HEALTHCARE SYSTEMS AND MONITORING METHOD FOR PHYSIOLOGICAL SIGNALS

• US 20170156592A1
• Filed: 11/17/2016
• Published: 06/08/2017
• Est. Priority Date: 12/02/2015
• Status: Abandoned Application

• Current Assignee: MediaTek, Inc.
• Original Assignee: MediaTek, Inc.
• Inventors: FU, Chih-Ming
• Application Number:
  US15/354,126
• Publication Number:
  US 20170156592A1
• Time in Patent Office: Days
• CPC Class Codes: A61B 2560/0295 adapted for recording user messages or annotations
• CPC Class Codes: A61B 5/0002 Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network A61B5/07 takes precedence; transmission systems for measured values G08C; transmission H04B; transmission of digital information H04L; wireless communication networks H04W
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/02055 Simultaneously evaluating both cardiovascular condition and temperature
• CPC Class Codes: A61B 5/02125 of pulse wave propagation time
• CPC Class Codes: A61B 5/02416 using photoplethysmograph signals, e.g. generated by infrared radiation A61B5/14552 takes precedence
• CPC Class Codes: A61B 5/0533 Measuring galvanic skin response
• CPC Class Codes: A61B 5/087 Measuring breath flow
• CPC Class Codes: A61B 5/14542 for measuring blood gases A61B5/14551 takes precedence
• CPC Class Codes: A61B 5/349 Detecting specific parameters of the electrocardiograph cycle
• CPC Class Codes: A61B 5/363 Detecting tachycardia or bradycardia
• CPC Class Codes: A61B 5/369 Electroencephalography [EEG] devices for psychotechnics A61B5/16
• CPC Class Codes: A61B 5/4806 Sleep evaluation A61B5/4821 takes precedence; devices for inducing sleep A61M21/02
• CPC Class Codes: A61B 5/7221 Determining signal validity, reliability or quality A61B5/7207 and A61B5/7217 take precedence
• 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 5/742 using visual displays A61B5/339 takes precedence
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 40/67 for remote operation
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

A healthcare system is provided. The healthcare system includes a data server, an algorithm server, a display device, and a communication network. The data server stores a plurality of physiological signals. The algorithm server receives the plurality of physiological signals from the data server. The algorithm server applies a plurality of algorithms on the plurality of physiological signals to obtain at least one feature of the plurality of physiological signals and generates at least one label according to the at least one label. The display device displays the at least one label. The communication network communicatively connects the data server, the algorithm server, and the display device for providing signal transmission paths therebetween.

Claims

1. A healthcare system comprising:

a data server storing a plurality of physiological signals;

an algorithm server receiving the plurality of physiological signals from the data server, applying a plurality of algorithms on the plurality of physiological signals to obtain at least one feature of the plurality of physiological signals and generating a label according to the at least one feature;

a display device displaying the label; and

a communication network communicatively connecting the data server, the algorithm server, and the display device for providing signal transmission paths therebetween.

2. The healthcare system as claimed in claim 1, wherein the algorithm server classifies the label into a not-screened-out category or a screened-out category.

3. The healthcare system as claimed in claim 2, wherein the display device displays the label which is classified into a not-screened-out category or the label which is classified into a screened-out category by different formats or colors.

4. The healthcare system as claimed in claim 3, wherein formats comprise plain text, text with marker, text with highlighted contrast, and text with lowlighted contrast.

5. The healthcare system as claimed in claim 2, wherein the label is classified into the not-screened-out category when the label is an abnormal label.

6. The healthcare system as claimed in claim 5, wherein the abnormal label is an abnormal electrocardiography (ECG), a hypertrophy label, an arrhythmia label, a tachycardia label, a bradycardia label, or an ST elevation label.

7. The healthcare system as claimed in claim 2, wherein the label is classified into the screened-out category when the label is a normal label or a noise label.

8. The healthcare system as claimed in claim 2, wherein when the label is classified into the screened-out category, the algorithm server does not transmit the plurality of physiological signals to the display device.

9. The healthcare system as claimed in claim 1, wherein the plurality of physiological signals are obtained in response to electrocardiography, photoplethysmogram, motion, a body temperature, galvanic skin response, electroencephalograph, oxygen saturation, airflow in respiratory tract, a heart rate, pulse wave transit time, or blood pressure of an object.

10. The healthcare system as claimed in claim 1, wherein when the plurality of physiological signals are electrocardiography (ECG) signals of an object, the algorithm server applies the plurality of algorithms on the ECG signals to remove noise of the ECG signals, estimate quality of the ECG signals, detect a heart rate of the object, determine a heart axis, and extract predetermined features of the ECG signals and further applies a labeling algorithm to obtain the label according to at least one of the estimated quality, the detected heart rate, the heart axis, and the extracted predetermined features.

11. The healthcare system as claimed in claim 10, wherein the labeling algorithm comprises at least one of a decision tree, a nearest neighbor algorithm, a support vector machine (SVM) algorithm, a random forest algorithm, an AdaBoost algorithm, a Naï

ve Bayes algorithm, a Bayesian-network, a neural network, a clustering algorithm, and a deep learning algorithm.

12. The healthcare system as claimed in claim 1, wherein comprises an awake label, a light sleep label, a deep sleep label, or a rapid eye movement sleep label.

13. The healthcare system as claimed in claim 1, wherein the label is represented by a JSON format.

14. A monitoring method comprising:

obtaining a plurality of physiological signals;

applying a plurality of algorithms on the plurality of physiological signals to obtain at least one feature for the plurality of physiological signals;

generating a label according to the at least one feature; and

showing the label.

15. The monitoring method as claimed in claim 14 further comprising classifying each of the label into a not-screened-out category or a screened-out category.

16. The monitoring method as claimed in claim 15, wherein the label which is classified into a not-screened-out category or the label which is classified into a screened-out category is shown by different formats or colors.

17. The monitoring method as claimed in claim 16, wherein formats comprise plain text, text with maker, text with highlighted contrast, and text with lowlighted contrast.

18. The monitoring method as claimed in claim 15, wherein the label is classified into the not-screened-out category when the label is an abnormal label.

19. The monitoring method as claimed in claim 18, wherein the abnormal label is an abnormal electrocardiography (ECG), a hypertrophy label, an arrhythmia label, a tachycardia label, a bradycardia label, or an ST elevation label.

20. The monitoring method as claimed in claim 15, wherein the label is classified into the screened-out category when the label is a normal label or a noise label.