Automatic sleep onset detection using single EEG sensor.

Sleep has been shown to be imperative for the health and well-being of an individual. To design intelligent sleep management tools, such as the music-induce sleep-aid device, automatic detection of sleep onset is critical. In this work, we propose a simple yet accurate method for sleep onset prediction, which merely relies on Electroencephalogram (EEG) signal acquired from a single frontal electrode in a wireless headband. The proposed method first extracts energy power ratio of theta (4-8Hz) and alpha (8-12Hz) bands along a 3-second shifting window, then calculates the slow wave of each frequency band along the time domain. The resulting slow waves are then fed to a rule-based engine for sleep onset detection. To evaluate the effectiveness of the approach, polysomnographic (PSG) and headband EEG signals were obtained from 20 healthy adults, each of which underwent 2 sessions of sleep events. In total, data from 40 sleep events were collected. Each recording was then analyzed offline by a PSG technologist via visual observation of PSG waveforms, who annotated sleep stages N1 and N2 by using the American Academy of Sleep Medicine (AASM) scoring rules. Using this as the gold standard, our approach achieved a 87.5% accuracy for sleep onset detection. The result is better or at least comparable to the other state of the art methods which use either multi-or single- channel based data. The approach has laid down the foundations for our future work on developing intelligent sleep aid devices.

Ballistocardiography with fiber optic sensor in headrest position: a feasibility study and a new processing algorithm.

Ballistocardiography (BCG) is a promising unobtrusive method for home e-healthcare systems, and has attracted increasing interest in recent years along with technological advances in related biomedical, electrical engineering and computer science fields. While existing systems have investigated the efficacy of BCG setups in bed, backrest, seat or scale positions, we propose to study BCG in headrest position that will allow new practical and portable applications. To this end, we designed and implemented a multi-modality sensing system including a high-sensitivity microbend fiber optic BCG sensor. In this preliminary study, we have collected multi-modality physiological data on 3 human subjects. We ran extensive analysis on BCG in correlation with ECG, and identified special characteristics of the signal in the new BCG setup. The result suggests that new appropriate computing techniques are necessary for accurately recovering the heart beat signal. Therefore, we developed a novel algorithm for heart beat detection. We evaluate the algorithm with the data and demonstrate that it can accurately compute heart rate intervals in the headrest BCG despite significant signal distortion.

Optimizing low-frequency common spatial pattern features for multi-class classification of hand movement directions.

Recent studies have demonstrated that hand movement directions can be decoded from low-frequency electroencephalographic (EEG) signals. This paper proposes a novel framework that can optimally select dyadic filter bank common spatial pattern (CSP) features in low-frequency band (0-8 Hz) for multi-class classification of four orthogonal hand movement directions. The proposed framework encompasses EEG signal enhancement, dyadic filter bank CSP feature extraction, fuzzy mutual information (FMI)-based feature selection, and one-versus-rest Fisher's linear discriminant analysis. Experimental results on data collected from seven human subjects show that (1) signal enhancement can boost accuracy by at least 4%; (2) low-frequency band (0-8 Hz) can adequately and effectively discriminate hand movement directions; and (3) dyadic filter bank CSP feature extraction and FMI-based feature selection are indispensable for analyzing hand movement directions, increasing accuracy by 6.06%, from 60.02% to 66.08%.

Impact of obstructive sleep apnea on sleep-wake stage ratio.

Patients with obstructive sleep apnea (OSA) experience fragmented sleep and exhibit different sleep architectures. While polysomnographic metrics for quantifying sleep architecture are studied, there is little information about the impact of OSA on the ratio of different sleep-wake stages (wake, W; rapid eye movement, REM; non-REM stages 1 to 3, N1 to N3). This study, therefore, aims to investigate the relationship between apnea-hypopnea index (AHI, a measure of OSA severity) and all possible ratios of sleep-wake stages. Sleep architectures of 24 adult subjects with suspected OSA were constructed according to the American Academy of Sleep Medicine scoring manual, and subsequently analyzed through various correlation (Pearson, Spearman, and Kendall) and regression (linear, logarithmic, exponential, and power-law) approaches. Results show a statistically significant positive, linear and monotonic correlation between AHI and REM/N3, as well as between AHI and N1/W (p-values < 0.05). These findings imply that patients with increased severity of OSA may spend more time in REM than deep sleep, and in light sleep than wake (or less time in deep sleep than REM, and in wake than light sleep). A power-law regression model may possibly explain the relationships of AHI-REM/N3 and AHI-N1/W, and predict the value of AHI using REM/N3 or N1/W.

Antibacterial action of dispersed single-walled carbon nanotubes on Escherichia coli and Bacillus subtilis investigated by atomic force microscopy.

Single-walled carbon nanotubes (SWCNTs) exhibit strong antibacterial activities. Direct contact between bacterial cells and SWCNTs may likely induce cell damages. Therefore, the understanding of SWCNT-bacteria interactions is essential in order to develop novel SWCNT-based materials for their potential environmental, imaging, therapeutic, and military applications. In this preliminary study, we utilized atomic force microscopy (AFM) to monitor dynamic changes in cell morphology and mechanical properties of two typical bacterial models (gram-negative Escherichia coli and gram-positive Bacillus subtilis) upon incubation with SWCNTs. The results demonstrated that individually dispersed SWCNTs in solution develop nanotube networks on the cell surface, and then destroy the bacterial envelopes with leakage of the intracellular contents. The cell morphology changes observed on air dried samples are accompanied by an increase in cell surface roughness and a decrease in surface spring constant. To mimic the collision between SWCNTs and cells, a sharp AFM tip of 2 nm was chosen to introduce piercings on the cell surface. No clear physical damages were observed if the applied force was below 10 nN. Further analysis also indicates that a single collision between one nanotube and a bacterial cell is unlikely to introduce direct physical damage. Hence, the antibacterial activity of SWCNTs is the accumulation effect of large amount of nanotubes through interactions between SWCNT networks and bacterial cells.

Analysis and modeling of snore source flow with its preliminary application to synthetic snore generation.

With the emerging use of snore properties for clinical purposes, there is a need to understand the characteristics of snore source flow (SF)-the acoustic source in snore production. This paper attempts to analyze and model both SF and its derivative (SFD), along with its preliminary application to the generation of synthetic snores. SFs and SFDs were extracted from natural snores via an iterative adaptive inverse filtering approach, and subsequently parameterized into various time- and amplitude-based parameters to quantify the oscillatory maneuvers of snore excitation source (ES). The SF and SFD waveforms were also, respectively, modeled using the first and second derivatives of the Gaussian probability density function. Subjective and objective measures, including paired comparison score and sum-of-squared error, were assessed to appraise the performance of SFD model in producing natural-sounding snores. Results consistently show that: 1) the shapes of SF pulse are different among snores and can be associated with the dynamic biomechanical properties (e.g., compliance and elasticity) of ES; 2) changes to the SF or SFD pulse shape can affect the snore properties, both acoustically and perceptually; and 3) the proposed SFD model can generate close-to-natural sounding snores. Further research in this area can potentially yield valuable benefits to snore-oriented applications.

Role of upper airway dimensions in snore production: acoustical and perceptual findings.

While considerable efforts have been expended to develop snore-driven markers for detecting obstructive sleep apnea (OSA), there is little emphasis on the relationship between the human upper airway (UA) dimensions and the attributes of snores. This paper aims to investigate the acoustical and perceptual impacts of changing the cross-sectional areas (CSA) of the pharynx and oral cavity on the production of snores. Synthetic snores were generated based on the source-filter theory, whereas natural snores were recorded from 40 snorers during nocturnal polysomnography. First formant frequency (F1), spectral peak frequency (PF), and psychoacoustic metrics (loudness, sharpness, roughness, fluctuation strength, and annoyance) of CSA perturbations were examined, completed with diagnostic appraisal of F1 and PF for single- and mixed-gender groupings using the receiver operating characteristic curve analysis. Results show that (1) narrowing the pharyngeal airway consistently increases F1, but not for PF; and (2) altering the airway dimensions yield no considerable differences in perception of snore sounds, but indirectly affect the psychoacoustics by changing the dynamics of snore source flow. Diagnostic outcomes for all groupings (p-value < 0.0001) demonstrate that F1 is more capable of distinguishing apneic and benign snorers than PF due to the close association of F1 with the UA anatomical structures. Correlation exists between the UA anatomy and the properties of snores; there is a promising future for developing snore-driven screening tools for OSA.

Investigation of obstructive sleep apnea using nonlinear mode interactions in nonstationary snore signals.

Acoustic studies on snoring sounds have recently drawn attention as a potential alternative to polysomnography in the diagnosis of obstructive sleep apnea (OSA). This paper investigates the feasibility of using nonlinear coupling between frequency modes in snore signals via wavelet bicoherence (WBC) analysis for screening of OSA. Two novel markers (PF1 and PSF), which are frequency modes with high nonlinear coupling strength in their respective WBC spectrum, are proposed to differentiate between apneic and benign snores in same- or both-gender snorers. Snoring sounds were recorded from 40 subjects (30 apneic and 10 benign) by a hanging microphone, and subsequently preprocessed within a wavelet transform domain. Forty inspiratory snores (30 as training and 10 as test data) from each subject were examined. Results demonstrate that nonlinear mode interactions in apneic snores are less self-coupled and usually occupy higher and wider frequency ranges than that of benign snores. PF1 and PSF are indicative of apneic and benign snores (p < 0.0001), with optimal thresholds of PF1 = 285 Hz and PSF = 492 Hz (for both genders combined), as well as sensitivity and specificity values between 85.0 and 90.7%, respectively, outperforming the conventional diagnostic indicator (spectral peak frequency, PF = 243-275 Hz, sensitivity = 77.7-79.7%, specificity = 72.0-78.0%, p < 0.0001). Relationships between apnea-hypopnea index and the proposed markers could likely take the functional form of exponential or power. Perspectives on nonlinear dynamics analysis of snore signals are promising for further research and development of a reliable and inexpensive diagnostic tool for OSA.

Snore signal enhancement and activity detection via translation-invariant wavelet transform.

Acoustical properties of snores have been widely studied as a potentially cost-effective and reliable alternative to diagnosing obstructive sleep apnea (OSA), with a common recognition that the diagnostic accuracy depends heavily on the snore signal quality and intelligibility. This paper proposes a novel preprocessing system that performs two critical tasks concurrently in a translation-invariant wavelet transform domain. These tasks include enhancement of snore signals via a level-correlation-dependent (LCD) threshold, and identification of snore presence through a snore activity (SA) detector. Various experiments were conducted to warrant the robustness of the system in terms of theoretical statistics quality, signal-to-noise ratio, mean opinion score, and clinical usefulness in detecting OSA. Results indicate that the proposed LCD threshold and SA detector are highly comparable to the existing denoising methodologies using level-dependent threshold and segmentation approaches using short-time energy and zero-crossing rate, yielding the best results in all the experiments. Given the strong initial performance of the proposed preprocessing system for snore signals, continued exploration in this direction could potentially lead to additional improvement in signal integrity, thereby increasing the diagnostic accuracy for OSA.

Using psychoacoustics of snoring sounds to screen for obstructive sleep apnea.

This paper aims to develop a reliable psychoacoustic evaluation tool for measuring snoring sounds. Thirteen snoring sound samples were assessed by 25 listeners, in terms of psychoacoustic metrics (loudness, sharpness, roughness, fluctuation strength, and annoyance) using a 7-point semantic differential scale with bipolar adjective pairs. The accuracy of this study was quantified by receiver operating characteristic curves, together with Pearson's product-moment and Spearman's rank correlation coefficients. Results consistently show that loudness, annoyance, and roughness are the best three metrics for classifying apneic and benign snorers as they can achieve high diagnostic accuracy and good correlation with apnea-hypopnea index, body mass index, and neck circumference. With these encouraging results, further research and development of an effective psychoacoustic evaluation tool is promising.

Could formant frequencies of snore signals be an alternative means for the diagnosis of obstructive sleep apnea?

OBJECTIVE: To study the feasibility of using acoustic signatures in snore signals for the diagnosis of obstructive sleep apnea (OSA). METHODS: Snoring sounds of 30 apneic snorers (24 males; 6 females; apnea-hypopnea index, AHI=46.9+/-25.7events/h) and 10 benign snorers (6 males; 4 females; AHI=4.6+/-3.4events/h) were captured in a sleep laboratory. The recorded snore signals were preprocessed to remove noise, and subsequently, modeled using a linear predictive coding (LPC) technique. Formant frequencies (F1, F2, and F3) were extracted from the LPC spectrum for analysis. The accuracy of this approach was assessed using receiver operating characteristic curves and notched box plots. The relationship between AHI and F1 was further explored via regression analysis. RESULTS: Quantitative differences in formant frequencies between apneic and benign snores are found in same- or both-gender snorers. Apneic snores exhibit higher formant frequencies than benign snores, especially F1, which can be related to the pathology of OSA. This study yields a sensitivity of 88%, a specificity of 82%, and a threshold value of F1=470Hz that best differentiate apneic snorers from benign snorers (both gender combined). CONCLUSION: Acoustic signatures in snore signals carry information for OSA diagnosis, and snore-based analysis might potentially be a non-invasive and inexpensive diagnostic approach for mass screening of OSA.