Audio-based Wearable Multi-Context Recognition System for Apnea Detection

Maritsa, Abidah Alfi; Ohnishi, Akiko; Terada, Tsutomu; Tsukamoto, Masahiko

doi:10.1109/iciibms52876.2021.9651598

Cited by 4 publications

(1 citation statement)

References 15 publications

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“…Authors of [18] and [19] propose a wearable system to recognize human contexts such as breathing, heartbeat pattern, and swallowing using audio sensors. Specifically, the proposed device contains two different microphones: the first one is an open-air mic and the second one is a contact mic; both mics are housed on the same body and record audio signals synchronously.…”

Section: Related Workmentioning

confidence: 99%

Obstructive Sleep Apnea Identification Based On VGGish Networks

Serrano¹,

Patané²,

Scarpa³

2023

ECMS 2023 Proceedings Edited by Enrico Vicario, Romeo Bandinelli, Virginia Fani, Michele Mastroianni

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Sleep disorders are continuously growing in the population and can have a significant negative impact on everyday life. Economic and non-invasive systems able to support the diagnosis procedure will be more and more adopted in the next years. The aim of this work is to investigate the classification performance of a convolutional neural network, based on a VGG structure, to identify obstructive sleep apnea events. A recently developed dataset containing audio signals recorded from high-quality contact microphones placed on the trachea of the subjects under study has been adopted to perform transfer learning over a pre-trained VGGish network. Spectrogram images have been extracted from the audio signals to serve as inputs for the classification process. The importance of the time window selection has been also investigated and comparisons with other recent methods proposed in the literature are reported.

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Section: Related Workmentioning

confidence: 99%

Obstructive Sleep Apnea Identification Based On VGGish Networks

Serrano¹,

Patané²,

Scarpa³

2023

ECMS 2023 Proceedings Edited by Enrico Vicario, Romeo Bandinelli, Virginia Fani, Michele Mastroianni

View full text Add to dashboard Cite

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Physiological parameters extraction by contactless accelerometric signal analysis during sleep

Gambi,

Senigagliesi,

Ricciuti

2023

J Ambient Intell Human Comput

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Since sleep problems, like sleep apnea, may pose a serious health concern, the quality of a person’s sleep is a good indicator of overall wellbeing. It is then crucial to continuously monitor people when they are sleeping, especially if they have cardiac or respiratory conditions. The goal of the present paper is to show how to extract physiological parameters from accelerometric signal processing during sleep by applying a non-invasive technology. Using an accelerometric device located under the mattress, we demonstrated the possibility of extracting heart rate and respiratory rate, and then how to use the same signal to implement an automatic algorithm to recognize apneas and, more generally, different activities. The proposed automatic approach has shown good accuracy and dependability, and it may be a useful tool for preventing significant harm during sleep.

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Detection and Classification of Obstructive Sleep Apnea Using Audio Spectrogram Analysis

Serrano,

Patanè,

Serghini

et al. 2024

Electronics

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Sleep disorders are steadily increasing in the population and can significantly affect daily life. Low-cost and noninvasive systems that can assist the diagnostic process will become increasingly widespread in the coming years. This work aims to investigate and compare the performance of machine learning-based classifiers for the identification of obstructive sleep apnea–hypopnea (OSAH) events, including apnea/non-apnea status classification, apnea–hypopnea index (AHI) prediction, and AHI severity classification. The dataset considered contains recordings from 192 patients. It is derived from a recently released dataset which contains, amongst others, audio signals recorded with an ambient microphone placed ∼1 m above the studied subjects and apnea/hypopnea accurate events annotations performed by specialized medical doctors. We employ mel spectrogram images extracted from the environmental audio signals as input of a machine-learning-based classifier for apnea/hypopnea events classification. The proposed approach involves a stacked model which utilizes a combination of a pretrained VGG-like audio classification (VGGish) network and a bidirectional long short-term memory (bi-LSTM) network. Performance analysis was conducted using a 5-fold cross-validation approach, leaving out patients used for training and validation of the models in the testing step. Comparative evaluations with recently presented methods from the literature demonstrate the advantages of the proposed approach. The proposed architecture can be considered a useful tool for supporting OSAHS diagnoses by means of low-cost devices such as smartphones.

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Audio-based Wearable Multi-Context Recognition System for Apnea Detection

Cited by 4 publications

References 15 publications

Obstructive Sleep Apnea Identification Based On VGGish Networks

Obstructive Sleep Apnea Identification Based On VGGish Networks

Physiological parameters extraction by contactless accelerometric signal analysis during sleep

Detection and Classification of Obstructive Sleep Apnea Using Audio Spectrogram Analysis

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