Automatic detection of whole night snoring events using non-contact microphone

A multi-branch convolutional neural network for snoring detection based on audio

Obstructive sleep apnea (OSA) is associated with various health complications, and snoring is a prominent characteristic of this disorder. Therefore, the exploration of a concise and effective method for detecting snoring has consistently been a crucial aspect of sleep medicine. As the easily accessible data, the identification of snoring through sound analysis offers a more convenient and straightforward method. The objective of this study was to develop a convolutional neural network (CNN) for classifying snoring and non-snoring events based on audio. This study utilized Mel-frequency cepstral coefficients (MFCCs) as a method for extracting features during the preprocessing of raw data. In order to extract multi-scale features from the frequency domain of sound sources, this study proposes the utilization of a multi-branch convolutional neural network (MBCNN) for the purpose of classification. The network utilized asymmetric convolutional kernels to acquire additional information, while the adoption of one-hot encoding labels aimed to mitigate the impact of labels. The experiment tested the network's performance by utilizing a publicly available dataset consisting of 1,000 sound samples. The test results indicate that the MBCNN achieved a snoring detection accuracy of 99.5%. The integration of multi-scale features and the implementation of MBCNN, based on audio data, have demonstrated a substantial improvement in the performance of snoring classification.

Automatic snoring detection using a hybrid 1D-2D convolutional neural network

Snoring, as a prevalent symptom, seriously interferes with life quality of patients with sleep disordered breathing only (simple snorers), patients with obstructive sleep apnea (OSA) and their bed partners. Researches have shown that snoring could be used for screening and diagnosis of OSA. Therefore, accurate detection of snoring sounds from sleep respiratory audio at night has been one of the most important parts. Considered that the snoring is somewhat dangerously overlooked around the world, an automatic and high-precision snoring detection algorithm is required. In this work, we designed a non-contact data acquire equipment to record nocturnal sleep respiratory audio of subjects in their private bedrooms, and proposed a hybrid convolutional neural network (CNN) model for the automatic snore detection. This model consists of a one-dimensional (1D) CNN processing the original signal and a two-dimensional (2D) CNN representing images mapped by the visibility graph method. In our experiment, our algorithm achieves an average classification accuracy of 89.3%, an average sensitivity of 89.7%, an average specificity of 88.5%, and an average AUC of 0.947, which surpasses some state-of-the-art models trained on our data. In conclusion, our results indicate that the proposed method in this study could be effective and significance for massive screening of OSA patients in daily life. And our work provides an alternative framework for time series analysis.

Dependable algorithm for visualizing snoring duration through acoustic analysis: A pilot study

Snoring is a nuisance for the bed partners of people who snore and is also associated with chronic diseases. Estimating the snoring duration from a whole-night sleep period is challenging. The authors present a dependable algorithm for visualizing snoring durations through acoustic analysis. Both instruments (Sony digital recorder and smartphone's SnoreClock app) were placed within 30 cm from the examinee's head during the sleep period. Subsequently, spectrograms were plotted based on audio files recorded from Sony recorders. The authors thereby developed an algorithm to validate snoring durations through visualization of typical snoring segments. In total, 37 snoring recordings obtained from 6 individuals were analyzed. The mean age of the participants was 44.6 ± 9.9 years. Every recorded file was tailored to a regular 600-second segment and plotted. Visualization revealed that the typical features of the clustered snores in the amplitude domains were near-isometric spikes (most had an ascending-descending trend). The recorded snores exhibited 1 or more visibly fixed frequency bands. Intervals were noted between the snoring clusters and were incorporated into the whole-night snoring calculation. The correlative coefficients of snoring rates from digitally recorded files examined between Examiners A and B were higher (0.865, P < .001) than those with SnoreClock app and Examiners (0.757, P < .001; 0.787, P < .001, respectively). A dependable algorithm with high reproducibility was developed for visualizing snoring durations.

Accurate Contactless Sleep Apnea Detection Framework with Signal Processing and Machine Learning Methods

The detection of sleep apnea is critical for assessing sleep quality. It is also a proven biometric in diagnosing cardiovascular and other diseases. Recent studies have shown that radar-based non-contact vital sign monitoring system can effectively detect sleep apnea. However, the detection accuracy in the current study still needs to be improved. In this paper, we propose a sleep apnea detection framework based on FMCW radar. First, the radar system is employed to record the sleep data throughout the night with polysomnography (PSG) comparison. Then, in order to extract more accurate respiratory signal from the raw radar data, the signal processing methods are investigated to solve the observed discontinuity phenomenon. Finally, machine learning methods are adopted. The apneic and not-apneic events are classified accurately by selecting effective features of respiratory signal. As shown in the experimental results, the proposed system could achieve a good classification performance with an accuracy of 95.53%, a sensitivity of 72.60%, a specificity of 97.32%, a Kappa of 0.68, and an F-score of 0.84.

Towards Sleep Study Automation: Detection Evaluation of Respiratory-Related Events

The diagnosis of sleep disordered breathing depends on the detection of several respiratory-related events: apneas, hypopneas, snores, or respiratory event-related arousals from sleep studies. While a number of automatic detection methods have been proposed, reproducibility of these methods has been an issue, in part due to the absence of a generally accepted protocol for evaluating their results. With sleep measurements this is usually treated as a classification problem and the accompanying issue of localization is not treated as similarly critical. To address these problems we present a detection evaluation protocol that is able to qualitatively assess the match between two annotations of respiratory-related events. This protocol relies on measuring the relative temporal overlap between two annotations in order to find an alignment that maximizes their F1-score at the sequence level. This protocol can be used in applications which require a precise estimate of the number of events, total event duration, and a joint estimate of event number and duration. We assess its application using a data set that contains over 10,000 manually annotated snore events from 9 subjects, and show that when using the American Academy of Sleep Medicine Manual standard, two sleep technologists can achieve an F1-score of 0.88 when identifying the presence of snore events. In addition, we drafted rules for marking snore boundaries and showed that one sleep technologist can achieve F1-score of 0.94 at the same tasks. Finally, we compared our protocol against the protocol that is used to evaluate sleep spindle detection and highlighted the differences.

Current and future strategies for diagnostic and management of obstructive sleep apnea

INTRODUCTION

Obstructive sleep apnea (OSA) is a common sleep disorder with multiple comorbidities including hypertension, diabetes, and cardiovascular disorders. Detected based on an overnight sleep study is called polysomnography (PSG); OSA still remains undiagnosed in majority of the population mainly attributed to lack of awareness. To overcome the limitations posed by PSG such as patient discomfort and overnight hospitalization, newer technologies are being explored. In addition, challenges associated with current management of OSA using continuous positive airway pressure (CPAP), etc. presents several pitfalls.

AREAS COVERED

Conventional and modern detection/management techniques including PSG, CPAP, smart wearable/pillows, bio-motion sensors, etc., have both pros and cons. To fulfill the limitations in OSA diagnostics, there is an imperative need for new technology for screening of symptomatic and more importantly asymptomatic OSA patients to reduce the risk of several associated life-threatening comorbidities. In this line, molecular marker-based diagnostics have shown great promises.

EXPERT OPINION

A detailed overview is presented on the OSA management and diagnostic approaches and recent advances in the molecular screening methods. The potentials of biomarker-based detection and its limitations are also portrayed and a comparison between the standard, current modern approaches, and promising futuristic technologies for OSA diagnostics and management is set forth.ABBREVIATIONS AHI: Apnea hypopnea index; AI: artificial intelligence; CAM: Cell adhesion molecules; CPAP: Continuous Positive Airway Pressure; COVID-19: Coronavirus Disease 2019; CVD: Cardiovascular disease; ELISA: Enzyme linked immunosorbent assay; HSAT: Home sleep apnea testing; IR-UWB: Impulse radio-ultra wideband; MMA: maxillomandibular advancement; PSG: Polysomnography; OSA: Obstructive sleep apnea; SOD: Superoxide dismutase; QD: Quantum dot.

Audio source separation to reduce sleeping partner sounds: a simulation study

Objective.When recording a subject in an at-home environment for sleep evaluation or for other breathing disorder diagnoses using non-contact microphones, the breathing recordings (audio signals) can be distorted by sounds such as TV, outside noise, or air-conditioners. If two people are sleeping together, both may produce breathing/snoring sounds that need to be separated. In this study, we present signal processing and source separation algorithms for the enhancement of individual breathing/snoring audio signals in a simulated environment.Approach.We developed a computer simulation of mixed signals derived from genuine nocturnal recordings of 110 subjects. Two main source separation approaches were tested: (1) changing the basis vectors for the mixtures in the time domain (principal and independent component analysis, PCA/ICA) and (2) converting the mixtures to their time-frequency representations (degenerate un-mixing estimation technique, DUET). In addition to these source separation techniques, a beamforming approach was tested.Main results.The separation results with a reverberation time of 0.15 s and zero SNR between signals showed good performance (mean source to interference ratio (SIR): DUET = 12.831 dB, ICA = 3.388 dB, PCA = 4.452 dB), and for beamforming (SIR = -0.304 dB). To evaluate our source separation results, we propose two new measures: an evaluation measure based on a spectral similarity score (mel-SID) between the target source and its estimation (after separation) and a breathing energy ratio measure (BER). The results with the new proposed measures yielded comparable conclusions (mel-SID: DUET = 1.320, ICA = 2.732, PCA = 1.927, and beamforming = 2.590, BER: DUET = 10.241 dB, ICA = 0.270 dB, PCA = -2.847 dB, and beamforming = -1.151 dB), but better differentiated the differences between the performance of the algorithms. The DUET is superior on all measures. Its main advantage is that it only uses two microphones for separation.Significance. The separated audio signal can thus contribute to a more informed diagnosis of sleep-related and non-sleep-related diseases. The Institutional Review Committee of Soroka University Medical Center approved this study protocol (protocol number 10141) and all methods were performed in accordance with the relevant guidelines and regulations.

Screening Severe Obstructive Sleep Apnea in Children with Snoring

Efficient screening for severe obstructive sleep apnea (OSA) is important for children with snoring before time-consuming standard polysomnography. This retrospective cross-sectional study aimed to compare clinical variables, home snoring sound analysis, and home sleep pulse oximetry on their predictive performance in screening severe OSA among children who habitually snored. Study 1 included 9 (23%) girls and 30 (77%) boys (median age of 9 years). Using univariate logistic regression models, 3% oxygen desaturation index (ODI3) ≥ 6.0 events/h, adenoidal-nasopharyngeal ratio (ANR) ≥ 0.78, tonsil size = 4, and snoring sound energy of 801–1000 Hz ≥ 22.0 dB significantly predicted severe OSA in descending order of odds ratio. Multivariate analysis showed that ODI3 ≥ 6.0 events/h independently predicted severe pediatric OSA. Among several predictive models, the combination of ODI3, tonsil size, and ANR more optimally screened for severe OSA with a sensitivity of 91% and a specificity of 94%. In Study 2 (27 (27%) girls and 73 (73%) boys; median age, 7 years), this model was externally validated to predict severe OSA with an accuracy of 76%. Our results suggested that home sleep pulse oximetry, combined with ANR, can screen for severe OSA more optimally than ANR and tonsil size among children with snoring.

The acoustical and perceptual features of snore-related sounds in patients with obstructive sleep apnea sleeping with the dynamic mandibular advancement system MATRx plus®

PURPOSE

The effect of snoring on the bed partner can be studied through the evaluation of in situ sound records by the bed partner or unspecialized raters as a proxy of real-life snoring perception. The aim was to characterize perceptual snore events through acoustical features in patients with obstructive sleep apnea (OSA) with an advanced mandibular position.

METHODS

Thirty-minute sound samples of 29 patients with OSA were retrieved from overnight, in-home recordings of a study to validate the MATRx plus® dynamic mandibular advancement system. Three unspecialized raters identified sound events and classified them as noise, snore, or breathing. The raters provided ratings for classification certainty and annoyance. Data were analyzed with respect to respiratory phases, and annoyance.

RESULTS

When subdividing perceptual events based on respiratory phase, the logarithm-transformed Mean Power, Spectral Centroid, and Snore Factor differed significantly between event types, although not substantially for the spectral centroid. The variability within event type was high and distributions suggested the presence of subpopulations. The general linear model (GLM) showed a significant patient effect. Inspiration segments occurred in 65% of snore events, expiration segments in 54%. The annoyance correlated with the logarithm of mean power (r = 0.48) and the Snore Factor (0.46).

CONCLUSION

Perceptual sound events identified by non-experts contain a non-negligible mixture of expiration and inspiration phases making the characterization through acoustical features complex. The present study reveals that subpopulations may exist, and patient-specific features need to be introduced.

Validation of snoring detection using a smartphone app

Purpose

Snoring is closely related to obstructive sleep apnea in adults. The increasing abundance and availability of smartphone technology has facilitated the examination and monitoring of snoring at home through snoring apps. However, the accuracy of snoring detection by snoring apps is unclear. This study explored the snoring detection accuracy of Snore Clock—a paid snoring detection app for smartphones.

Methods

Snoring rates were detected by smartphones that had been installed with the paid app Snore Clock. The app provides information on the following variables: sleep duration, snoring duration, snoring loudness (in dB), maximum snoring loudness (in dB), and snoring duration rate (%). In brief, we first reviewed the snoring rates detected by Snore Clock; thereafter, an ear, nose, and throat specialist reviewed the actual snoring rates by using the playback of the app recordings.

Results

In total, the 201 snoring records of 11 patients were analyzed. Snoring rates measured by Snore Clock and those measured manually were closely correlated (r = 0.907). The mean snoring detection accuracy rate of Snore Clock was 95%, with a positive predictive value, negative predictive value, sensitivity, and specificity of 65% ± 35%, 97% ± 4%, 78% ± 25%, and 97% ± 4%, respectively. However, the higher the snoring rates, the higher were the false-negative rates for the app.

Conclusion

Snore Clock is compatible with various brands of smartphones and has a high predictive value for snoring. Based on the strong correlation between Snore Clock and manual approaches for snoring detection, these findings have validated that Snore Clock has the capacity for at-home snoring detection.

Perceptual snoring as a basis for a psychoacoustical modeling and clinical patient profiling

PURPOSE

The perceptual burden and social nuisance for mainly the co-sleeper can affect the relationship between snorer and bedpartner. Mandibular advancement devices (MAD) are commonly recommended to treat sleep-related breathing such as snoring or sleep apnea. There is no consensus about the definition of snoring particularly with MAD, which is essential for assessing the effectiveness of treatment. We aimed to stablish a notion of perceptual snoring with MAD in place.

METHODS

Sound samples, each 30 min long, were recorded during in-home, overnight, automatic mandibular repositioning titration studies in a population of 29 patients with obstructive sleep apnea syndrome (OSAS) from a clinical trial carried out to validate the MATRx plus. Three unspecialized and calibrated raters identified sound events and classified them as noise, snore, or breathing as well as providing scores for classification certainty and annoyance. Data were analyzed with respect to expiration-inspiration, duration, annoyance, and classification certainty.

RESULTS

A Fleiss' kappa (>0.80) and correlation duration of events (>0.90) between raters were observed. Prevalence of all breath sounds: snore 55.6% (N = 6398), breathing sounds 31.7% (N = 3652), and noise 9.3% (N = 1072). Inspiration occurs in 88.3% of events, 96.8% contained at least on expiration phase. Snore and breath events had similar duration, respectively 2.58s (sd 1.43) and 2.41s (sd 1.22). Annoyance is lowest for breathing events (8.00 sd 0.98) and highest for snore events (4.90 sd 1.92) on a VAS from zero to ten.

CONCLUSION

Perceptual sound events can be a basis for analysis in a psychosocial context. Perceived snoring occurs during both expiration as well as inspiration. Substantial amount of snoring remains despite repositioning of the mandible aimed at the reduction of AHI-ODI.

Audio-based snore detection using deep neural networks

BACKGROUND AND OBJECTIVE

Snoring is a prevalent phenomenon. It may be benign, but can also be a symptom of obstructive sleep apnea (OSA) a prevalent sleep disorder. Accurate detection of snoring may help with screening and diagnosis of OSA.

METHODS

We introduce a snore detection algorithm based on the combination of a convolutional neural network (CNN) and a recurrent neural network (RNN). We obtained audio recordings of 38 subjects referred to a clinical center for a sleep study. All subjects were recorded by a total of 5 microphones placed at strategic positions around the bed. The CNN was used to extract features from the sound spectrogram, while the RNN was used to process the sequential CNN output and to classify the audio events to snore and non-snore events. We also addressed the impact of microphone placement on the performance of the algorithm.

RESULTS

The algorithm achieved an accuracy of 95.3 ± 0.5%, a sensitivity of 92.2 ± 0.9%, and a specificity of 97.7 ± 0.4% over all microphones in snore detection on our data set including 18412 sound events. The best accuracy (95.9%) was observed from the microphone placed about 70 cm above the subject's head and the worst (94.4%) was observed from the microphone placed about 130 cm above the subject's head.

CONCLUSION

Our results suggest that our method detects snore events from audio recordings with high accuracy and that microphone placement does not have a major impact on detection performance.

Estimating a Sleep Apnea Hypopnea Index Based on the ERB Correlation Dimension of Snore Sounds

This paper proposes a new perspective of analyzing non-linear acoustic characteristics of the snore sounds. According to the ERB (Equivalent Rectangular Bandwidth) scale used in psychoacoustics, the ERB correlation dimension (ECD) of the snore sound was computed to feature different severity levels of sleep apnea hypopnea syndrome (SAHS). For the training group of 93 subjects, snore episodes were manually segmented and the ECD parameters of the snores were extracted, which established the gaussian mixture models (GMM). The nocturnal snore sound of the testing group of another 120 subjects was tested to detect SAHS snores, thus estimating the apnea hypopnea index (AHI), which is called AHIECD. Compared to the AHIPSG value of the gold standard polysomnography (PSG) diagnosis, the estimated AHIECD achieved an accuracy of 87.5% in diagnosis the SAHS severity levels. The results suggest that the ECD vectors can be effective parameters for screening SAHS.

Development of a support vector machine learning and smart phone Internet of Things-based architecture for real-time sleep apnea diagnosis

Background

The breathing disorder obstructive sleep apnea syndrome (OSAS) only occurs while asleep. While polysomnography (PSG) represents the premiere standard for diagnosing OSAS, it is quite costly, complicated to use, and carries a significant delay between testing and diagnosis.

Methods

This work describes a novel architecture and algorithm designed to efficiently diagnose OSAS via the use of smart phones. In our algorithm, features are extracted from the data, specifically blood oxygen saturation as represented by SpO2. These features are used by a support vector machine (SVM) based strategy to create a classification model. The resultant SVM classification model can then be employed to diagnose OSAS. To allow remote diagnosis, we have combined a simple monitoring system with our algorithm. The system allows physiological data to be obtained from a smart phone, the data to be uploaded to the cloud for processing, and finally population of a diagnostic report sent back to the smart phone in real-time.

Results

Our initial evaluation of this algorithm utilizing actual patient data finds its sensitivity, accuracy, and specificity to be 87.6%, 90.2%, and 94.1%, respectively.

Discussion

Our architecture can monitor human physiological readings in real time and give early warning of abnormal physiological parameters. Moreover, after our evaluation, we find 5G technology offers higher bandwidth with lower delays ensuring more effective monitoring. In addition, we evaluate our algorithm utilizing real-world data; the proposed approach has high accuracy, sensitivity, and specific, demonstrating that our approach is very promising.

Conclusions

Experimental results on the apnea data in University College Dublin (UCD) Database have proven the efficiency and effectiveness of our methodology. This work is a pilot project and still under development. There is no clinical validation and no support. In addition, the Internet of Things (IoT) architecture enables real-time monitoring of human physiological parameters, combined with diagnostic algorithms to provide early warning of abnormal data.

Use of mandibular advancement devices for the treatment of primary snoring with or without obstructive sleep apnea (OSA): A systematic review

The aim of this review was to systematically evaluate the available scientific evidence on the benefit of mandibular advancement devices (MADs) in the treatment of primary snoring (PS). From 905 initially identified articles, 18 were selected. Papers that provided indirect information regarding obstructive sleep apnea syndrome (OSAS) and/or sleep breathing disorders (SBD) were included. Information was obtained on monoblock and duoblock appliances from the selected studies. The devices were most commonly able to achieve 50%-70% of the maximum mandibular protrusion. The frequently used outcome measurements were the apnea-hypopnea index, Epworth sleepiness scale, and oxygen desaturation index, which all yielded positive post-treatment results. The most common side effects were temporomandibular joint pain and excessive salivation, which improved with time. Our findings indicated that the use of MADs, even with varying designs, improved outcomes in all the reported patient populations (PS, OSAS, and SBD). Despite the lack of studies on PS, the available evidence supports the use of MADs for treatment of PS. Snoring should be treated from a preventive and psychosocial perspective to avoid progression to more severe diseases that could have a significant medical and economic impact.

Relationship between Vowel Sound Features and Pharyngeal Airway Cross-Sectional Area during Normal Breathing

Assessment of the pharyngeal airway is becoming important for delivering personalized treatment and better management of sleep apnea. However, evaluation of the pharyngeal airway area is difficult in the current state of the art. It is essential to use simple and accessible technology to measure the pharyngeal airway area. As vowel sounds are generated by vocal cords vibration and characterized by the pharyngeal airway, vowel sounds have the potential to evaluate the pharyngeal airway area. The objective of this study was to investigate the relationship between acoustic features of vowel sounds and the pharyngeal airway cross-sectional area (PAXSA) between soft palate and glottis. Twenty subjects were included in this study whose PAXSA was measured by acoustic pharyngometry. Vowel sounds were recorded with a microphone while lying supine. Vowel sound average power was calculated in different frequency ranges of 100-3000 Hz, 100-500 Hz, 500-1000 Hz, 1000-1500 Hz, 1500-2000 Hz, 2000-2500 Hz and 2500-3000 Hz. Statistical analysis showed that the decreases in the PAXSA were strongly correlated with the higher average power of vowel sounds in all frequency ranges. These results showed that individuals with low PAXSA might articulate the vowel in higher intensity. Clinical Relevance - This study demonstrates that the pharyngeal airway cross-sectional area during normal breathing has a significant effect on vowel articulation. Thus, vowel sound features can be used to estimate the resting pharyngeal airway cross-sectional area.

Are annoyance scores based on sound pressure levels suitable for snoring assessment in the home environment?

Purpose

An objective statement about the annoyance of snoring can be made with the Psychoacoustic Snore Score (PSS). The PSS was developed based on subjective assessments and is strongly influenced by observed sound pressure levels. Robustness against day-to-day interfering noises is a fundamental requirement for use at home. This study investigated whether or not the PSS is suitable for use in the home environment.

Methods

Thirty-six interfering noises, which commonly occur at night, were played in the acoustic laboratory in parallel with 5 snoring sounds. The interfering noises were each presented at sound pressure levels ranging from 25 to 55 dB(A), resulting in 3255 distinct recordings. Annoyance was then assessed using the PSS.

Results

In the case of minimally annoying snoring sounds, interfering noises with a sound pressure level of 25 dB(A) caused significant PSS changes from 40 to 55 dB(A) for annoying snoring sounds. If the interfering noise was another snoring sound, the PSS was more robust depending on the sound pressure level of the interfering noise up to 10 dB(A). Steady (no-peak) interfering noises influenced the PSS more strongly than peak noises.

Conclusions

The PSS is significantly distorted by quiet interfering noises. Its meaningfulness therefore depends strongly on the acoustic environment. It may therefore be assumed that scores dependent on sound pressure level are suitable for measurements when there is minimal ambient noise, as in the sleep laboratory. However, for measurements where noise is incalculable, as in the home environment, interfering noises may distort the results.

A New Feature with the Potential to Detect the Severity of Obstructive Sleep Apnoea via Snoring Sound Analysis

The severity of obstructive sleep apnoea (OSA) is diagnosed with polysomnography (PSG), during which patients are monitored by over 20 physiological sensors overnight. These sensors often bother patients and may affect patients’ sleep and OSA. This study aimed to investigate a method for analyzing patient snore sounds to detect the severity of OSA. Using a microphone placed at the patient’s bedside, the snoring and breathing sounds of 22 participants were recorded while they simultaneously underwent PSG. We examined some features from the snoring and breathing sounds and examined the correlation between these features and the snore-specific apnoea-hypopnea index (ssAHI), defined as the number of apnoea and hypopnea events during the hour before a snore episode. Statistical analyses revealed that the ssAHI was positively correlated with the Mel frequency cepstral coefficients (MFCC) and volume information (VI). Based on clustering results, mild snore sound episodes and snore sound episodes from mild OSA patients were mainly classified into cluster 1. The results of clustering severe snore sound episodes and snore sound episodes from severe OSA patients were mainly classified into cluster 2. The features of snoring sounds that we identified have the potential to detect the severity of OSA.

Automatic Audio-Based Classification of Patient Inhaler Use: A Pharmacy Based Study

Chronic respiratory diseases may be controlled through the delivery of medication to the airways and lungs using an inhaler. However, adherence to correct inhaler technique is poor, which impedes patients from receiving maximum clinical benefit from their medication. In this study, the Inhaler Compliance Assessment device was employed to record audio of patients using a Diskus dry powder inhaler. An algorithm that classifies inhaler sounds (blister, inhalation, interference) was developed to automatically assess patient adherence from these inhaler audio recordings. The presented algorithm employed audio-based signal processing methods and statistical modeling in the form of quadratic discriminant analysis (QDA). A total of 350 audio recordings were obtained from 70 patients. The acquired audio dataset was split evenly for training and testing. A total accuracy of 85.35% was obtained (testing dataset) for this 3-class classification system. A sensitivity of 89.22% and 70% was obtained for inhalation and blister detection respectively. This approach may have significant clinical impact by providing healthcare professionals with an efficient, objective method of monitoring patient adherence to inhaler treatment.

Non-contact diagnosis of obstructive sleep apnea using impulse-radio ultra-wideband radar

While full-night polysomnography is the gold standard for the diagnosis of obstructive sleep apnea, its limitations include a high cost and first-night effects. This study developed an algorithm for the detection of respiratory events based on impulse-radio ultra-wideband radar and verified its feasibility for the diagnosis of obstructive sleep apnea. A total of 94 subjects were enrolled in this study (23 controls and 24, 14, and 33 with mild, moderate, and severe obstructive sleep apnea, respectively). Abnormal breathing detected by impulse-radio ultra-wideband radar was defined as a drop in the peak radar signal by ≥30% from that in the pre-event baseline. We compared the abnormal breathing index obtained from impulse-radio ultra-wideband radar and apnea–hypopnea index (AHI) measured from polysomnography. There was an excellent agreement between the Abnormal Breathing Index and AHI (intraclass correlation coefficient = 0.927). The overall agreements of the impulse-radio ultra-wideband radar were 0.93 for Model 1 (AHI ≥ 5), 0.91 for Model 2 (AHI ≥ 15), and 1 for Model 3 (AHI ≥ 30). Impulse-radio ultra-wideband radar accurately detected respiratory events (apneas and hypopneas) during sleep without subject contact. Therefore, impulse-radio ultra-wideband radar may be used as a screening tool for obstructive sleep apnea.

A Deep Learning Model for Snoring Detection and Vibration Notification Using a Smart Wearable Gadget

Snoring, a form of sleep-disordered breathing, interferes with sleep quality and quantity, both for the person who snores and often for the person who sleeps with the snorer. Poor sleep caused by snoring can create significant physical, mental, and economic problems. A simple and natural solution for snoring is to sleep on the side, instead of sleeping on the back. In this project, a deep learning model for snoring detection is developed and the model is transferred to an embedded system—referred to as the listener module—to automatically detect snoring. A novel wearable gadget is developed to apply a vibration notification on the upper arm until the snorer sleeps on the side. The gadget is rechargeable, and it is wirelessly connected to the listener module using low energy Bluetooth. A smartphone app—connected to the listener module using home Wi-Fi—is developed to log the snoring events with timestamps, and the data can be transferred to a physician for treating and monitoring diseases such as sleep apnea. The snoring detection deep learning model has an accuracy of 96%. A prototype system consisting of the listener module, the wearable gadget, and a smartphone app has been developed and tested successfully.

Recurrent Neural Network for Classification of Snoring and Non-Snoring Sound Events

Obstructive sleep apnea (OSA) is a disorder that affects up to 38% of the western population. It is characterized by repetitive episodes of partial or complete collapse of the upper airway during sleep. These episodes are almost always accompanied by loud snoring. Questionnaires such as STOP-BANG exploit snoring to screen for OSA. However, they are not quantitative and thus do not exploit its full potential. A method for automatic detection of snoring in whole-night recordings is required to enable its quantitative evaluation. In this study, we propose such a method. The centerpiece of the proposed method is a recurrent neural network for modeling of sequential data with variable length. Mel-frequency cepstral coefficients, which were extracted from snoring and non-snoring sound events, were used as inputs to the proposed network. A total of 20 subjects referred to clinical sleep recording were also recorded by a microphone that was placed 70 cm from the top end of the bed. These recordings were used to assess the performance of the proposed method. When it comes to the detection of snoring events, our results show that the proposed method has an accuracy of 95%, sensitivity of 92%, and specificity of 98%. In conclusion, our results suggest that the proposed method may improve the process of snoring detection and with that the process of OSA screening. Follow-up clinical studies are required to confirm this potential.

Sleep staging using nocturnal sound analysis

Sleep staging is essential for evaluating sleep and its disorders. Most sleep studies today incorporate contact sensors that may interfere with natural sleep and may bias results. Moreover, the availability of sleep studies is limited, and many people with sleep disorders remain undiagnosed. Here, we present a pioneering approach for rapid eye movement (REM), non-REM, and wake staging (macro-sleep stages, MSS) estimation based on sleep sounds analysis. Our working hypothesis is that the properties of sleep sounds, such as breathing and movement, within each MSS are different. We recorded audio signals, using non-contact microphones, of 250 patients referred to a polysomnography (PSG) study in a sleep laboratory. We trained an ensemble of one-layer, feedforward neural network classifiers fed by time-series of sleep sounds to produce real-time and offline analyses. The audio-based system was validated and produced an epoch-by-epoch (standard 30-sec segments) agreement with PSG of 87% with Cohen's kappa of 0.7. This study shows the potential of audio signal analysis as a simple, convenient, and reliable MSS estimation without contact sensors.

Multimodal Ambulatory Sleep Detection Using LSTM Recurrent Neural Networks

Unobtrusive and accurate ambulatory methods are needed to monitor long-term sleep patterns for improving health. Previously developed ambulatory sleep detection methods rely either in whole or in part on self-reported diary data as ground truth, which is a problem, since people often do not fill them out accurately. This paper presents an algorithm that uses multimodal data from smartphones and wearable technologies to detect sleep/wake state and sleep onset/offset using a type of recurrent neural network with long-short-term memory (LSTM) cells for synthesizing temporal information. We collected 5580 days of multimodal data from 186 participants and compared the new method for sleep/wake classification and sleep onset/offset detection to, first, nontemporal machine learning methods and, second, a state-of-the-art actigraphy software. The new LSTM method achieved a sleep/wake classification accuracy of 96.5%, and sleep onset/offset detection F₁ scores of 0.86 and 0.84, respectively, with mean absolute errors of 5.0 and 5.5 min, res-pectively, when compared with sleep/wake state and sleep onset/offset assessed using actigraphy and sleep diaries. The LSTM results were statistically superior to those from nontemporal machine learning algorithms and the actigraphy software. We show good generalization of the new algorithm by comparing participant-dependent and participant-independent models, and we show how to make the model nearly realtime with slightly reduced performance.

Objective Assessment of Patient Inhaler User Technique Using an Audio-Based Classification Approach

Many patients make critical user technique errors when using pressurised metered dose inhalers (pMDIs) which reduce the clinical efficacy of respiratory medication. Such critical errors include poor actuation coordination (poor timing of medication release during inhalation) and inhaling too fast (peak inspiratory flow rate over 90 L/min). Here, we present a novel audio-based method that objectively assesses patient pMDI user technique. The Inhaler Compliance Assessment device was employed to record inhaler audio signals from 62 respiratory patients as they used a pMDI with an In-Check Flo-Tone device attached to the inhaler mouthpiece. Using a quadratic discriminant analysis approach, the audio-based method generated a total frame-by-frame accuracy of 88.2% in classifying sound events (actuation, inhalation and exhalation). The audio-based method estimated the peak inspiratory flow rate and volume of inhalations with an accuracy of 88.2% and 83.94% respectively. It was detected that 89% of patients made at least one critical user technique error even after tuition from an expert clinical reviewer. This method provides a more clinically accurate assessment of patient inhaler user technique than standard checklist methods.

Detection of Snores Using Source Separation on an Emfit Signal

Snoring (SN) is an early sign of upper airway dysfunction, and it is strongly associated with obstructive sleep apnea. SN detection is important to monitor SN objectively and to improve the diagnostic sensitivity of sleep-disordered breathing. In this study, an automatic snore detection method using an electromechanical film transducer (Emfit) signal is presented. Representative polysomnographs of normal breathing and SN periods from 30 subjects were selected. Individual SN events were identified using source separation applying nonnegative matrix factorization deconvolution. The algorithm was evaluated using manual annotation of the polysomnographic recordings. According to our results, the sensitivity, and the positive predictive value of the developed method to reveal snoring from the Emfit signal were 82.81% and 86.29%, respectively. Compared to other approaches, our method adapts to the individual spectral snoring profile of the subject rather than matching a particular spectral profile, estimates the snoring intensity, and obtains the specific spectral profile of the snores in the epoch. Additionally, no training is necessary. This study suggests that it is possible to detect individual SN events with Emfit mattress, which can be used as a contactless alternative to more conventional methods such as piezo-snore sensors or microphones.

Spatial acoustic radiation of respiratory sounds for sleep evaluation

Body posture has an effect on sleeping quality and breathing disorders and therefore it is important to be recognized for the completion of the sleep evaluation process. Since humans have a directional acoustic radiation pattern, it is hypothesized that microphone arrays can be used to recognize different body postures, which is highly practical for sleep evaluation applications that already measure respiratory sounds using distant microphones. Furthermore, body posture may have an effect on distant microphone measurement; hence, the measurement can be compensated if the body posture is correctly recognized. A spherical harmonics decomposition approach to the spatial acoustic radiation is presented, assuming an array of eight microphones in a medium-sized audiology booth. The spatial sampling and reconstruction of the radiation pattern is discussed, and a final setup for the microphone array is recommended. A case study is shown using recorded segments of snoring and breathing sounds of three human subjects in three body postures in a silent but not anechoic audiology booth.

SNORAP: A Device for the Correction of Impaired Sleep Health by Using Tactile Stimulation for Individuals with Mild and Moderate Sleep Disordered Breathing

Sleep physiology and sleep hygiene play significant roles in maintaining the daily lives of individuals given that sleep is an important physiological need to protect the functions of the human brain. Sleep disordered breathing (SDB) is an important disease that disturbs this need. Snoring and Obstructive Sleep Apnea Syndrome (OSAS) are clinical conditions that affect all body organs and systems that intermittently, repeatedly, with at least 10 s or more breathing stops that decrease throughout the night and disturb sleep integrity. The aim of this study was to produce a new device for the treatment of patients especially with position and rapid eye movement (REM)-dependent mild and moderate OSAS. For this purpose, the main components of the device (the microphone (snore sensor), the heart rate sensor, and the vibration motor, which we named SNORAP) were applied to five volunteer patients (male, mean age: 33.2, body mass index mean: 29.3). After receiving the sound in real time with the microphone, the snoring sound was detected by using the Audio Fingerprint method with a success rate of 98.9%. According to the results obtained, the severity and the number of the snoring of the patients using SNORAP were found to be significantly lower than in the experimental conditions in the apnea hypopnea index (AHI), apnea index, hypopnea index, in supine position’s AHI, and REM position’s AHI before using SNORAP (Paired Sample Test, p < 0.05). REM sleep duration and nocturnal oxygen saturation were significantly higher when compared to the group not using the SNORAP (Paired Sample Test, p < 0.05).

Noncontact Sleep Study by Multi-Modal Sensor Fusion

Polysomnography (PSG) is considered as the gold standard for determining sleep stages, but due to the obtrusiveness of its sensor attachments, sleep stage classification algorithms using noninvasive sensors have been developed throughout the years. However, the previous studies have not yet been proven reliable. In addition, most of the products are designed for healthy customers rather than for patients with sleep disorder. We present a novel approach to classify sleep stages via low cost and noncontact multi-modal sensor fusion, which extracts sleep-related vital signals from radar signals and a sound-based context-awareness technique. This work is uniquely designed based on the PSG data of sleep disorder patients, which were received and certified by professionals at Hanyang University Hospital. The proposed algorithm further incorporates medical/statistical knowledge to determine personal-adjusted thresholds and devise post-processing. The efficiency of the proposed algorithm is highlighted by contrasting sleep stage classification performance between single sensor and sensor-fusion algorithms. To validate the possibility of commercializing this work, the classification results of this algorithm were compared with the commercialized sleep monitoring device, ResMed S+. The proposed algorithm was investigated with random patients following PSG examination, and results show a promising novel approach for determining sleep stages in a low cost and unobtrusive manner.

Estimation of macro sleep stages from whole night audio analysis

During routine sleep diagnostic procedure, sleep is broadly divided into three states: rapid eye movement (REM), non-REM (NREM) states, and wake, frequently named macro-sleep stages (MSS). In this study, we present a pioneering attempt for MSS detection using full night audio analysis. Our working hypothesis is that there might be differences in sound properties within each MSS due to breathing efforts (or snores) and body movements in bed. In this study, audio signals of 35 patients referred to a sleep laboratory were recorded and analyzed. An additional 178 subjects were used to train a probabilistic time-series model for MSS staging across the night. The audio-based system was validated on 20 out of the 35 subjects. System accuracy for estimating (detecting) epoch-by-epoch wake/REM/NREM states for a given subject is 74% (69% for wake, 54% for REM, and 79% NREM). Mean error (absolute difference) was 36±34 min for detecting total sleep time, 17±21 min for sleep latency, 5±5% for sleep efficiency, and 7±5% for REM percentage. These encouraging results indicate that audio-based analysis can provide a simple and comfortable alternative method for ambulatory evaluation of sleep and its disorders.

Automatic picking of snore events from overnight breath sound recordings

Snoring is one of the earliest symptoms of Obstructive Sleep Apnea (OSA). However, the unavailability of an objective snore definition is a major obstacle in developing automated snore analysis system for OSA screening. The objectives of this paper is to develop a method to identify and extract snore sounds from a continuous sound recording following an objective definition of snore that is independent of snore loudness. Nocturnal sounds from 34 subjects were recorded using a non-contact microphone and computerized data-acquisition system. Sound data were divided into non-overlapping training (n = 21) and testing (n = 13) datasets. Using training dataset an Artificial Neural Network (ANN) classifier were trained for snore and non-snore classification. Snore sounds were defined based on the key observation that sounds perceived as `snores' by human are characterized by repetitive packets of energy that are responsible for creating the vibratory sound peculiar to snorers. On the testing dataset, the accuracy of ANN classifier ranged between 86 - 89%. Our results indicate that it is possible to define snoring using loudness independent, objective criteria, and develop automated snore identification and extraction algorithms.

Obstructive Sleep Apnea Screening Using a Piezo-Electric Sensor

In this study, we propose a novel method for obstructive sleep apnea (OSA) detection using a piezo-electric sensor. OSA is a relatively common sleep disorder. However, more than 80% of OSA patients remain undiagnosed. We investigated the feasibility of OSA assessment using a single-channel physiological signal to simplify the OSA screening. We detected both snoring and heartbeat information by using a piezo-electric sensor, and snoring index (SI) and features based on pulse rate variability (PRV) analysis were extracted from the filtered piezo-electric sensor signal. A support vector machine (SVM) was used as a classifier to detect OSA events. The performance of the proposed method was evaluated on 45 patients from mild, moderate, and severe OSA groups. The method achieved a mean sensitivity, specificity, and accuracy of 72.5%, 74.2%, and 71.5%; 85.8%, 80.5%, and 80.0%; and 70.3%, 77.1%, and 71.9% for the mild, moderate, and severe groups, respectively. Finally, these results not only show the feasibility of OSA detection using a piezo-electric sensor, but also illustrate its usefulness for monitoring sleep and diagnosing OSA.

Breathing and Snoring Sound Characteristics during Sleep in Adults

STUDY OBJECTIVES

Sound level meter is the gold standard approach for snoring evaluation. Using this approach, it was established that snoring intensity (in dB) is higher for men and is associated with increased apnea-hypopnea index (AHI). In this study, we performed a systematic analysis of breathing and snoring sound characteristics using an algorithm designed to detect and analyze breathing and snoring sounds. The effect of sex, sleep stages, and AHI on snoring characteristics was explored.

METHODS

We consecutively recruited 121 subjects referred for diagnosis of obstructive sleep apnea. A whole night audio signal was recorded using noncontact ambient microphone during polysomnography. A large number (> 290,000) of breathing and snoring (> 50 dB) events were analyzed. Breathing sound events were detected using a signal-processing algorithm that discriminates between breathing and nonbreathing (noise events) sounds.

RESULTS

Snoring index (events/h, SI) was 23% higher for men (p = 0.04), and in both sexes SI gradually declined by 50% across sleep time (p < 0.01) independent of AHI. SI was higher in slow wave sleep (p < 0.03) compared to S2 and rapid eye movement sleep; men have higher SI in all sleep stages than women (p < 0.05). Snoring intensity was similar in both genders in all sleep stages and independent of AHI. For both sexes, no correlation was found between AHI and snoring intensity (r = 0.1, p = 0.291).

CONCLUSIONS

This audio analysis approach enables systematic detection and analysis of breathing and snoring sounds from a full night recording. Snoring intensity is similar in both sexes and was not affected by AHI.

Diagnostic accuracy of audio-based seizure detection in patients with severe epilepsy and an intellectual disability

We evaluated the performance of audio-based detection of major seizures (tonic-clonic and long generalized tonic) in adult patients with intellectual disability living in an institute for residential care.

METHODS

First, we checked in a random sample (n=17, 102 major seizures) how many patients have recognizable sounds during these seizures. In the second part of this trial, we followed 10 patients (who had major seizures with recognizable sounds) during four weeks with an acoustic monitoring system developed by CLB ('CLB-monitor') and video camera. In week 1, we adapted the sound detection threshold until, per night, a maximum of 20 sounds was found. During weeks 2-4, we selected the epilepsy-related sounds and performed independent video verification and labeling ('snoring', 'laryngeal contraction') of the seizures. The video images were also fully screened for false negatives. In the third part, algorithms in the CLB-monitor detected one specific sound (sleep-related snoring) to illustrate the value of automatic sound recognition.

RESULTS

Part 1: recognizable sounds (louder than whispering) occurred in 23 (51%) of the 45 major seizures, 20 seizures (45%) were below this threshold, and 2 (4%) were without any sound. Part 2: in the follow-up group (n=10, 112 major seizures; mean: 11.2, range: 1-30), we found a mean sensitivity of 0.81 (range: 0.33-1.00) and a mean positive predictive value of 0.40 (range: 0.06-1.00). All false positive alarms (mean value: 1.29 per night) were due to minor seizures. We missed 4 seizures (3%) because of lack of sound and 10 (9%) because of sounds below the system threshold. Part 3: the machine-learning algorithms in the CLB-monitor resulted in an overall accuracy for 'snoring' of 98.3%.

CONCLUSIONS

Audio detection of major seizures is possible in half of the patients. Lower sound detection thresholds may increase the proportion of suitable candidates. Human selection of seizure-related sounds has a high sensitivity and moderate positive predictive value because of minor seizures which do not need intervention. Algorithms in the CLB-monitor detect seizure-related sounds and may be used alone or in multimodal systems.

Breathing rate estimation during sleep using audio signal analysis

Sleep is associated with important changes in respiratory rate and ventilation. Currently, breathing rate (BR) is measured during sleep using an array of contact and wearable sensors, including airflow sensors and respiratory belts; there is need for a simplified and more comfortable approach to monitor respiration. Here, we present a new method for BR evaluation during sleep using a non-contact microphone. The basic idea behind this approach is that during sleep the upper airway becomes narrower due to muscle relaxation, which leads to louder breathing sounds that can be captured via ambient microphone. In this study we developed a signal processing algorithm that emphasizes breathing sounds, extracts breathing-related features, and estimates BR during sleep. A comparison between audio-based BR estimation and BR calculated using the traditional (gold-standard) respiratory belts during in-laboratory polysomnography (PSG) study was performed on 204 subjects. Pearson's correlation between subjects' averaged BR of the two approaches was R=0.97. Epoch-by-epoch (30 s) BR comparison revealed a mean relative error of 2.44% and Pearson's correlation of 0.68. This study shows reliable and promising results for non-contact BR estimation.

Can we discriminate between apnea and hypopnea using audio signals?

Obstructive sleep apnea (OSA) affects up to 14% of the population. OSA is characterized by recurrent apneas and hypopneas during sleep. The apnea-hypopnea index (AHI) is frequently used as a measure of OSA severity. In the current study, we explored the acoustic characteristics of hypopnea in order to distinguish it from apnea. We hypothesize that we can find audio-based features that can discriminate between apnea, hypopnea and normal breathing events. Whole night audio recordings were performed using a non-contact microphone on 44 subjects, simultaneously with the polysomnography study (PSG). Recordings were segmented into 2015 apnea, hypopnea, and normal breath events and were divided to design and validation groups. A classification system was built using a 3-class cubic-kernelled support vector machine (SVM) classifier. Its input is a 36-dimensional audio-based feature vector that was extracted from each event. Three-class accuracy rate using the hold-out method was 84.7%. A two-class model to separate apneic events (apneas and hypopneas) from normal breath exhibited accuracy rate of 94.7%. Here we show that it is possible to detect apneas or hypopneas from whole night audio signals. This might provide more insight about a patient's level of upper airway obstruction during sleep. This approach may be used for OSA severity screening and AHI estimation.

Snoring sound classification from respiratory signal

Snoring is common in the general population and the irregularity could lead to the presence of Obstructive sleep apnea. Diagnosis of OSA could therefore be made by snoring sound analysis. However, there is still a shortage of robust methods to automatically detect snoring sounds without the need to calibrate for every individual. In this paper, a novel method based on neural network is proposed to classify breathing sound episodes from snoring and non-snoring sound segments. Our snore detection algorithm was applied to the tracheal sounds of nine individuals with different OSA severities. On the testing dataset, the classifier achieved a sensitivity and specificity of 95.9% and 97.6% respectively. Our results indicate that using such a method could help to detect snoring sounds with high accuracy which would be useful in the diagnosis of sleep apnea.

Snoring Sounds Predict Obstruction Sites and Surgical Response in Patients with Obstructive Sleep Apnea Hypopnea Syndrome

Snoring sounds generated by different vibrators of the upper airway may be useful indicators of obstruction sites in patients with obstructive sleep apnea hypopnea syndrome (OSAHS). This study aimed to investigate associations between snoring sounds, obstruction sites, and surgical responses (≥50% reduction in the apnea-hypopnea index [AHI] and <10 events/hour) in patients with OSAHS. This prospective cohort study recruited 36 OSAHS patients for 6-hour snoring sound recordings during in-lab full-night polysomnography, drug-induced sleep endoscopy (DISE), and relocation pharyngoplasty. All patients received follow-up polysomnography after 6 months. Fifteen (42%) patients with at least two complete obstruction sites defined by DISE were significantly, positively associated with maximal snoring sound intensity (40-300 Hz; odds ratio [OR], 1.25, 95% confidence interval [CI] 1.05-1.49) and body mass index (OR, 1.48, 95% CI 1.02-2.15) after logistic regression analysis. Tonsil obstruction was significantly, inversely correlated with mean snoring sound intensity (301-850 Hz; OR, 0.84, 95% CI 0.74-0.96). Moreover, baseline tonsil obstruction detected by either DISE or mean snoring sound intensity (301-850 Hz), and AHI could significantly predict the surgical response. Our findings suggest that snoring sound detection may be helpful in determining obstruction sites and predict surgical responses.

Predicting Obstructive Sleep Apnea with Periodic Snoring Sound Recorded at Home

STUDY OBJECTIVES

The cost-effectiveness of diagnosing obstructive sleep apnea (OSA) could be improved by using a preliminary screening method among subjects with no suspicion of other sleep disorders. We aimed to evaluate the diagnostic value of periodic snoring sound recorded at home.

METHODS

We included 211 subjects, aged 18-83 (130 men), who were referred to our laboratory for suspicion of OSA, and had a technically successful overnight polygraphy, measured with the Nox T3 Sleep Monitor (Nox Medical, Iceland) with a built-in microphone. We analyzed the percentage of periodic snoring during the home sleep apnea study.

RESULTS

Apnea-hypopnea index (AHI) ranged from 0.1 to 116 events/h and the percentage of periodic snoring from 1% to 97%. We found a strong positive correlation (r = 0.727, p < 0.001) between periodic snoring and AHI. The correlation was slightly stronger among female, younger, and obese subjects. The best threshold value of the periodic snoring for predicting an AHI > 15 events/h with as high sensitivity as possible was found to be 15%. There, sensitivity was 93.3%, specificity 35.1%, and negative predictive value 75.0%.

CONCLUSIONS

According to our results, it is possible to set a periodic snoring threshold (15% or more) for the subject to advance to further sleep studies. Together with medical history and prior to more expensive studies, measuring periodic snoring at home is a simple and useful method for predicting the probability of OSA, in particular among women who are often unaware of their apnea-related snoring.

Detection of breathing sounds during sleep using non-contact audio recordings

Evaluation of respiratory activity during sleep is essential in order to reliably diagnose sleep disorder breathing (SDB); a condition associated with serious cardio-vascular morbidity and mortality. In the current study, we developed and validated a robust automatic breathing-sounds (i.e. inspiratory and expiratory sounds) detection system of audio signals acquired during sleep. Random forest classifier was trained and tested using inspiratory/expiratory/noise events (episodes), acquired from 84 subjects consecutively and prospectively referred to SDB diagnosis in sleep laboratory and in at-home environment. More than 560,000 events were analyzed, including a variety of recording devices and different environments. The system's overall accuracy rate is 88.8%, with accuracy rate of 91.2% and 83.6% in in-laboratory and at-home environments respectively, when classifying between inspiratory, expiratory, and noise classes. Here, we provide evidence that breathing-sounds can be reliably detected using non-contact audio technology in at-home environment. The proposed approach may improve our understanding of respiratory activity during sleep. This in return, will improve early SDB diagnosis and treatment.

How to measure snoring? A comparison of the microphone, cannula and piezoelectric sensor

The objective of this study was to compare to each other the methods currently recommended by the American Academy of Sleep Medicine (AASM) to measure snoring: an acoustic sensor, a piezoelectric sensor and a nasal pressure transducer (cannula). Ten subjects reporting habitual snoring were included in the study, performed at Landspitali-University Hospital, Iceland. Snoring was assessed by listening to the air medium microphone located on a patient's chest, compared to listening to two overhead air medium microphones (stereo) and manual scoring of a piezoelectric sensor and nasal cannula vibrations. The chest audio picked up the highest number of snore events of the different snore sensors. The sensitivity and positive predictive value of scoring snore events from the different sensors was compared to the chest audio: overhead audio (0.78, 0.98), cannula (0.55, 0.67) and piezoelectric sensor (0.78, 0.92), respectively. The chest audio was capable of detecting snore events with lower volume and higher fundamental frequency than the other sensors. The 200 Hz sampling rate of the cannula and piezoelectric sensor was one of their limitations for detecting snore events. The different snore sensors do not measure snore events in the same manner. This lack of consistency will affect future research on the clinical significance of snoring. Standardization of objective snore measurements is therefore needed. Based on this paper, snore measurements should be audio-based and the use of the cannula as a snore sensor be discontinued, but the piezoelectric sensor could possibly be modified for improvement.

Diurnal and seasonal variation of cough episodes in healthy young adults

SETTING

We developed an algorithm to assess recorded cough episodes and differentiate them from similar, non-cough sounds.

OBJECTIVE

To measure cough episodes in healthy young adults, cigarette smokers and non-smokers over a 24-hour recording period, during the course of normal activity.

DESIGN

The study subjects were students, aged 20-40 years old. 24-hour sound recordings were conducted by a portable recorder. Analysis used an algorithm that was developed and tested in the study.

RESULTS

Seventy students were recruited. Recordings included 2628 cough episodes in 1704 h of recording. The cough detection algorithm successfully detected 88.5% of recorded cough episodes and 95.6% of non-cough sounds. There was a clear tendency for more coughs among smokers. Autumn was the season with the highest mean cough episodes per day (58.65), while summer had the lowest (14.19). There was a strong correlation between self-reported cough episodes and recorded coughs. Cough episodes were significantly more frequent between noon and midnight (p < 0.0001).

CONCLUSION

There is a very large range in daily coughs among healthy young adults. During sleeping hours there are less cough episodes. In autumn and spring there are more cough episodes compared to summer and winter, probably secondary to environmental factors. In smokers, the coughing rate is relatively high. If the cough detection device will be able to discriminate between cough variants (i.e., healthy versus patient), and stringent validation will confirm sensitivity and specificity, valuable data from this device may ease the decision regarding medications, or any other changes in order to improve outcome.