Utilizing Convolution Neural Networks for the Acoustic Detection of Inhaler Actuations

被引：0
|
作者：
Kikidis, Dimitrios [1 ]
Votis, Konstantinos [1 ]
Tzovaras, Dimitrios [1 ]
机构：
[1] Ctr Res & Technol Hellas, Inst Informat Technol, Thessaloniki, Greece
来源：
2015 E-HEALTH AND BIOENGINEERING CONFERENCE (EHB) | 2015年
关键词：
asthma; metered dose inhaler; inhaler actuation; biosignal processing; convolution neural networks; ADHERENCE; ASTHMA; CORTICOSTEROIDS; MEDICATION;
D O I：
暂无
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
Asthma is a chronic respiratory disease and a significant burden for patients, their families and the healthcare system as a whole. Unfortunately, the management of the disease is far from optimal mainly due to the reduced adherence of patients to their medication plan. In order to solve this problem, a number of novel inhalers have been proposed over the past that monitor and support the proper use of inhaled medication. Aiming in this direction, the current study investigates the use of acoustic signals for the detection of inhaler actuations during activities of daily living and outside the controlled environment of the laboratory. The proposed algorithm is based on Convolution Neural Networks. The results of the current approach, have led to high levels of accuracy (98%), demonstrating the potential of this method for the development of novel inhalers and medical devices in the area of respiratory medicine.
引用
收藏
页数：4
相关论文
共 50 条
  • [21] Malware visualization methods based on deep convolution neural networks
    Ren, Zhuojun
    Chen, Guang
    Lu, Wenke
    MULTIMEDIA TOOLS AND APPLICATIONS, 2020, 79 (15-16) : 10975 - 10993
  • [22] Effect of Blurring on Identification of Aerial Images Using Convolution Neural Networks
    Mahajan, Palak
    Abrol, Pawanesh
    Lehana, Parveen K.
    PROCEEDINGS OF RECENT INNOVATIONS IN COMPUTING, ICRIC 2019, 2020, 597 : 469 - 484
  • [23] An Efficient License Plate Recognition System Using Convolution Neural Networks
    Lin, Cheng-Hung
    Lin, Yong-Sin
    Liu, Wei-Chen
    PROCEEDINGS OF 4TH IEEE INTERNATIONAL CONFERENCE ON APPLIED SYSTEM INNOVATION 2018 ( IEEE ICASI 2018 ), 2018, : 224 - 227
  • [24] Recognizing Very Small Face Images Using Convolution Neural Networks
    Horng, Shi-Jinn
    Supardi, Julian
    Zhou, Wanlei
    Lin, Chin-Teng
    Jiang, Bin
    IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2022, 23 (03) : 2103 - 2115
  • [25] Convolution neural networks for optical coherence tomography (OCT) image classification
    Karthik, Karri
    Mahadevappa, Manjunatha
    BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2023, 79
  • [26] Cancer detection in breast histopathology with convolution neural network based approach
    Kausar, Tasleem
    Wang, MingJiang
    Malik, M. S. S.
    2019 IEEE/ACS 16TH INTERNATIONAL CONFERENCE ON COMPUTER SYSTEMS AND APPLICATIONS (AICCSA 2019), 2019,
  • [27] Brain Tumour Identification Through MRI Images Using Convolution Neural Networks
    Rao, N. Jagan Mohana
    Kumar, B. Anil
    PROCEEDING OF THE INTERNATIONAL CONFERENCE ON COMPUTER NETWORKS, BIG DATA AND IOT (ICCBI-2018), 2020, 31 : 1046 - 1053
  • [28] Grading Diabetic Retinopathy Severity Using Modern Convolution Neural Networks (CNN)
    Lee, Andrew
    Khushi, Matloob
    Hao, Patrick
    Uddin, Shahadat
    Poon, Simon K.
    2021 IEEE INTERNATIONAL CONFERENCE ON DIGITAL HEALTH (ICDH 2021), 2021, : 19 - 26
  • [29] An Efficient Approach for Classifying Social Network Events Using Convolution Neural Networks
    Hussain, Ahsan
    Keshavamurthy, Bettahally N.
    Wazarkar, Seema
    ADVANCES IN DATA AND INFORMATION SCIENCES, ICDIS 2017, VOL 2, 2019, 39 : 177 - 184
  • [30] Face Emotion Recognition From Static Image Based on Convolution Neural Networks
    Nasri, M. A.
    Hmani, M. A.
    Mtibaa, A.
    Petrovska-Delacretaz, D.
    Ben Slima, M.
    Ben Hamida, A.
    2020 5TH INTERNATIONAL CONFERENCE ON ADVANCED TECHNOLOGIES FOR SIGNAL AND IMAGE PROCESSING (ATSIP'2020), 2020,
12345