
Description
Technical field
The present invention relates to human body communication (HumanBodyCommunication, HBC) and biomedicine signals technical field, be specifically related to a kind of body-worn medical equipment of the prevention epilepsy based on HBC.
Background technology
HBC is a kind of short-distance wireless communication technology carried out human peripheral or inside of human body, human body is utilized to transmit data as communication path, wireless communication technology frontier body area network (BodyAreaNetwork, BAN) in, an application scenarios of most prospect, has vast potential for future development at daily health caring and medical application fields.Epilepsy is as one paroxysmal brain neuron paradoxical discharge disease, and electroencephalogram is epilepsy prevention and the most effective auxiliary diagnostic tool of diagnosis.Research shows, epilepsy is occur suddenly in the change of stage of attack and interparoxysmal electroencephalogram, it is characterized in that: paroxysmal high-voltage slow wave, sharp wave, spike, or with the fast wave of low wave amplitude, this change can appear at the Epileptic seizure behavior preictal several seconds.Can occur significantly to change this feature according to epileptic's EEG signals before outbreak and can carry out early prediction to epilepsy.
The sensitivity of Duffing chaotic oscillator system to initial condition may be used for detecting weak periodic signal or chaotic signal; EEG signals is extensively proved chaotic time sequence; Epileptic attack brain electricity seasonal effect in time series LyaPunov index decreased, namely normal and epileptic electroencephalogram (eeg) time series has different chaotic property; Duffing oscillator has that to distinguish the ability of epilepsy EEG and normal EEG be principle based on Chaotic Synchronous and basis.
The fast development of Digital Signal Processing and the extensive use of computer, and wireless communication technology enters and contacts field BAN the most closely with human individual in recent years, the reaching its maturity of HBC technology in this field, the Prevention Research for epileptics has started brand-new situation.
Epilepsy has broken property, if now patient is occupied in certain risky operation (as driving a car), is then easy to be subject to unexpected injury, and this causes the disabled even dead main cause of epileptic.On the other hand due to the repeatability outbreak that epilepsy is long-term, and patient oneself cannot look-ahead, and this not only makes patient body suffer misery, and causes its spirit and mental maladjustment to a certain extent.Treatment means mainly Drug therapy and excision focus at present to epileptics.Although the number of times that these two kinds of methods can reduce epilepsy effectively suppresses epilepsy even completely, alleviate the misery of epileptic, cannot realize before outbreak, realizing prompting to epileptic.
Summary of the invention
In order to overcome the shortcoming of above-mentioned prior art, the object of the present invention is to provide a kind of body-worn medical equipment of the prevention epilepsy based on HBC, prevention warning prompt sound and vibrations can be sent to patient before epileptic seizure, remind patient to make the wearable armarium of necessary preventive measure, have wear conveniently, remind in time, the accurate advantage such as safety.
In order to achieve the above object, the technical scheme that the present invention takes is:
A kind of body-worn medical equipment of the prevention epilepsy based on HBC, comprise main control computer 101, the first input end of main control computer 101 is connected with the outfan of signal transmission system 102, first input end and the human body head 107 of signal transmission system 102 coordinate, the outfan of main control computer 101 is connected with the input of controller 104, the outfan of controller 104 is connected with the input of switch module 105, the outfan of switch module 105 is connected with the first input end of warning devices 103, second input of main control computer 101, second input of signal transmission system 102, second input of warning devices 103 is connected with three outfans of DC/DC multiple-output electric power module 106 respectively.
Described signal transmission system 102 utilizes human body head 107 as communication path transmission EEG signals data, form a BAN communication link based on HBC technology, signal transmission system 102 comprises signal acquisition module 1021, input and the human body head 107 of signal acquisition module 1021 coordinate, the outfan of signal acquisition module 1021 is connected with the input of Signal-regulated kinase 1022, the input of conditioning module 1022 is connected with the input of signal transmitting module 1023, the outfan of signal transmitting module 1023 is connected with the input of the signal processing module 1011 of main control computer 101.
Described main control computer 101 comprises signal processing module 1011, the outfan of signal processing module 1011 is connected with the input of epilepsy early warning detection system 1012, and the outfan of epilepsy early warning detection system 1012 is connected with the input of controller 104.
Described warning devices 103 comprises buzzer 1031 and electromagnetic shaker 1032, and buzzer 1031 is connected with the outfan of switch module 105 respectively with the input of electromagnetic shaker 1032.
Described epilepsy early warning detection system 1012 utilizes the chaotic characteristic of Duffing chaotic oscillator to distinguish epilepsy and normal EEG feature, and then realizing the function of prevention epilepsy, ultimate principle is utilize the chaotic characteristic of epilepsy EEG to be weaker than power that the chaotic characteristic of normal EEG and Duffing chaotic oscillator can distinguish two kinds of EEG chaotic characteristics;
The model of Duffing chaotic oscillator is such as formula shown in (1):
Wherein μ is occupy ratio ,-x+x
3for nonlinear restoring force, fcos (ω t) is built-in signal, and y (t) is input signal,
Built-in signal fcos (ω t) and the input of input signal y (t) as Duffing chaotic oscillator, the output of Duffing chaotic oscillator is output signal x (t) and output signal derivative x'(t), the plane phasor utilizing Lyapunov exponential sum Duffing oscillator to export is as the quantizating index describing chaos time sequence chaos degree.
Described epilepsy early warning detection system 1012 utilize the chaotic characteristic of epilepsy EEG to be weaker than power that the chaotic characteristic of normal EEG and Duffing chaotic oscillator can distinguish two kinds of EEG chaotic characteristics, concrete steps are:
The first step, the amplitude f and the frequencies omega that adjust built-in signal fcos (ω t) when not having input signal y (t) make Duffing chaotic oscillator be in periodic state and chaos state, now obtain the output of Duffing chaotic oscillator for output signal x (t) and output signal derivative x'(t), formation cycle phase-plane diagram and chaos phase-plane diagram, the amplitude f determined using chaos phase-plane diagram and frequencies omega perform next step task as the systematic parameter of epilepsy early warning detection system 1012
Second step, is sent to Duffing chaotic oscillator using epilepsy EEG chaos time sequence as input signal y (t) and obtains output signal x (t) and output signal derivative x'(t), form chaos phase-plane diagram,
3rd step, is sent to Duffing chaotic oscillator using normal EEG chaos time sequence as input signal y (t) and obtains output signal x (t) and output signal derivative x'(t), form chaos phase-plane diagram.
The body-worn medical equipment of described a kind of prevention epilepsy based on HBC, early warning and monitoring workflow is as follows:
The first step, based on HBC technology signal transmission system 102 the EEG caught from human body head 107 emission electrode by acquisition module 1021, Signal-regulated kinase 1022 through amplifying, after the means of filtering by HBC band transmissions to the signal processing module 1011 in main control computer 101
Second step, main control computer 101 flows to epilepsy early warning detection system 1012 by collecting electrode sky bundle of lines from the EEG signal that signal transmitting module 1023 obtains from signal processing module 1011,
3rd step, the data of epilepsy EEG and normal EEG are analyzed by epilepsy early warning detection system 1012, analysis result are fed back to main control computer 101, and main control computer 101 sends instruction according to analysis result to controller 104,
4th step, controller 104 sends instruction according to the instruction of main control computer 101 to switch module 105,
5th step, switch module 105 starts warning devices 103 according to the instruction of controller 104, enters running status, complete the early warning Detection task of epilepsy after the buzzer 1031 in dynamic warning devices 103 and electromagnetic shaker 1032 are energized.
Beneficial effect of the present invention is: the present invention can send prevention information epilepsy forward direction patient.As long as this equipment is worn on the head by epileptic, sound prompting can be heard while experiencing vibrations before epilepsy, to take certain preventive measure in time in order to avoid be subject to unexpected injury.Have and wear conveniently, prevent the features such as effective, form more perfect epileptics prophylactic treatment system together with existing epilepsy therapy means, decrease physical pain and the Mental health problem of patient.
Accompanying drawing explanation
Fig. 1 is structural representation of the present invention.
Fig. 2 is the structural representation of epilepsy early warning detection system 1012 of the present invention.
Fig. 3 is the cycle phase-plane diagram that epilepsy early warning detection system 1012 of the present invention exports when not having extraneous input.
Fig. 4 is the chaos phase-plane diagram that epilepsy early warning detection system 1012 of the present invention exports when not having extraneous input.
Fig. 5 is the epilepsy early warning detection system 1012 of the present invention chaos phase-plane diagram that system exports when adding the input of epilepsy EEG chaos time sequence.
Fig. 6 is the epilepsy early warning detection system 1012 of the present invention chaos phase-plane diagram that system exports when adding the input of normal EEG chaos time sequence.
Fig. 7 is the body-worn medical equipment workflow schematic diagram of the prevention epilepsy based on HBC of the present invention.
Detailed description of the invention
Below in conjunction with drawings and Examples, the present invention is further elaborated.
As shown in Figure 1, a kind of body-worn medical equipment of the prevention epilepsy based on HBC, comprise main control computer 101, the first input end of main control computer 101 is connected with the outfan of signal transmission system 102, first input end and the human body head 107 of signal transmission system 102 coordinate, the outfan of main control computer 101 is connected with the input of controller 104, the outfan of controller 104 is connected with the input of switch module 105, the outfan of switch module 105 is connected with the first input end of warning devices 103, second input of main control computer 101, second input of signal transmission system 102, second input of warning devices 103 is connected with three outfans of DC/DC multiple-output electric power module 106 respectively.
Described signal transmission system 102 utilizes human body head 107 as communication path transmission EEG signals data, form a BAN communication link based on HBC technology, signal transmission system 102 comprises signal acquisition module 1021, input and the human body head 107 of signal acquisition module 1021 coordinate, the outfan of signal acquisition module 1021 is connected with the input of Signal-regulated kinase 1022, the input of conditioning module 1022 is connected with the input of signal transmitting module 1023, the outfan of signal transmitting module 1023 is connected with the input of the signal processing module 1011 of main control computer 101.
Described main control computer 101 comprises signal processing module 1011, the outfan of signal processing module 1011 is connected with the input of epilepsy early warning detection system 1012, and the outfan of epilepsy early warning detection system 1012 is connected with the input of controller 104.
Described warning devices 103 comprises buzzer 1031 and electromagnetic shaker 1032, and buzzer 1031 is connected with the outfan of switch module 105 respectively with the input of electromagnetic shaker 1032.
After switch module 105 starts, the faint brain electric analoging signal that signal acquisition module 1021 captures the brain electrode be attached on human body head 107 nurses one's health into through Signal-regulated kinase 1022 signal that signal transmitting module 1023 can identify, signal transmitting module 1023 sends to signal processing module 1011 EEG signals, through the link that signal processing module 1011 is sampled, modulates, quantizes, encodes, reconciles and preserved, convert analogue signal to digital signal.
As shown in Figure 2, described epilepsy early warning detection system 1012 utilizes the chaotic characteristic of Duffing chaotic oscillator to distinguish epilepsy and normal EEG feature, and then realizing the function of prevention epilepsy, ultimate principle is utilize the chaotic characteristic of epilepsy EEG to be weaker than power that the chaotic characteristic of normal EEG and Duffing chaotic oscillator can distinguish two kinds of EEG chaotic characteristics;
The model of Duffing chaotic oscillator is such as formula shown in (1):
Wherein μ is occupy ratio ,-x+x
3for nonlinear restoring force, fcos (ω t) is built-in signal, and y (t) is input signal,
Built-in signal fcos (ω t) and the input of input signal y (t) as Duffing chaotic oscillator, the output of Duffing chaotic oscillator is output signal x (t) and output signal derivative x'(t), the plane phasor utilizing Lyapunov exponential sum Duffing oscillator system to export is as the quantizating index describing chaos time sequence chaos degree, LyaPunov index is the important parameter describing chaotic oscillator Nonlinear dynamic behaviors, the average index speed that Lyapunov index definition adjacent orbit is restrained or dispersed in phase space, positive Lyapunov exponential representation system table reveals chaotic dynamics behavior, namely any system at least comprising a positive LyaPunov index is all considered to chaos, negative Lyapunov exponential representation system does not have chaotic dynamics behavior, and phase track shrinks in other words, and Lyapunov index represents that system is along track moving with the speed lower than index when being zero, the value of index represents that system is uncertain in this time range, phase plane method is one method of geometry intuitively, study a kind of graphical method of second order autonomous system with the track of relation between the sign variable be plotted on right-angle plane coordinate and rate of change thereof, this method is used for the motion of the large nonlinear systems of analysis one, it is applicable to the motion in one dimension of system, the kinestate of etching system when any point represents this in phase plane, be called phase point, the track that phase point consecutive variations is formed then describes the equation of motion of system, be called phase orbit, this figure is also referred to as phasor, on phase-plane diagram, the various global nature of system can be found out significantly according to phase path race.
Described epilepsy early warning detection system 1012 utilize the chaotic characteristic of epilepsy EEG to be weaker than power that the chaotic characteristic of normal EEG and Duffing chaotic oscillator can distinguish two kinds of EEG chaotic characteristics, concrete steps are:
The first step, the amplitude f and the frequencies omega that adjust built-in signal fcos (ω t) when not having input signal y (t) make Duffing chaotic oscillator be in periodic state and chaos state, now obtain the output of Duffing chaotic oscillator for output signal x (t) and output signal derivative x'(t), the cycle phase-plane diagram formed and chaos phase-plane diagram are as shown in Figure 3 and Figure 4, the amplitude f determined using chaos phase-plane diagram and frequencies omega perform next step task as the systematic parameter of epilepsy early warning detection system 1012
Second step, epilepsy EEG chaos time sequence is sent to Duffing chaotic oscillator as input signal y (t) and obtains output signal x (t) and output signal derivative x'(t), the chaos phase-plane diagram formed as shown in Figure 5, the LyaPunov index of input signal y (t), output signal x (t) is 0.0033,0.0623 respectively
3rd step, normal EEG chaos time sequence is sent to Duffing chaotic oscillator as input signal y (t) and obtains output signal x (t) and output signal derivative x'(t), as shown in Figure 6, the LyaPunov index of input signal y (t), output signal x (t) is 0.2355,0.1726 to the chaos phase-plane diagram formed respectively.
With reference to Fig. 7, the body-worn medical equipment of described a kind of prevention epilepsy based on HBC, early warning and monitoring workflow is as follows:
The first step, based on HBC technology signal transmission system 102 the EEG caught from human body head 107 emission electrode by acquisition module 1021, Signal-regulated kinase 1022 through amplifying, after the means of filtering by HBC band transmissions to the signal processing module 1011 in main control computer 101
Second step, main control computer 101 flows to epilepsy early warning detection system 1012 by collecting electrode sky bundle of lines from the EEG signal that signal transmitting module 1023 obtains from signal processing module 1011,
3rd step, the data of epilepsy EEG and normal EEG are analyzed by epilepsy early warning detection system 1012, analysis result are fed back to main control computer 101, and main control computer 101 sends instruction according to analysis result to controller 104,
4th step, controller 104 sends instruction according to the instruction of main control computer 101 to switch module 105,
5th step, switch module 105 starts warning devices 103 according to the instruction of controller 104, enters running status, complete the early warning Detection task of epilepsy after the buzzer 1031 in dynamic warning devices 103 and electromagnetic shaker 1032 are energized.
Claims (7)
1. the body-worn medical equipment based on the prevention epilepsy of HBC, comprise main control computer (101), it is characterized in that: the first input end of main control computer (101) is connected with the outfan of signal transmission system (102), the first input end of signal transmission system (102) and human body head (107) coordinate, the outfan of main control computer (101) is connected with the input of controller (104), the outfan of controller (104) is connected with the input of switch module (105), the outfan of switch module (105) is connected with the first input end of warning devices (103), second input of main control computer (101), second input of signal transmission system (102), second input of warning devices (103) is connected with three outfans of DC/DC multiple-output electric power module (106) respectively.
2. the body-worn medical equipment of a kind of prevention epilepsy based on HBC according to claim 1, it is characterized in that: described signal transmission system (102) utilizes human body head (107) as communication path transmission EEG signals data, form a BAN communication link based on HBC technology, signal transmission system (102) comprises signal acquisition module (1021), the input of signal acquisition module (1021) and human body head (107) coordinate, the outfan of signal acquisition module (1021) is connected with the input of Signal-regulated kinase (1022), the input of conditioning module (1022) is connected with the input of signal transmitting module (1023), the outfan of signal transmitting module (1023) is connected with the input of the signal processing module (1011) of main control computer (101).
3. the body-worn medical equipment of a kind of prevention epilepsy based on HBC according to claim 1, it is characterized in that: described main control computer (101) comprises signal processing module (1011), the outfan of signal processing module (1011) is connected with the input of epilepsy early warning detection system (1012), and the outfan of epilepsy early warning detection system (1012) is connected with the input of controller (104).
4. the body-worn medical equipment of a kind of prevention epilepsy based on HBC according to claim 1, it is characterized in that: described warning devices (103) comprises buzzer (1031) and electromagnetic shaker (1032), buzzer (1031) is connected with the outfan of switch module (105) respectively with the input of electromagnetic shaker (1032).
5. the body-worn medical equipment of a kind of prevention epilepsy based on HBC according to claim 3, it is characterized in that: described epilepsy early warning detection system (1012) utilizes the chaotic characteristic of Duffing chaotic oscillator to distinguish epilepsy and normal EEG feature, and then realizing the function of prevention epilepsy, ultimate principle is utilize the chaotic characteristic of epilepsy EEG to be weaker than power that the chaotic characteristic of normal EEG and Duffing chaotic oscillator can distinguish two kinds of EEG chaotic characteristics;
The model of Duffing chaotic oscillator is such as formula shown in (1):
Wherein μ is occupy ratio ,-x+x
3for nonlinear restoring force, fcos (ω t) is built-in signal, and y (t) is input signal,
Built-in signal fcos (ω t) and the input of input signal y (t) as Duffing chaotic oscillator, the output of Duffing chaotic oscillator is output signal x (t) and output signal derivative x'(t), the plane phasor utilizing Lyapunov exponential sum Duffing oscillator to export is as the quantizating index describing chaos time sequence chaos degree.
6. the body-worn medical equipment of a kind of prevention epilepsy based on HBC according to claim 3, it is characterized in that: described epilepsy early warning detection system (1012) utilize the chaotic characteristic of epilepsy EEG to be weaker than power that the chaotic characteristic of normal EEG and Duffing chaotic oscillator can distinguish two kinds of EEG chaotic characteristics, concrete steps are:
The first step, the amplitude f and the frequencies omega that adjust built-in signal fcos (ω t) when not having input signal y (t) make Duffing chaotic oscillator be in periodic state and chaos state, now obtain the output of Duffing chaotic oscillator for output signal x (t) and output signal derivative x'(t), formation cycle phase-plane diagram and chaos phase-plane diagram, the amplitude f determined using chaos phase-plane diagram and frequencies omega perform next step task as the systematic parameter of epilepsy early warning detection system (1012)
Second step, is sent to Duffing chaotic oscillator using epilepsy EEG chaos time sequence as input signal y (t) and obtains output signal x (t) and output signal derivative x'(t), form chaos phase-plane diagram,
3rd step, is sent to Duffing chaotic oscillator using normal EEG chaos time sequence as input signal y (t) and obtains output signal x (t) and output signal derivative x'(t), form chaos phase-plane diagram.
7. the body-worn medical equipment of a kind of prevention epilepsy based on HBC according to claim 1, it is characterized in that, early warning and monitoring workflow is as follows:
The first step, signal transmission system (102) based on HBC technology passes through HBC band transmissions to the signal processing module (1011) in main control computer (101) the EEG caught from human body head (107) emission electrode by acquisition module (1021), Signal-regulated kinase (1022) after the means of amplification, filtering
Second step, main control computer (101) flows to epilepsy early warning detection system (1012) by collecting electrode sky bundle of lines from the EEG signal that signal transmitting module (1023) obtains from signal processing module (1011)
3rd step, the data of epilepsy EEG and normal EEG are analyzed by epilepsy early warning detection system (1012), analysis result is fed back to main control computer (101), main control computer (101) sends instruction according to analysis result to controller (104)
4th step, controller (104) sends instruction according to the instruction of main control computer (101) to switch module (105),
5th step, switch module (105) starts warning devices (103) according to the instruction of controller (104), enter running status after buzzer (1031) in warning devices (103) and electromagnetic shaker (1032) energising, complete the early warning Detection task of epilepsy.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610034365.5A CN105520731A (en) | 2016-01-19 | 2016-01-19 | HBC (human body communication) based wearable type medical equipment capable of preventing epileptic seizure |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610034365.5A CN105520731A (en) | 2016-01-19 | 2016-01-19 | HBC (human body communication) based wearable type medical equipment capable of preventing epileptic seizure |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN105520731A true CN105520731A (en) | 2016-04-27 |
Family
ID=55763496
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610034365.5A Pending CN105520731A (en) | 2016-01-19 | 2016-01-19 | HBC (human body communication) based wearable type medical equipment capable of preventing epileptic seizure |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105520731A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106491126A (en) * | 2016-11-30 | 2017-03-15 | 山东大学 | A kind of device of dynamic early-warning epileptic attack |
| CN107616793A (en) * | 2017-09-18 | 2018-01-23 | 电子科技大学 | A kind of eeg monitoring device and method with epileptic seizure prediction function |
| CN108498069A (en) * | 2017-02-23 | 2018-09-07 | 光宝电子(广州)有限公司 | Wearable electronic device and its emergency help method |
| CN111643067A (en) * | 2020-06-08 | 2020-09-11 | 深圳迈拓数码科技有限公司 | HBC-based wearable medical equipment for preventing epileptic seizure |
| CN113017653A (en) * | 2021-03-15 | 2021-06-25 | 西安交通大学 | Steady-state visual evoked potential identification system and method based on chaos detection principle |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101170943A (en) * | 2005-05-06 | 2008-04-30 | 皇家飞利浦电子股份有限公司 | Wireless medical monitoring device |
| CN101627906A (en) * | 2009-07-30 | 2010-01-20 | 上海交通大学 | Portable multiparameter health monitoring system |
| CN101848669A (en) * | 2007-09-07 | 2010-09-29 | 埃姆申塞公司 | Integrated sensor headset |
| CN102393874A (en) * | 2011-07-01 | 2012-03-28 | 山东大学 | Electroencephalogram spike wave frequency based early-alarming method and device |
| CN103462611A (en) * | 2013-09-09 | 2013-12-25 | 中国科学院深圳先进技术研究院 | Wearable epilepsy monitoring device and system |
| CN103598873A (en) * | 2013-09-27 | 2014-02-26 | 四川大学 | Physiological signal intelligent monitoring system based on self-adaptation wireless sensor network |
| CN103845046A (en) * | 2012-11-30 | 2014-06-11 | 索尼公司 | Biological information measurement apparatus, biological information measurement system, biological information measurement method, and program |
| CN104138662A (en) * | 2013-05-10 | 2014-11-12 | 索尼公司 | Image display device and image display method |
| CN105078448A (en) * | 2014-05-16 | 2015-11-25 | 上海理工大学 | Electroencephalogram acquisition and wireless transmission device |
-
2016
- 2016-01-19 CN CN201610034365.5A patent/CN105520731A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101170943A (en) * | 2005-05-06 | 2008-04-30 | 皇家飞利浦电子股份有限公司 | Wireless medical monitoring device |
| CN101848669A (en) * | 2007-09-07 | 2010-09-29 | 埃姆申塞公司 | Integrated sensor headset |
| CN101627906A (en) * | 2009-07-30 | 2010-01-20 | 上海交通大学 | Portable multiparameter health monitoring system |
| CN102393874A (en) * | 2011-07-01 | 2012-03-28 | 山东大学 | Electroencephalogram spike wave frequency based early-alarming method and device |
| CN103845046A (en) * | 2012-11-30 | 2014-06-11 | 索尼公司 | Biological information measurement apparatus, biological information measurement system, biological information measurement method, and program |
| CN104138662A (en) * | 2013-05-10 | 2014-11-12 | 索尼公司 | Image display device and image display method |
| CN103462611A (en) * | 2013-09-09 | 2013-12-25 | 中国科学院深圳先进技术研究院 | Wearable epilepsy monitoring device and system |
| CN103598873A (en) * | 2013-09-27 | 2014-02-26 | 四川大学 | Physiological signal intelligent monitoring system based on self-adaptation wireless sensor network |
| CN105078448A (en) * | 2014-05-16 | 2015-11-25 | 上海理工大学 | Electroencephalogram acquisition and wireless transmission device |
Non-Patent Citations (1)
| Title |
|---|
| 袁野: "癫痫脑电的非线性方法分析", 《中国博士学位论文全文数据库 医药卫生科技辑》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106491126A (en) * | 2016-11-30 | 2017-03-15 | 山东大学 | A kind of device of dynamic early-warning epileptic attack |
| CN106491126B (en) * | 2016-11-30 | 2019-03-08 | 山东大学 | A kind of device of dynamic early-warning epileptic attack |
| CN108498069A (en) * | 2017-02-23 | 2018-09-07 | 光宝电子(广州)有限公司 | Wearable electronic device and its emergency help method |
| CN107616793A (en) * | 2017-09-18 | 2018-01-23 | 电子科技大学 | A kind of eeg monitoring device and method with epileptic seizure prediction function |
| CN111643067A (en) * | 2020-06-08 | 2020-09-11 | 深圳迈拓数码科技有限公司 | HBC-based wearable medical equipment for preventing epileptic seizure |
| CN113017653A (en) * | 2021-03-15 | 2021-06-25 | 西安交通大学 | Steady-state visual evoked potential identification system and method based on chaos detection principle |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105520731A (en) | HBC (human body communication) based wearable type medical equipment capable of preventing epileptic seizure | |
| Acharya et al. | Automated seizure prediction | |
| Chen et al. | A unified framework and method for EEG-based early epileptic seizure detection and epilepsy diagnosis | |
| Nallathambi et al. | Integrate and fire pulse train automaton for QRS detection | |
| CN102512153B (en) | Non-contact electrocardio monitoring mobile terminal and electrocardio monitoring method | |
| CN112754431A (en) | Respiration and heartbeat monitoring system based on millimeter wave radar and lightweight neural network | |
| AlSharabi et al. | A DWT-entropy-ANN based architecture for epilepsy diagnosis using EEG signals | |
| US20200382286A1 (en) | System and method for smart, secure, energy-efficient iot sensors | |
| Lu et al. | Detection and classification of epileptic EEG signals by the methods of nonlinear dynamics | |
| Bose et al. | EEG signal analysis for Seizure detection using Discrete Wavelet Transform and Random Forest | |
| Dehzangi et al. | Towards multi-modal wearable driver monitoring: Impact of road condition on driver distraction | |
| CN108836324A (en) | A kind of fatigue driving method for early warning and system based on EEG signals monitoring | |
| Jiang et al. | Denoising and chaotic feature extraction of electrocardial signals for driver fatigue detection by Kolmogorov entropy | |
| Prince et al. | Brain machine interface using Emotiv EPOC to control robai cyton robotic arm | |
| Kambhampati et al. | Unified framework for triaxial accelerometer‐based fall event detection and classification using cumulants and hierarchical decision tree classifier | |
| Majumder et al. | A real-time cardiac monitoring using a multisensory smart IoT system | |
| Sakib et al. | A feedforward neural network for drone accident prediction from physiological signals | |
| Albalawi et al. | Single-channel real-time drowsiness detection based on electroencephalography | |
| Koohestani et al. | Drivers performance evaluation using physiological measurement in a driving simulator | |
| Ravi et al. | Real time driver fatigue detection and smart rescue system | |
| EP3897383A1 (en) | Stroke detection sensor | |
| Najmaei et al. | Human factors for robot safety assessment | |
| Deepa et al. | EEG feature extraction and classification of Alzheimer’s disease using support vector machine classifier | |
| Chowdhury et al. | Brain controlled assistive buzzer system for physically impaired people | |
| Chen et al. | Non-contact monitoring of fatigue driving using FMCW millimeter wave radar |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20160427 |