A Development of a Sound Recognition-Based Cardiopulmonary Resuscitation Training System
The objective of this study was to develop a sound recognition-based cardiopulmonary resuscitation (CPR) training system that is accessible, cost-effective, easy-to-maintain and provides accurate CPR feedback. Beep-CPR, a novel device with accordion squeakers that emit high-pitched sounds during com...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Journal of Translational Engineering in Health and Medicine |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10613881/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850037698156298240 |
|---|---|
| author | Dong Hyun Choi Yoon Ha Joo Ki Hong Kim Jeong Ho Park Hyunjin Joo Hyoun-Joong Kong Hyunju Lee Kyoung Jun Song Sungwan Kim |
| author_facet | Dong Hyun Choi Yoon Ha Joo Ki Hong Kim Jeong Ho Park Hyunjin Joo Hyoun-Joong Kong Hyunju Lee Kyoung Jun Song Sungwan Kim |
| author_sort | Dong Hyun Choi |
| collection | DOAJ |
| description | The objective of this study was to develop a sound recognition-based cardiopulmonary resuscitation (CPR) training system that is accessible, cost-effective, easy-to-maintain and provides accurate CPR feedback. Beep-CPR, a novel device with accordion squeakers that emit high-pitched sounds during compression, was developed. The sounds emitted by Beep-CPR were recorded using a smartphone, segmented into 2-second audio fragments, and then transformed into spectrograms. A total of 6,065 spectrograms were generated from approximately 40 minutes of audio data, which were then randomly split into training, validation, and test datasets. Each spectrogram was matched with the depth, rate, and release velocity of the compression measured at the same time interval by the ZOLL X Series monitor/defibrillator. Deep learning models utilizing spectrograms as input were trained using transfer learning based on EfficientNet to predict the depth (Depth model), rate (Rate model), and release velocity (Recoil model) of compressions. Results: The mean absolute error (MAE) for the Depth model was 0.30 cm (95% confidence interval [CI]: 0.27–0.33). The MAE of the Rate model was 3.6/min (95% CI: 3.2–3.9). For the Recoil model, the MAE was 2.3 cm/s (95% CI: 2.1–2.5). External validation of the models demonstrated acceptable performance across multiple conditions, including the utilization of a newly-manufactured device, a fatigued device, and evaluation in an environment with altered spatial dimensions. We have developed a novel sound recognition-based CPR training system, that accurately measures compression quality during training. Significance: Beep-CPR is a cost-effective and easy-to-maintain solution that can improve the efficacy of CPR training by facilitating decentralized at-home training with performance feedback. |
| format | Article |
| id | doaj-art-e2f0196bdedd43f1b4ca162908850842 |
| institution | DOAJ |
| issn | 2168-2372 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Translational Engineering in Health and Medicine |
| spelling | doaj-art-e2f0196bdedd43f1b4ca1629088508422025-08-20T02:56:47ZengIEEEIEEE Journal of Translational Engineering in Health and Medicine2168-23722024-01-011255055710.1109/JTEHM.2024.343344810613881A Development of a Sound Recognition-Based Cardiopulmonary Resuscitation Training SystemDong Hyun Choi0https://orcid.org/0000-0001-6036-1404Yoon Ha Joo1https://orcid.org/0000-0002-2133-6297Ki Hong Kim2https://orcid.org/0000-0003-2115-0078Jeong Ho Park3https://orcid.org/0000-0001-6330-9820Hyunjin Joo4Hyoun-Joong Kong5Hyunju Lee6Kyoung Jun Song7Sungwan Kim8https://orcid.org/0000-0002-9318-849XDepartment of Biomedical Engineering, Seoul National University College of Medicine, Jongno, Seoul, South KoreaBiomedical Research Institute, Seoul National University Hospital, Jongno, Seoul, South KoreaDepartment of Emergency Medicine, Seoul National University Hospital, Jongno, Seoul, South KoreaDepartment of Emergency Medicine, Seoul National University Hospital, Jongno, Seoul, South KoreaInnovative Medical Technology Research Institute, Seoul National University Hospital, Jongno, Seoul, South KoreaDepartment of Transdisciplinary Medicine, Innovative Medical Technology Research Institute, Seoul National University Hospital, Jongno, Seoul, South KoreaLaboratory of Emergency Medical Services, Biomedical Research Institute, Seoul National University Hospital, Jongno, Seoul, South KoreaDepartment of Emergency Medicine, Seoul Metropolitan Boramae Medical Center, Dongjak, Seoul, South KoreaDepartment of Biomedical Engineering, Seoul National University College of Medicine, Jongno, Seoul, South KoreaThe objective of this study was to develop a sound recognition-based cardiopulmonary resuscitation (CPR) training system that is accessible, cost-effective, easy-to-maintain and provides accurate CPR feedback. Beep-CPR, a novel device with accordion squeakers that emit high-pitched sounds during compression, was developed. The sounds emitted by Beep-CPR were recorded using a smartphone, segmented into 2-second audio fragments, and then transformed into spectrograms. A total of 6,065 spectrograms were generated from approximately 40 minutes of audio data, which were then randomly split into training, validation, and test datasets. Each spectrogram was matched with the depth, rate, and release velocity of the compression measured at the same time interval by the ZOLL X Series monitor/defibrillator. Deep learning models utilizing spectrograms as input were trained using transfer learning based on EfficientNet to predict the depth (Depth model), rate (Rate model), and release velocity (Recoil model) of compressions. Results: The mean absolute error (MAE) for the Depth model was 0.30 cm (95% confidence interval [CI]: 0.27–0.33). The MAE of the Rate model was 3.6/min (95% CI: 3.2–3.9). For the Recoil model, the MAE was 2.3 cm/s (95% CI: 2.1–2.5). External validation of the models demonstrated acceptable performance across multiple conditions, including the utilization of a newly-manufactured device, a fatigued device, and evaluation in an environment with altered spatial dimensions. We have developed a novel sound recognition-based CPR training system, that accurately measures compression quality during training. Significance: Beep-CPR is a cost-effective and easy-to-maintain solution that can improve the efficacy of CPR training by facilitating decentralized at-home training with performance feedback.https://ieeexplore.ieee.org/document/10613881/Cardiopulmonary arrestsound recognitiondeep learningfeedback communications |
| spellingShingle | Dong Hyun Choi Yoon Ha Joo Ki Hong Kim Jeong Ho Park Hyunjin Joo Hyoun-Joong Kong Hyunju Lee Kyoung Jun Song Sungwan Kim A Development of a Sound Recognition-Based Cardiopulmonary Resuscitation Training System IEEE Journal of Translational Engineering in Health and Medicine Cardiopulmonary arrest sound recognition deep learning feedback communications |
| title | A Development of a Sound Recognition-Based Cardiopulmonary Resuscitation Training System |
| title_full | A Development of a Sound Recognition-Based Cardiopulmonary Resuscitation Training System |
| title_fullStr | A Development of a Sound Recognition-Based Cardiopulmonary Resuscitation Training System |
| title_full_unstemmed | A Development of a Sound Recognition-Based Cardiopulmonary Resuscitation Training System |
| title_short | A Development of a Sound Recognition-Based Cardiopulmonary Resuscitation Training System |
| title_sort | development of a sound recognition based cardiopulmonary resuscitation training system |
| topic | Cardiopulmonary arrest sound recognition deep learning feedback communications |
| url | https://ieeexplore.ieee.org/document/10613881/ |
| work_keys_str_mv | AT donghyunchoi adevelopmentofasoundrecognitionbasedcardiopulmonaryresuscitationtrainingsystem AT yoonhajoo adevelopmentofasoundrecognitionbasedcardiopulmonaryresuscitationtrainingsystem AT kihongkim adevelopmentofasoundrecognitionbasedcardiopulmonaryresuscitationtrainingsystem AT jeonghopark adevelopmentofasoundrecognitionbasedcardiopulmonaryresuscitationtrainingsystem AT hyunjinjoo adevelopmentofasoundrecognitionbasedcardiopulmonaryresuscitationtrainingsystem AT hyounjoongkong adevelopmentofasoundrecognitionbasedcardiopulmonaryresuscitationtrainingsystem AT hyunjulee adevelopmentofasoundrecognitionbasedcardiopulmonaryresuscitationtrainingsystem AT kyoungjunsong adevelopmentofasoundrecognitionbasedcardiopulmonaryresuscitationtrainingsystem AT sungwankim adevelopmentofasoundrecognitionbasedcardiopulmonaryresuscitationtrainingsystem AT donghyunchoi developmentofasoundrecognitionbasedcardiopulmonaryresuscitationtrainingsystem AT yoonhajoo developmentofasoundrecognitionbasedcardiopulmonaryresuscitationtrainingsystem AT kihongkim developmentofasoundrecognitionbasedcardiopulmonaryresuscitationtrainingsystem AT jeonghopark developmentofasoundrecognitionbasedcardiopulmonaryresuscitationtrainingsystem AT hyunjinjoo developmentofasoundrecognitionbasedcardiopulmonaryresuscitationtrainingsystem AT hyounjoongkong developmentofasoundrecognitionbasedcardiopulmonaryresuscitationtrainingsystem AT hyunjulee developmentofasoundrecognitionbasedcardiopulmonaryresuscitationtrainingsystem AT kyoungjunsong developmentofasoundrecognitionbasedcardiopulmonaryresuscitationtrainingsystem AT sungwankim developmentofasoundrecognitionbasedcardiopulmonaryresuscitationtrainingsystem |