Detecting Breathing and Snoring Episodes Using a Wireless Tracheal Sensor—A Feasibility Study

Młyńczak, Marcel; Migacz, Ewa; Migacz, Maciej; Kukwa, Wojciech

doi:10.1109/jbhi.2016.2632976

Cited by 24 publications

(12 citation statements)

References 35 publications

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“…The gold standard of sleep-disordered diagnosis including conditions such as OSA is polysomnography (PSG). It is used to determine the frequency and severity of normal respiratory disorder events per hour and reports as the Apnea-Hypopnea Index (AHI) which can be used to classify the OSA as normal (AHI<5), mild (AHI is in [5][6][7][8][9][10][11][12][13][14], moderate (AHI is in [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30], and severe (AHI>30), respectively [10]. However, this method is a form of clinical practice which has to be done overnight in a laboratory or hospital [13] using numerous sensors to acquire the necessary data, such as electroencephalogram (EEG), electrooculogram (EOG), chin electromyography (EMG), leg movement, airflow, cannula flow, respiratory effort, oximetry, body position, electrocardiogram (ECG), and so forth [6].…”

Section: Introductionmentioning

confidence: 99%

Single Channel ECG for Obstructive Sleep Apnea Severity Detection Using a Deep Learning Approach

Banluesombatkul

Rakthanmanon

Wilaiprasitporn

2018

TENCON 2018 - 2018 IEEE Region 10 Conference

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Obstructive sleep apnea (OSA) is a common sleep disorder caused by abnormal breathing. The severity of OSA can lead to many symptoms such as sudden cardiac death (SCD). Polysomnography (PSG) is a gold standard for OSA diagnosis. It records many signals from the patient's body for at least one whole night and calculates the Apnea-Hypopnea Index (AHI) which is the number of apnea or hypopnea incidences per hour. This value is then used to classify patients into OSA severity levels. However, it has many disadvantages and limitations. Consequently, we proposed a novel methodology of OSA severity classification using a Deep Learning approach. We focused on the classification between normal subjects (AHI < 5) and severe OSA patients (AHI > 30). The 15-second raw ECG records with apnea or hypopnea events were used with a series of one-dimensional Convolutional Neural Networks (1-D CNN) for automatic feature extraction, deep recurrent neural networks with Long Short-Term Memory (LSTM) for temporal information extraction, and fully-connected neural networks (DNN) for feature encoding from a large number of features until it closed to two classes. The main advantages of our proposed method include easier data acquisition, instantaneous OSA severity detection, and effective feature extraction without domain knowledge from expertise. To evaluate our proposed method, 545 subjects of which 364 were normal and 181 were severe OSA patients obtained from the MrOS sleep study (Visit 1) database were used with the kfold cross-validation technique. The accuracy of 79.45% for OSA severity classification with sensitivity, specificity, and F-score was achieved. This is significantly higher than the results from the SVM classifier with RR Intervals and ECG derived respiration (EDR) signal feature extraction. The promising result shows that this proposed method is a good start for the detection of OSA severity from a single channel ECG which can be obtained from wearable devices at home and can also be applied to near realtime alerting systems such as before SCD occurs.

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Section: Introductionmentioning

confidence: 99%

Single Channel ECG for Obstructive Sleep Apnea Severity Detection Using a Deep Learning Approach

Banluesombatkul

Rakthanmanon

Wilaiprasitporn

2018

TENCON 2018 - 2018 IEEE Region 10 Conference

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“…Of course, it is not the first study presenting the tracheal audio registration. Other systems were presented and discussed in our previous paper [15] (see Discussion). The sensor appears to fit the wearable sensing paradigm, discussed in [35].…”

Section: Discussionmentioning

confidence: 99%

“…The second approach is based on the previously published algorithm for segmentation breathing episodes and classifying them as normal breathing or snoring [15]. If there is an interval greater that 10 seconds between subsequent detected episodes (after removing possible "crackles" and noises), we may count in the apnea indexes.…”

Section: Audio Data Analysis For Apnea Detectionmentioning

confidence: 99%

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Joint Apnea and Body Position Analysis for Home Sleep Studies Using a Wireless Audio and Motion Sensor

2020

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This study aims to evaluate the accuracy of a wireless tracheal audio sensor including the sleep body position measurement, as a possible tool to screen for obstructive sleep apnea (OSA) and also to distinguish among "positional" and "non-positional" sleep apnea patients. 30 adult subjects were asked to sleep naturally for a single night in the sleep laboratory, having simultaneous polysomnography (PSG) as a reference. The results were compared using, e.g., Pearson's and Lin's correlation coefficients, Bland-Altman plots, mean absolute error, intercept of the fitted linear model etc. We found the thresholding approach performed on normalized audio energy data achieved mean absolute error of 5.7, and average difference was −2.0. Pearson's and Lin's R coefficients were 0.92 and 0.70, respectively. The qualitative approach reached 86% accuracy (sensitivity of 96%, and specificity of 76%) when setting a binary border at the level of AHI=15 (combined normal + mild versus combined moderate + severe cases). We also checked the distribution of apneas depending on the body position. Our sensor showed a mean ± SE difference between supine apnea index and non-supine apnea index of 8.3 ± 3.2, while PSG reference of 11.2 ± 3.8. The proposed sensor might be a good complement for home sleep studies, being less disturbing and allowing for longitudinal observations and reliably showing positional OSA. PSG is the gold standard in diagnosing OSA; however, it requires to spend a night in a lab with medical staff, it provides short observation, the cost of the study is very high, and it is less suitable for children. This is why a reliable screening method is needed in sleep medicine. INDEX TERMS Obstructive sleep apnea, apnea-hypopnea index, home sleep study, tracheal sounds, audio processing, supine sleep, non-supine sleep

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“…In a recently published study, M lynczak et al . [ 24 ] used a wireless acoustic sensor placed on the tracheal notch to measure breathing sounds during sleep. The study investigated the accuracy of a new method in differentiating between normal breathing sounds and snoring episodes.…”

Section: Clinical Applications For Sleep Studiesmentioning

confidence: 99%

The use of tracheal sounds for the diagnosis of sleep apnoea

Penzel

Sabil

2017

Breathe

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Tracheal sounds have been the subject of many research studies. In this review, we describe the state of the art, original work relevant to upper airways obstruction during sleep, and ongoing research concerning the methods used when analysing tracheal sounds. Tracheal sound sensors are a simple and noninvasive means of measurement and are more reliable than other breathing sensors. Developments in acoustic processing techniques and enhancements in tracheal sound signals over the past decade have led to improvements in the accuracy and clinical relevance of diagnoses based on this technology. Past and current research suggests that they may have a significant role in the diagnosis of obstructive sleep apnoea.Key pointsTracheal sounds are currently a topic of significant interest but are not yet used in most routine sleep study systems.Measured at the suprasternal notch, tracheal sounds can provide reliable information on breathing sounds, snoring sounds and respiratory efforts.Tracheal sounds may be used as a noninvasive method of studying abnormalities of the upper airways during wakefulness.Educational aimsTo understand the principles of tracheal sound measurement and analysis.To highlight the importance of tracheal sounds for the diagnosis of sleep apnoea–hypopnoea syndrome.To present the most relevant clinical studies that have validated the use of tracheal sound sensors and to make future clinical validation studies possible.

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Detecting Breathing and Snoring Episodes Using a Wireless Tracheal Sensor—A Feasibility Study

Abstract: The system opens unexplored possibilities in sleep monitoring and study enabling a multinight recording strategy involving the collection and analysis of abundant data from thousands of people.

Cited by 24 publications

References 35 publications

Single Channel ECG for Obstructive Sleep Apnea Severity Detection Using a Deep Learning Approach

Single Channel ECG for Obstructive Sleep Apnea Severity Detection Using a Deep Learning Approach

Joint Apnea and Body Position Analysis for Home Sleep Studies Using a Wireless Audio and Motion Sensor

The use of tracheal sounds for the diagnosis of sleep apnoea

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