Classifier ensemble for detection of abnormal heart sounds

A classifier, abnormal technology, applied in applications, stethoscopes, sensors, etc., can solve problems such as low accuracy, discrepancy, and different quality of heart sound recordings

Inactive Publication Date: 2019-06-04
KONINKLJIJKE PHILIPS NV
View PDF7 Cites 1 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] However, analysis of heart sound recordings in both clinical and non-clinical settings has proven to be challenging due to environmental noise (e.g., alarms, talking)
In addition, recording heart sounds by non-experts also adds challenges to automated heart sound analysis
For example, changing microphone position may alter heart sound amplitude and may make heart sounds prone to noise
Also, when heart sounds are recorded by different instruments, the quality of the heart sound recordings may vary (e.g. due to differences in the filter specifications of the different instruments), making it challenging to use a single algorithm
Due to the above factors, feature-based methods (traditional heart sound analysis) may have relatively low accuracy in the classification of abnormal heart sounds

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Classifier ensemble for detection of abnormal heart sounds
  • Classifier ensemble for detection of abnormal heart sounds
  • Classifier ensemble for detection of abnormal heart sounds

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0041] The description and figures presented herein illustrate various principles. It will be appreciated that those skilled in the art will be able to devise various arrangements that, although not explicitly described or shown herein, embody these principles and are included within the scope of the present disclosure. As used herein, the term "or" as used herein means a non-exclusive or (ie, and / or). Additionally, the various embodiments described in this disclosure are not necessarily mutually exclusive and can be combined to yield additional embodiments that incorporate the principles described in this disclosure.

[0042] In order to facilitate the understanding of the invention of the present disclosure, the following Figure 1A and Figure 1B The description of ® teaches two (2) embodiments of the PCG classifier ensemble system of the present disclosure. according to Figure 1A and Figure 1B Given the description, those of ordinary skill in the art of the present ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

Various embodiments of the inventions of the present disclosure provide a combination of feature-based approach and deep learning approach for distinguishing between normal heart sounds and abnormal heart sounds. A feature-based classifier (60) is applied to a phonocardiogram (PCG) signal to obtain a feature-based abnormality classification of the heart sounds represented by the PCG signal and a deep learning classifier (70) is also applied to the PCG signal to obtain a deep learning abnormality classification of the heart sounds represented by the PCG signal. A final decision analyzer (80) isapplied to the feature-based abnormality classification and the deep learning abnormality classification of the heart sounds represented by the PCG signal to determine a final abnormality classification decision of the PCG signal.

Description

technical field [0001] Various embodiments described in this disclosure relate to systems, devices, and methods for detecting abnormal heart sounds. Background technique [0002] Cardiovascular disease (CVD) is a leading cause of morbidity and mortality worldwide, accounting for an estimated 17.5 million deaths in 2012. Cardiac auscultation is the main tool for screening and diagnosing CVD in primary health care. The use of digital stethoscopes and mobile devices offers clinicians the opportunity to record and analyze heart sounds (phonocardiogram, PCG) for diagnostic purposes. [0003] However, analysis of heart sound recordings in both clinical and non-clinical settings has proven to be a challenging task due to environmental noise (eg, alarms, talking). In addition, recording heart sounds by non-experts also increases the challenge for automated heart sound analysis. For example, changing the microphone position may alter the heart sound amplitude and may make the hear...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): A61B7/00
CPCA61B7/00G16H50/20A61B5/725A61B5/7267A61B5/7278A61B5/7282A61B7/04G06N3/04G06N3/08
Inventor S·珀尔沃内C·M·波特斯布兰东A·拉赫曼B·康罗伊
Owner KONINKLJIJKE PHILIPS NV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap