Wireless stroke monitoring

• US 8,750,971 B2
• Filed: 08/02/2007
• Issued: 06/10/2014
• Est. Priority Date: 05/24/2007
• Status: Active Grant

• Current Assignee: Bao Tran
• Original Assignee: Bao Tran
• Inventors: Tran, Bao
• Primary Examiner(s): Heller, Tammie K
• Application Number:
  US11/832,735
• Publication Number:
  US 20080294019A1
• Time in Patent Office: 2,504 Days
• Field of Search: 600/503, 600/504, 600/509
• US Class Current: 600/509
• 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 heart monitoring system for a patient includes 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 connected to one of the transmitter and receiver. The analyzer calibrates a Doppler radar signal with an actual blood pressure during a training phase to develop a model. The analyzer is configured to use the model with the Doppler radar signal during an operational phase to estimate blood pressure.

Claims

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

a Doppler radar formed with an IEEE 802 protocol transmitter and an IEEE 802 protocol receiver to detect heartbeat motion on a chest of the patient, wherein frequency waves are transmitted by the IEEE 802 protocol transmitter to the chest of the patient located a fixed distance from the transmitter, reflected frequency waves are received by the IEEE protocol 802 receiver from the chest of the patient, and the IEEE 802 protocol receiver is configured to generate a Doppler radar signal based on the reflected frequency waves; and

an analyzer coupled to one of the transmitter and receiver including computer readable code configured to perform Doppler operations using the IEEE 802 protocol transmitter and receiver, wherein the analyzer calibrates a training Doppler radar signal with a training blood pressure during a training phase to develop a model and wherein the analyzer is configured to use the model with the Doppler radar signal during an operational phase to estimate blood pressure.

2. The system of claim 1, wherein the analyzer identifies one patient from another based on the Doppler radar signal.

3. The system of claim 1, further comprising a sound transducer coupled to the transmitter and a telephone connection to communicate audio over a plain old telephone service (POTS) network or a public switched telephone network (PSTN) network.

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

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, or a Bayesian network.

5. The system of claim 1, wherein the analyzer monitors patient movement using triangulation.

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

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

8. The system of claim 1, wherein the IEEE 802 protocol transmitter communicates an IEEE 802 protocol and wherein the IEEE 802 protocol comprises one of:

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

9. The system of claim 1, further comprising a wireless router coupled to the IEEE 802 protocol transmitter and wherein the wireless router comprises one of:

an 802.11 router, an 802.16 router, a WiFi router, a WiMAX router, a BLUETOOTH router, or an X10 router.

10. The system of claim 1, further comprising a mesh network appliance coupled to a power line to communicate X10 data to and from the IEEE 802 protocol transmitter.

11. The system of claim 1, wherein the IEEE 802 protocol 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, or stroke attack.

13. The system of claim 1, wherein the analyzer stores and analyzes 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, further 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 shirt, a garment, jewelry, a bed spread, a pillow cover, a pillow, a mattress, or a blanket, each having one or more sensors in communication with a wireless mesh network.

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

detects a walking pattern for loss of balance or coordination;

asks the patient to move his or her hands/feet in a predetermined pattern;

checks whether the patient experienced dizziness or headache;

displays a text image and asks the patient to read back the text image one eye at a time;

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

queries the patient for body numbness.

16. The system of claim 1, wherein the analyzer estimates 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 a heart parameter, wherein frequency waves are transmitted by the WLAN transceivers to a chest of the person located a fixed distance from the transceivers, reflected frequency waves are received by the WLAN transceivers from the chest of the patient, and the WLAN transceivers are configured to generate wireless signals based on the reflected frequency waves;

a processor coupled to the WLAN transceivers; and

computer readable code executed by the processor that processes the wireless signals as Doppler radar signals, wherein the processor calibrates a Doppler radar signal with an actual blood pressure during a training phase to develop a model and uses the model with Doppler radar signals during an operational phase to estimate blood pressure.

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

detects a walking pattern for loss of balance or coordination;

asks the person to move his or her hands/feet in a predetermined pattern;

checks whether the person experienced dizziness or headache;

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

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

or queries the person for body numbness.

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

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, or exercise habits.

21. The system of claim 17, comprising one or more of the following:

a sensor to detect weakness in left half and right half of a patient body;

a sensor to detect a walking pattern for loss of balance or coordination;

a sensor to detect user movement in hand or foot in a predetermined pattern;

a sensor to detect whether the person experienced dizziness or headache;

a display to show a text image and request the person to enter data corresponding to the text image one eye at a time;

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

a sensor to detect body numbness.

22. The system of claim 17, wherein the processor detects weakness in left half and right half of a patient body and detects a walking pattern for loss of balance or coordination.