Snoring Event Detection Using Machine Learning Techniques

使用机器学习技术检测打鼾事件

基本信息

项目摘要

Snoring is one of the major symptoms of a sleep disorder. Surveys conducted by the National Sleep Foundation**(1999-2004) have revealed that at least 40 million Americans suffer from over 70 different sleep disorders.**Snoring seems underestimated because people cannot recognize the seriousness of their snoring since it occurs**during sleep. Manual recording and examination of a person's respiratory sounds for the entire night can be a**very time-consuming and operator-dependent task. Therefore, an automatic sound recording technique is**desirable. Furthermore, existing solutions in the market for detection of snoring are not designed to address the**core issue itself. Our aim is to both diagnose and address the core issue of snoring using our active antisnoring**solution in the users' bedroom. Almost all of the snore related products make physical contact with the snorer**which result in painful, uncomfortable or disruptive experiences. Smart Nora is the first contact-free, non-invasive**active product on the market to help stop snoring. Although the current product works fine with average**scenarios, the company receives a number of returns and negative feedbacks based on snore detection algorithm**of the current product. Customers often complain about the product as 'not catching the snore', 'activating too**much', or giving 'false alarms' when there is no snore. This project will investigate the development and**implementation of a machine learning algorithm which aims to distinguish the snore events from different type**background noises by using machine learning algorithms and advanced signal processing techniques.
打鼾是睡眠障碍的主要症状之一。美国国家睡眠基金会(National Sleep Foundation)在1999年至2004年进行的调查显示,至少有4000万美国人患有70多种不同的睡眠障碍。打鼾似乎被低估了,因为人们无法认识到打鼾的严重性,因为它发生在睡眠中。人工记录和检查一个人的呼吸声整个晚上可以是一个非常耗时和操作员依赖的任务。因此,需要一种自动录音技术。此外,市场上现有的检测打鼾的解决方案并不是为了解决核心问题本身而设计的。我们的目标是在用户的卧室中使用我们的主动防打鼾 ** 解决方案来诊断和解决打鼾的核心问题。几乎所有与打鼾相关的产品都与打鼾者进行身体接触 **,导致疼痛、不舒服或破坏性体验。Smart诺拉是市场上第一款帮助停止打鼾的无接触、非侵入性 ** 主动产品。虽然目前的产品适用于一般 ** 场景,但公司收到了许多基于当前产品的打鼾检测算法 ** 的退货和负面反馈。消费者经常抱怨该产品“不能捕捉打鼾”,“激活太多”,或者在没有打鼾时发出“假警报”。本项目将研究一种机器学习算法的开发和实现,该算法旨在通过使用机器学习算法和先进的信号处理技术来区分打鼾事件和不同类型的背景噪声。

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

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Rahnamayan, Shahryar其他文献

Bias reduction in representation of histopathology images using deep feature selection.
  • DOI:
    10.1038/s41598-022-24317-z
  • 发表时间:
    2022-11-21
  • 期刊:
  • 影响因子:
    4.6
  • 作者:
    Bidgoli, Azam Asilian;Rahnamayan, Shahryar;Dehkharghanian, Taher;Grami, Ali;Tizhoosh, H. R.
  • 通讯作者:
    Tizhoosh, H. R.
Evolutionary deep feature selection for compact representation of gigapixel images in digital pathology
  • DOI:
    10.1016/j.artmed.2022.102368
  • 发表时间:
    2022-08-02
  • 期刊:
  • 影响因子:
    7.5
  • 作者:
    Bidgoli, Azam Asilian;Rahnamayan, Shahryar;Tizhoosh, H. R.
  • 通讯作者:
    Tizhoosh, H. R.
Opposition-based differential evolution
  • DOI:
    10.1109/tevc.2007.894200
  • 发表时间:
    2008-02-01
  • 期刊:
  • 影响因子:
    14.3
  • 作者:
    Rahnamayan, Shahryar;Tizhoosh, Hamid R.;Salama, Magdy M. A.
  • 通讯作者:
    Salama, Magdy M. A.
A novel binary many-objective feature selection algorithm for multi-label data classification
Enhanced opposition-based differential evolution for solving high-dimensional continuous optimization problems
用于解决高维连续优化问题的增强型基于反对派的差分进化
  • DOI:
    10.1007/s00500-010-0642-7
  • 发表时间:
    2011-11-01
  • 期刊:
  • 影响因子:
    4.1
  • 作者:
    Wang, Hui;Wu, Zhijian;Rahnamayan, Shahryar
  • 通讯作者:
    Rahnamayan, Shahryar

Rahnamayan, Shahryar的其他文献

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{{ truncateString('Rahnamayan, Shahryar', 18)}}的其他基金

Efficient Evolutionary Algorithms for Many-objective Optimization
多目标优化的高效进化算法
  • 批准号:
    RGPIN-2015-03651
  • 财政年份:
    2022
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Individual
Efficient Evolutionary Algorithms for Many-objective Optimization
多目标优化的高效进化算法
  • 批准号:
    RGPIN-2015-03651
  • 财政年份:
    2021
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Individual
Efficient Evolutionary Algorithms for Many-objective Optimization
多目标优化的高效进化算法
  • 批准号:
    RGPIN-2015-03651
  • 财政年份:
    2020
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Individual
Efficient Evolutionary Algorithms for Many-objective Optimization
多目标优化的高效进化算法
  • 批准号:
    RGPIN-2015-03651
  • 财政年份:
    2019
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Individual
Efficient Evolutionary Algorithms for Many-objective Optimization
多目标优化的高效进化算法
  • 批准号:
    RGPIN-2015-03651
  • 财政年份:
    2018
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Individual
Efficient Evolutionary Algorithms for Many-objective Optimization
多目标优化的高效进化算法
  • 批准号:
    RGPIN-2015-03651
  • 财政年份:
    2017
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Individual
Efficient Evolutionary Algorithms for Many-objective Optimization
多目标优化的高效进化算法
  • 批准号:
    RGPIN-2015-03651
  • 财政年份:
    2016
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Individual
Efficient Evolutionary Algorithms for Many-objective Optimization
多目标优化的高效进化算法
  • 批准号:
    RGPIN-2015-03651
  • 财政年份:
    2015
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Individual
Oppostition-based evolutionary algorithms: toward solving high-dimensional optimization problems efficiently
基于对立的进化算法:高效解决高维优化问题
  • 批准号:
    371992-2010
  • 财政年份:
    2014
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Individual
Oppostition-based evolutionary algorithms: toward solving high-dimensional optimization problems efficiently
基于对立的进化算法:高效解决高维优化问题
  • 批准号:
    371992-2010
  • 财政年份:
    2013
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Individual

相似国自然基金

甲醇合成汽油工艺中烯烃催化聚合过程的单元步骤(single event)微动力学理论研究
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