Patient signal analysis based on vector analysis

• US 10,149,625 B2
• Filed: 04/20/2018
• Issued: 12/11/2018
• Est. Priority Date: 11/14/2014
• Status: Active Grant

• Current Assignee: PixArt Imaging Incorporated
• Original Assignee: Siemens Healthcare GMBH (Siemens AG)
• Inventors: Zhang, Hongxuan
• Primary Examiner(s): MAHMOOD, NADIA AHMAD
• Application Number:
  US15/958,198
• Publication Number:
  US 20180235493A1
• Time in Patent Office: 235 Days
• Field of Search: 600512, 600517
• CPC Class Codes: A61B 5/316 Modalities, i.e. specific diagnostic methods
• CPC Class Codes: A61B 5/341 Vectorcardiography [VCG]
• CPC Class Codes: A61B 5/346 Analysis of electrocardiograms
• CPC Class Codes: A61B 5/352 Detecting R peaks, e.g. for synchronising diagnostic apparatus; Estimating R-R interval
• CPC Class Codes: A61B 5/361 Detecting fibrillation
• CPC Class Codes: A61B 5/363 Detecting tachycardia or bradycardia
• CPC Class Codes: A61B 5/7235 Details of waveform analysis A61B5/349 takes precedence
• CPC Class Codes: A61N 1/36514 controlled by a physiological quantity other than heart potential, e.g. blood pressure controlled by two or more physical parameters A61N1/36585
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

Disclosed herein is a framework for facilitating patient signal analysis based on vector analysis. In accordance with one aspect, a set of vectors is generated from a patient signal data waveform. The vectors may be directed from a common center to points of interest on the patient signal data waveform. The framework may further extract one or more vector parameters from the set of vectors, and determine one or more vector ratios based on the vector parameters to monitor changes in the patient signal data waveform.

Claims

1. A system for patient signal analysis, comprising:

a patient monitor that acquires a patient signal data waveform from a patient;

a medical treatment device that provides treatment to the patient; and

a computer system communicatively coupled to the patient monitor and the medical treatment device, wherein the computer system includes

a non-transitory memory device for storing computer readable program code, and

a processor in communication with the memory device, the processor being operative with the computer readable program code to perform steps including

generating a set of vectors directed from a common center on the patient signal data waveform to points of interest on the patient signal data waveform,

extracting one or more vector parameters from the vectors,

determining one or more vector ratios based on the one or more vector parameters to monitor changes in the patient signal data waveform,

determining one or more control parameters based on the one or more vector ratios, and

controlling the medical treatment device by applying the control parameters.

2. The system of claim 1 wherein the computer system comprises an implantable cardiac pacing control device.

3. The system of claim 1 wherein the patient monitor comprises an electrocardiogram recorder with multiple leads for acquiring multi-channel patient signal data.

4. The system of claim 3 wherein the multiple leads are connected to a intra-cardiac basket catheter.

5. The system of claim 4 wherein the intra-cardiac basket catheter is placed into the right atrium of the patient'"'"'s heart.

6. The system of claim 3 wherein the medical treatment device comprises a cardiac ablation or pacing device.

7. The system of claim 6 wherein the cardiac ablation or pacing device is paired with the multiple leads and controlled by the control parameters and wherein the medical treatment is in real-time and adaptive based on the acquired patient signal data waveform.

8. The system of claim 1 wherein the patient signal data waveform comprises an electrocardiogram waveform.

9. The system of claim 1 wherein the patient signal data waveform comprises one of hemodynamic signal data, respiration signal data, blood pressure data and oximetric data.

10. The system of claim 1 further comprising an artificial neural network configured to map the one or more vector ratios and patient data to cardiac event or pathology.

11. The system of claim 1 wherein the artificial neural network has self-learning capability.