Dong Hyun Choi;Yoon Ha Joo;Ki Hong Kim;Jeong Ho Park;Hyunjin Joo;Hyoun-Joong Kong;Hyunju Lee;Kyoung Jun Song;Sungwan Kim
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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. 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引用次数: 0
摘要
本研究的目的是开发一种基于声音识别的心肺复苏(CPR)培训系统,该系统方便使用、经济实惠、易于维护并能提供准确的心肺复苏反馈。Beep-CPR 是一种新型装置,带有风琴式尖叫器,在按压过程中会发出高亢的声音。Beep-CPR 发出的声音由智能手机录制,分割成 2 秒钟的音频片段,然后转换成频谱图。从大约 40 分钟的音频数据中共生成了 6,065 张频谱图,然后随机分成训练数据集、验证数据集和测试数据集。每张频谱图都与 ZOLL X 系列监护仪/除颤器在相同时间间隔内测量到的压缩深度、速率和释放速度相匹配。以频谱图为输入的深度学习模型通过基于 EfficientNet 的迁移学习进行训练,以预测按压的深度(深度模型)、速率(速率模型)和释放速度(反冲模型)。结果:深度模型的平均绝对误差(MAE)为 0.30 厘米(95% 置信区间 [CI]:0.27-0.33)。速率模型的 MAE 为 3.6/分钟(95% 置信区间:3.2-3.9)。后坐力模型的 MAE 为 2.3 厘米/秒(95% CI:2.1-2.5)。模型的外部验证表明,在多种条件下,包括使用新制造的设备、疲劳设备以及在空间尺寸改变的环境中进行评估,其性能都是可以接受的。我们开发了一种新型的基于声音识别的心肺复苏训练系统,可在训练过程中准确测量按压质量。意义重大:Beep-CPR 是一种成本效益高且易于维护的解决方案,可通过提供性能反馈来促进分散的家庭培训,从而提高心肺复苏术培训的效果。
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 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.
期刊介绍:
The IEEE Journal of Translational Engineering in Health and Medicine is an open access product that bridges the engineering and clinical worlds, focusing on detailed descriptions of advanced technical solutions to a clinical need along with clinical results and healthcare relevance. The journal provides a platform for state-of-the-art technology directions in the interdisciplinary field of biomedical engineering, embracing engineering, life sciences and medicine. A unique aspect of the journal is its ability to foster a collaboration between physicians and engineers for presenting broad and compelling real world technological and engineering solutions that can be implemented in the interest of improving quality of patient care and treatment outcomes, thereby reducing costs and improving efficiency. The journal provides an active forum for clinical research and relevant state-of the-art technology for members of all the IEEE societies that have an interest in biomedical engineering as well as reaching out directly to physicians and the medical community through the American Medical Association (AMA) and other clinical societies. The scope of the journal includes, but is not limited, to topics on: Medical devices, healthcare delivery systems, global healthcare initiatives, and ICT based services; Technological relevance to healthcare cost reduction; Technology affecting healthcare management, decision-making, and policy; Advanced technical work that is applied to solving specific clinical needs.