WIRELESS STROKE MONITORING

• US 20080294019A1
• Filed: 08/02/2007
• Published: 11/27/2008
• Est. Priority Date: 05/24/2007
• Status: Active Grant

• Current Assignee: Bao Tran
• Original Assignee: Bao Tran
• Inventors: Tran, Bao
• Granted Patent: US 8,750,971 B2
• Time in Patent Office: Days
• US Class Current: 600/301
• CPC Class Codes: A61B 2560/0412 Low-profile patch shaped housings
• CPC Class Codes: A61B 2560/045 with a separable interface unit, e.g. for communication
• CPC Class Codes: A61B 5/0006 ECG or EEG signals
• CPC Class Codes: A61B 5/002 Monitoring the patient using a local or closed circuit, e.g. in a room or building A61B5/0017 takes precedence
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/0059 using light, e.g. diagnosis by transillumination, diascopy, fluorescence A61B5/0093, A61B5/1455, A61B5/02416 and A61B5/0261 take precedence
• CPC Class Codes: A61B 5/02028 Determining haemodynamic parameters not otherwise provided for, e.g. cardiac contractility or left ventricular ejection fraction
• CPC Class Codes: A61B 5/02042 Determining blood loss or bleeding, e.g. during a surgical procedure
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/024 Detecting, measuring or recording pulse rate or heart rate A61B5/0205, A61B5/021 take precedence
• CPC Class Codes: A61B 5/026 Measuring blood flow A61B3/1233, A61B3/1241 take precedence
• CPC Class Codes: A61B 5/1075 for measuring dimensions by non-invasive methods, e.g. for determining thickness of tissue layer A61B8/0858 takes precedence
• CPC Class Codes: A61B 5/11 Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb for measuring pulse A61B5/02 ;A61B5/1038 takes precedence; motion detection to correct for motion artifacts in physiological signals A61B5/721
• CPC Class Codes: A61B 5/1113 Local tracking of patients, e.g. in a hospital or private home
• CPC Class Codes: A61B 5/1117 Fall detection
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/1128 using image analysis A61B5/1127 takes precedence
• CPC Class Codes: A61B 5/113 occurring during breathing
• CPC Class Codes: A61B 5/242 Detecting biomagnetic fields, e.g. magnetic fields produced by bioelectric currents
• CPC Class Codes: A61B 5/318 Heart-related electrical modalities, e.g. electrocardiography [ECG]
• CPC Class Codes: A61B 5/369 : Electroencephalography [EEG] devices for psychotechnics A61B5/16
• CPC Class Codes: A61B 5/389 : Electromyography [EMG]
• CPC Class Codes: A61B 5/4023 : Evaluating sense of balance
• CPC Class Codes: A61B 5/447 : specially adapted for aiding the prevention of ulcer or pressure sore development, i.e. before the ulcer or sore has developed
• CPC Class Codes: A61B 5/4875 : Hydration status, fluid retention of the body
• CPC Class Codes: A61B 5/4881 : Determining interstitial fluid distribution or content within body tissue measuring analytes in interstitial fluid in-vivo A61B5/1451
• CPC Class Codes: A61B 5/681 : Wristwatch-type devices
• CPC Class Codes: A61B 5/7203 : for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/7257 : using Fourier transforms
• CPC Class Codes: A61B 5/726 : using Wavelet transforms
• CPC Class Codes: A61B 5/7264 : Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61B 5/7267 : involving training the classification device
• CPC Class Codes: A61B 5/7275 : Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: A61B 5/7465 : Arrangements for interactive communication between patient and care services, e.g. by using a telephone network telemetry of measured physiological signal A61B5/0002
• CPC Class Codes: A61B 8/06 : Measuring blood flow measuring volume flow in general G01F, e.g. G01F1/66, G01F1/72; measuring speed of fluids in general G01P5/00
• CPC Class Codes: A61B 8/08 : Detecting organic movements or changes, e.g. tumours, cysts, swellings A61B8/02 - A61B8/06 take precedence
• CPC Class Codes: A61B 8/565 : involving data transmission via a network
• CPC Class Codes: G16H 10/60 : for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 15/00 : ICT specially adapted for medical reports, e.g. generation or transmission thereof
• CPC Class Codes: G16H 40/63 : for local operation

Abstract

A monitoring system includes wireless local area network (WLAN) transceivers operating as a Doppler radar to wirelessly detect the person'"'"'s heart parameter; and a processor coupled to the WLAN transceivers to determine a stroke attack.

Claims

1. A heart monitoring system for a patient, comprising:

an 802 protocol transmitter;

an 802 protocol receiver adapted to communicate with the 802 transmitter, the 802 protocol transmitter and receiver forming a Doppler radar to detect heartbeat motion on a chest; and

an analyzer coupled to one of the transmitter and receiver to determine heart attack or stroke attack.

2. The system of claim 1, wherein the analyzer identifies one patient from another based on heart rate signature characteristics.

3. The system of claim 1, comprising a sound transducer coupled to the transmitter to communicate audio over a POTS network or a PSTN network.

4. The system of claim 1, comprising one of:

an EMG detector, EEG detector, an EKG detector, an ECG detector, an electromagnetic detector, an ultrasonic detector, an optical detector, a Hidden Markov Model (HMM) recognizer, a dynamic time warp (DTW) recognizer, a neural network, a fuzzy logic engine, a Bayesian network.

5. The system of claim 1, wherein the appliance monitors patient movement.

6. The system of claim 1, comprising an in-door positioning system coupled to one or more mesh network appliances to provide location information.

7. The system of claim 1, comprising a call center coupled to the analyzer to provide a human response.

8. The system of claim 1, wherein the 802.11 protocol comprises one of:

802.11 protocol, 802.15 protocol, 802.16 protocol, WiFi protocol, WiMAX protocol.

9. The system of claim 1, comprising a wireless router coupled to the mesh network and wherein the wireless router comprises one of:

802.11 router, 802.16 router, WiFi router, WiMAX router, Bluetooth router, X10 router.

10. The system of claim 1, comprising a mesh network appliance coupled to a power line to communicate X10 data to and from the mesh network.

11. The system of claim 1, wherein the transmitter transmits voice.

12. The system of claim 1, wherein the analyzer determines one of:

total body water, compartmentalization of body fluids, cardiac monitoring, blood flow, skinfold thickness, dehydration, blood loss, wound monitoring, ulcer detection, deep vein thrombosis, hypovolemia, hemorrhage, blood loss, heart attack, stroke attack.

13. The system of claim 1, comprising code to store and analyze patient information including vital sign rate of change, medicine taking habits, eating and drinking habits, sleeping habits, or exercise habits.

14. The system of claim 1, comprising one or more bioelectric contacts coupleable to the patient from one of:

a patch, a wristwatch, a band, a wristband, a chest band, a leg band, a sock, a glove, a foot pad, a head-band, an ear-clip, an ear phone, a shower-cap, an armband, an ear-ring, eye-glasses, sun-glasses, a belt, a sock, a shirt, a garment, a jewelry, a bed spread, a pillow cover, a pillow, a mattress, a blanket, each having one or more sensors in communication with the wireless mesh network.

15. The system of claim 1, wherein the analyzer detects weakness in left half and right half of patient body;

detects walking pattern for loss of balance or coordination;

asks user to move hands/feet in a predetermined pattern;

checking whether the person experienced dizziness or headache;

displays a text image and ask the person to read back the text image one eye at a time;

using a speech recognizer to detect confusion, trouble speaking or understanding; and

querying the person for body numbness.

16. The system of claim 16, comprising calibrating Doppler radar signal with an actual blood pressure during a training phase to develop a model and using the model with Doppler radar signal during an operational phase to estimate continuous blood pressure.

17. A monitoring system for a person, comprising:

wireless local area network (WLAN) transceivers operating as a Doppler radar to wirelessly detect the person'"'"'s heart parameter; and

a processor coupled to the WLAN transceivers to determine a stroke attack.

18. The system of claim 17, wherein the processor detects weakness in left half and right half of patient body;

detects walking pattern for loss of balance or coordination;

asks user to move hands/feet in a predetermined pattern;

checking whether the person experienced dizziness or headache;

displays a text image and ask the person to read back the text image one eye at a time;

using a speech recognizer to detect confusion, trouble speaking or understanding; and

querying the person for body numbness.

19. The system of claim 17, wherein the processor determines one person from another using each person'"'"'s heartbeat signature.

20. The system of claim 17, wherein the processor analyzes patient information including one of:

vital sign rate of change, medicine taking habits, eating and drinking habits, sleeping habits, exercise habits.

21. The system of claim 17, wherein the processor detects weakness in left half and right half of patient body;

detects walking pattern for loss of balance or coordination;

asks user to move hands/feet in a predetermined pattern;

checking whether the person experienced dizziness or headache;

displays a text image and ask the person to read back the text image one eye at a time;

using a speech recognizer to detect confusion, trouble speaking or understanding; and

querying the person for body numbness.

22. The system of claim 17, comprising calibrating Doppler radar signal with an actual blood pressure during a training phase to develop a model and using the model with Doppler radar signal during an operational phase to estimate continuous blood pressure.