Digital stethoscope and monitoring instrument

• US 20080013747A1
• Filed: 06/30/2006
• Published: 01/17/2008
• Est. Priority Date: 06/30/2006
• Status: Abandoned Application

• Current Assignee: Bao Tran
• Original Assignee: Bao Tran
• Inventors: Tran, Bao
• Application Number:
  US11/480,206
• Publication Number:
  US 20080013747A1
• Time in Patent Office: Days
• US Class Current: 381/67
• CPC Class Codes: A61B 2562/028 Microscale sensors, e.g. electromechanical sensors [MEMS]
• CPC Class Codes: A61B 5/029 Measuring or recording blood output from the heart, e.g. minute volume A61B8/065 takes precedence
• CPC Class Codes: A61B 5/0295 using plethysmography, i.e. measuring the variations in the volume of a body part as modified by the circulation of blood therethrough, e.g. impedance plethysmography
• CPC Class Codes: A61B 5/0535 Impedance plethysmography for measuring blood flow A61B5/0295
• CPC Class Codes: A61B 5/08 Detecting, measuring or recording devices for evaluating the respiratory organs A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/349 Detecting specific parameters of the electrocardiograph cycle
• 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 5/411 Detecting or monitoring allergy or intolerance reactions to an allergenic agent or substance
• CPC Class Codes: A61B 5/4519 Muscles A61B5/389, A61B5/224 take precedence
• 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 7/04 Electric stethoscopes microphones, acoustic transducers therefor H04R
• CPC Class Codes: G16H 40/63 for local operation

Abstract

An electronic stethoscope includes a microphone; an accelerometer to detect stethoscope movement; a processor coupled to the microphone and the accelerometer; and a speaker coupled to the processor to reproduce a biological sound.

Claims

1. An electronic stethoscope, comprising:

a CMOS micro-machined mesh transducer;

a decimation filter coupled to the transducer;

a processor coupled to the decimation filter; and

a speaker coupled to the processor to reproduce a biological sound.

2. The electronic stethoscope of claim 1, comprising an accelerometer coupled to the processor, wherein the processor captures biological sound when the accelerometer output is below a predetermined threshold.

3. The electronic stethoscope of claim 1, wherein the transducer comprises a MEMS (microelectromechanical systems) device.

4. The electronic stethoscope of claim 1, comprising a wireless mesh network coupled to the processor.

5. The electronic stethoscope of claim1, comprising a second mesh transducer, wherein the two mesh transducers form an array for noise-cancellation.

6. The electronic stethoscope of claim 1, comprising an acoustical vent having a resistance and a mass element and air-cavity volume performing a second order low-pass filtering of ambient noise.

7. The electronic stethoscope of claim 1, wherein the biological sound comprises one of:

heart sound, lung sound.

8. The electronic stethoscope of claim 1, comprising a low pass filter and a high pass filter for each of the heart sound, lung sound.

9. The electronic stethoscope of claim 1, wherein the decimation filter comprises a CODEC.

10. The electronic stethoscope of claim 1, comprising one of:

EKG sensor, ECG sensor, EMG sensor, EEG sensor, bioimpedance sensor.

11. The electronic stethoscope of claim 1, wherein the mesh transducer is housed in one of:

a chest piece, a head, a patch.

12. The electronic stethoscope of claim 1, wherein the speaker'"'"'s output is adapted to a listener'"'"'s individual hearing skill.

13. The electronic stethoscope of claim 12, wherein the processor measures the hearing skill objectively and converts the hearing skill to a transfer function stored in the stethoscope.

14. The electronic stethoscope of claim 1, comprising a pattern recognizer to analyze sound captured by the mesh transducer.

15. The electronic stethoscope of claim 14, wherein the sound is heart sound and wherein the pattern recognizer detects one of:

Normal S1, Split S1, Normal S2, Normal split S2, Wide split S2, Paradoxical split S2, Fixed split S2, S3 right ventricle origin, S3 left ventricle origin, opening snap, S4 right ventricle origin, S4 left ventricle origin, aortic ejection sound, pulmonic ejection sound.

16. The electronic stethoscope of claim 14, wherein the pattern recognizer comprises one of:

a Bayesian network, a Hidden Markov Model, a neural network, a fuzzy logic engine.

17. The electronic stethoscope of claim 1, wherein the speaker comprises a CMOS micro-machined mesh microspeaker.

18. A method to listen to a body sound, comprising:

capturing the body sound using a MEMS (microelectromechanical systems) metal mesh microphone;

filtering the output of the MEMS metal mesh microphone;

playing the body sound on a speaker to reproduce a biological sound.

19. The method of claim 18, comprising performing noise cancellation using an array of noise canceling MEMS metal mesh microphones.

20. An electronic stethoscope, comprising:

a microphone;

an accelerometer to detect stethoscope movement;

a processor coupled to the microphone and the accelerometer; and

a speaker coupled to the processor to reproduce a biological sound.