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.
In disorders such as sleep apnea, sleep is fragmented with frequent EEG-arousal (EEGA) as determined via changes in the sleep-electroencephalogram. EEGA is a poorly understood, complicated phenomenon which is critically important in studying the mysteries of sleep. In this paper we study the information flow between the left and right hemispheres of the brain during the EEGA as manifested through inter-hemispheric asynchrony (IHA) of the surface EEG. EEG data (using electrodes A1/C4 and A2/C3 of international 10-20 system) was collected from 5 subjects undergoing routine polysomnography (PSG). Spectral correlation coefficient (R) was computed between EEG data from two hemispheres for delta-delta(0.5-4 Hz), theta-thetas(4.1-8 Hz), alpha-alpha(8.1-12 Hz) & beta-beta(12.1-25 Hz) frequency bands, during EEGA events. EEGA were graded in 3 levels as (i) micro arousals (3-6 s), (ii) short arousals (6.1-10 s), & (iii) long arousals (10.1-15 s). Our results revealed that in beta band, IHA increases above the baseline after the onset of EEGA and returns to the baseline after the conclusion of event. Results indicated that the duration of EEGA events has a direct influence on the onset of IHA. The latency (L) between the onset of arousals and IHA were found to be L=2plusmn0.5 s (for micro arousals), 4plusmn2.2 s (short arousals) and 6.5plusmn3.6 s (long arousals)
Polysomnography (PSG), which incorporates measures of sleep with measures of EEG arousal, air flow, respiratory movement and oxygenation, is universally regarded as the reference standard in diagnosing sleep-related respiratory diseases such as obstructive sleep apnoea syndrome. Over 15 channels of physiological signals are measured from a subject undergoing a typical overnight PSG session. The signals often suffer from data losses, interferences and artefacts. In a typical sleep scoring session, artefact-corrupted signal segments are visually detected and removed from further consideration. This is a highly time-consuming process, and subjective judgement is required for the job. During typical sleep scoring sessions, the target is the detection of segments of diagnostic interest, and signal restoration is not utilized for distorted segments. In this paper, we propose a novel framework for artefact detection and signal restoration based on the redundancy among respiratory flow signals. We focus on the air flow (thermistor sensors) and nasal pressure signals which are clinically significant in detecting respiratory disturbances. The method treats the respiratory system and other organs that provide respiratory-related inputs/outputs to it (e.g., cardiovascular, brain) as a possibly nonlinear coupled-dynamical system, and uses the celebrated Takens embedding theorem as the theoretical basis for signal prediction. Nonlinear prediction across time (self-prediction) and signals (cross-prediction) provides us with a mechanism to detect artefacts as unexplained deviations. In addition to detection, the proposed method carries the potential to correct certain classes of artefacts and restore the signal. In this study, we categorize commonly occurring artefacts and distortions in air flow and nasal pressure measurements into several groups and explore the efficacy of the proposed technique in detecting/recovering them. The results we obtained from a database of clinical PSG signals indicated that the proposed technique can detect artefacts/distortions with a sensitivity >88.3% and specificity >92.4%. This work has the potential to simplify the work done by sleep scoring technicians, and also to improve automated sleep scoring methods.
Obstructive sleep apnea (OSA) is characterized by upper airway obstructions known as apnea/hypopnea events. Narrowing of the upper airway during or near the vicinity of apnea/hypopnea causes the spectrum of the snores to shift to higher frequencies. Using an instrumentation quality wideband (WB) microphone (4Hz-100kHz), we previously demonstrated that potentially diagnostically useful frequency shifts could be detected even in regions beyond the human hearing range. WB-microphone based systems are expensive and not available for home use or population screening application. In this paper we explore the feasibility of using smart phones to analyze snoring sounds in the 20Hz-22kHz band to identify events of upper airway obstructions. Modern smart phones have internal microphones with bandwidths up to 22kHz, above the nominal human hearing range, and provide a good platform for sound acquisition and processing. For the work of this paper we used a Samsung Galaxy S3 phone and recorded overnight respiratory sound data from 8 patients undergoing routine Polysomnography (PSG) study in a hospital. Our target was to develop models to classify each standard 30 second epoch of data as non-apnea or apnea. Using 700 epochs we developed logistic regression models with the input as snoring sound features and the outputs as the diagnostic classification of each event (apnea/non-apnea). Models developed within a 20Hz-15kHz band had accuracies of 89-93%, sensitivities 70-78% and kappa index ranging 0.75-0.83 on validation data set. When the same models were developed on the 20Hz-22kHz frequency band the improved performance shows accuracies 94- 97%, sensitivities 93-100%, and kappa ranging 0.86-0.91. The study shows that smart phones based high frequency band (15-22kHz) of snoring sounds carry information about the upper airway obstructions. Our non-contact, smart phone based snoring sound technology has potential to identify upper airway obstructions.
Obstructive sleep apnea (OSA) is one of the most common sleep disorders. It is characterized by repetitive obstruction of the upper airways during sleep. The frequency of such events can range up to hundreds of events per sleep-hour. Full closure of the airways is termed apnea, and a partial closure is known as hypopnea. The number of apnea/hypopnea events per hour is known as the AHI-index, and is used by clinical community as a measure of the severity of OSA. OSA, when untreated, presents as a major public health concern throughout the world. OSA patients use health facilities at twice the average rate (Delaive, Roos, Manfreda, & Kryger, 1998), causing huge pressures on national healthcare systems. OSA is associated with serious complications such as cardiovascular disease, stroke, (Barber & Quan, 2002; Kryger, 2000,), and sexual impotence. It also causes cognitive deficiencies, low IQ in children, fatigue, and accidents. Australian Sleep Association reported (ASA, 1999) that in the state of New South Wales alone 11,000–43,000 traffic accidents per year were attributable to untreated-OSA.Request access from your librarian to read this chapter's full text.
Scoring of Macro Sleep Architecture (MSA) is a critical process in assessing several sleep disorders. MSA is defined as classification of sleep into three major states of sleep, State Wake, State REM and State NREM. Existing methods of MSA analysis require the recording of six channels of electro physiological signals such as the EEG, EOG and EMG. They depend on the manual scoring of overnight data records using the R&K Criteria (1968), developed for visual analysis of signals based on morphological features. Manual analysis of MSA is tedious, subjective and suffers from both inter and intra scorer variability In addition to this due to dependency of MSA on several biological signals, makes it impossible to incorporate in portable apnea screening devices. Non-availability of MSA ham pers these devices accuracy making them non-acceptable among medical community. In this paper we propose a novel method for MSA analysis, which requires just one channel of only EEG data. We also develop a fully automated, objective MSA analysis technique, which uses a single one-dimensional slice of the Bisprectrum of EEG, representing a nonlinear transformation of a system function that can be considered as the EEG generator. The method was evaluated on an overnight clinical database of 23 patients. The results were compared with those obtained by an experienced human scorer. The method proposed in this paper led to agreements in the range of 70°.4-87%, comparable to that possible between two expert human scorers.
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