Investigating the Relationship between Cough Detection and Sampling Frequency for Wearable Devices
Investigating the Relationship between Cough Detection and Sampling Frequency for Wearable Devices
复制标题
研究可穿戴设备的咳嗽检测与采样频率之间的关系
DOI：
10.1109/embc46164.2021.9630082
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Lobaton, Edgar
中科院分区：
文献类型：
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作者：
Abdelkhalek, Mahmoud;Qiu, Jinyi;Hernandez, Michelle;Bozkurt, Alper;Lobaton, Edgar
咳嗽检测可以提供监测慢性呼吸道疾病的重要标志。然而，需要人类专业知识来计数咳嗽的手动技术既昂贵又耗时。最近的自动咳嗽检测算法（ACDA）已经显示出满足临床监测要求的前景，但由于传感技术所需的便携性和数据记录的持续时间延长，仅在近年来它们才进入非临床环境。更确切地说，这些ACDA以高采样频率工作，这导致高功耗和计算要求，使得这些难以在可穿戴设备上实现。此外，其性能的可重复性也至关重要。不幸的是，由于大多数ACDA是使用私人临床数据开发的，因此很难重现其结果。因此，我们提出了一个ACDA，满足临床监测的要求，并可靠地在低采样频率。这个ACDA是使用卷积神经网络（CNN）和公开可用的数据实现的。它实现了92.7%的灵敏度，92.3%的特异性和92.5%的准确性，仅使用750 Hz的采样频率。我们还表明，一个低的采样频率，使我们能够保护患者的隐私，通过混淆他们的语音，我们分析语音混淆隐私和咳嗽检测accuracy.Clinical relevance之间的权衡本文提出了一种新的咳嗽检测技术和初步分析检测精度和混淆语音隐私之间的权衡。这些发现表明，使用公开可用的数据集，我们可以在750 Hz下对信号进行采样，同时仍保持90%以上的灵敏度，这足以用于临床监测[1]。
Cough detection can provide an important marker to monitor chronic respiratory conditions. However, manual techniques which require human expertise to count coughs are both expensive and time-consuming. Recent Automatic Cough Detection Algorithms (ACDAs) have shown promise to meet clinical monitoring requirements, but only in recent years they have made their way to non-clinical settings due to the required portability of sensing technologies and the extended duration of data recording. More precisely, these ACDAs operate at high sampling frequencies, which leads to high power consumption and computing requirements, making these difficult to implement on a wearable device. Additionally, reproducibility of their performance is essential. Unfortunately, as the majority of ACDAs were developed using private clinical data, it is difficult to reproduce their results. We, hereby, present an ACDA that meets clinical monitoring requirements and reliably operates at a low sampling frequency. This ACDA is implemented using a convolutional neural network (CNN), and publicly available data. It achieves a sensitivity of 92.7%, a specificity of 92.3%, and an accuracy of 92.5% using a sampling frequency of just 750 Hz. We also show that a low sampling frequency allows us to preserve patients’ privacy by obfuscating their speech, and we analyze the trade-off between speech obfuscation for privacy and cough detection accuracy.Clinical relevance—This paper presents a new cough detection technique and preliminary analysis on the trade-off between detection accuracy and obfuscation of speech for privacy. These findings indicate that, using a publicly available dataset, we can sample signals at 750 Hz while still maintaining a sensitivity above 90%, suggested to be sufficient for clinical monitoring [1].
DOI：
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发表时间：
2015-12
期刊：
--
影响因子：
--
作者：
Dario Amodei;S. Ananthanarayanan;Rishita Anubhai;Jin Bai;Eric Battenberg;Carl Case;J. Casper;
通讯作者：
Dario Amodei;S. Ananthanarayanan;Rishita Anubhai;Jin Bai;Eric Battenberg;Carl Case;J. Casper;
P159 VitaloJAK™ 24 小时动态咳嗽监测仪的验证
影响因子：
10
作者：
K. Mcguinness;K. Holt;R. Dockry;Jacky A. Smith
通讯作者：
Jacky A. Smith
影响因子：
7.3
作者：
Stowell, Dan;Giannoulis, Dimitrios;Plumbley, Mark D.
通讯作者：
Plumbley, Mark D.