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Marimuthu Palaniswami - One of the best experts on this subject based on the ideXlab platform.
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Characterization of chimeric surface submentalis EMG activity during Hypopneas in obstructive sleep apnea patients
2009 IEEE Toronto International Conference Science and Technology for Humanity (TIC-STH), 2009Co-Authors: MAK A. Daulatzai, Chandan K. Karmakar, Ahsan H. Khandoker, Marimuthu Palaniswami, Neela KhanAbstract:Polysomnogram (PSG) is the standard diagnostic test for the evaluation of sleep disorders. The current rules require surface (s) electromyography (EMG) of the submentalis muscle (SM) in order to document atonia during REM sleep. The sSM EMG signals reflect contracting motor units; the firing of the latter is a function of intrinsic neuromuscular characteristics of its component muscle fibers, and indeed forms the basis for the spectral properties. Here we have studied OSA patients with apnea-Hypopnea index (AHI) of <5, 5-10, 30-35, and 60+, and document, for the first time, a ¿Chimeric¿ sSM EMG activity phenotype during Hypopneas in Non-REM sleep. This unique pattern characteristically displays contiguous tonic-phasic segments of high activity and low activity or vice versa. We have analyzed the total duration, and other attributes of these hybrids in comparison with the normal awake and apnea/Hypopnea-free sleep periods. We document an inherent heterogeneity between Hypopneas, and between heterogeneous segments of the chimeras in OSA patients of varying AHI. This study emphasizes that rectified and filtered sSM EMG activity signals provide a novel, valid and useful metric in PSG evaluation which may be of clinical significance in sleep-related and other pathological conditions.
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Automated Scoring of Obstructive Sleep Apnea and Hypopnea Events Using Short-Term Electrocardiogram Recordings
IEEE Transactions on Information Technology in Biomedicine, 2009Co-Authors: Ahsan H. Khandoker, Jayavardhana Gubbi, Marimuthu PalaniswamiAbstract:Obstructive sleep apnea or Hypopnea causes a pause or reduction in airflow with continuous breathing effort. The aim of this study is to identify individual apnea and Hypopnea events from normal breathing events using wavelet-based features of 5-s ECG signals (sampling rate = 250 Hz) and estimate the surrogate apnea index (AI)/Hypopnea index (HI) (AHI). Total 82 535 ECG epochs (each of 5-s duration) from normal breathing during sleep, 1638 ECG epochs from 689 Hypopnea events, and 3151 ECG epochs from 1862 apnea events were collected from 17 patients in the training set. Two-staged feedforward neural network model was trained using features from ECG signals with leave-one-patient-out cross-validation technique. At the first stage of classification, events (apnea and Hypopnea) were classified from normal breathing events, and at the second stage, Hypopneas were identified from apnea. Independent test was performed on 16 subjects' ECGs containing 483 Hypopnea and 1352 apnea events. The cross-validation and independent test accuracies of apnea and Hypopnea detection were found to be 94.84% and 76.82%, respectively, for training set, and 94.72% and 79.77%, respectively, for test set. The Bland-Altman plots showed unbiased estimations with standard deviations of plusmn 2.19, plusmn 2.16, and plusmn 3.64 events/h for AI, HI, and AHI, respectively. Results indicate the possibility of recognizing apnea/Hypopnea events based on shorter segments of ECG signals.
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Lateral decubitus posture during sleep: Sub-groups of obstructive sleep apnea patients — therapeutic value of vertical position in OSA
2009 International Conference on Intelligent Sensors Sensor Networks and Information Processing (ISSNIP), 2009Co-Authors: MAK A. Daulatzai, Neela Khan, Ahsan H. Khandoker, Chandan Karmakar, Marimuthu PalaniswamiAbstract:The incidence of obstructive sleep apnea (OSA) is steadily increasing, but to date there is no efficacious universal treatment. Therefore, measures that ameliorate this condition need special attention. Avoidance of sleep in the supine (horizontal) position has a positive influence on frequency and severity of OSA. OSA patients are ¿Responders¿ when they respond to positional therapeutic measure, while those in whom sleeping vertically does not result in lowering of apnea Hypopnea index (AHI) are ¿Non-Responders¿. The aim of this study was to evaluate the number of apnea and Hypopnea in responders and non-responders. One hundred and seven adult OSA patients with varying AHI, i.e. from 5 to 117, were included. We recorded the number of apnea and Hypopnea in the supine position as well as in the lateral position of sleep. We categorized them in to different groups. We report, for the first time, four different groups of OSA patients in a seemingly so-called homogeneous population. One group of OSA patients, 57.0 %, responded to the lateral position in sleep and showed a decrease in their apnea and Hypopnea; a second group, 23.3 %, exhibited a decrease in their apnea but not in Hypopnea; a third group, 10.3 %, in whom the apnea increased but not Hypopnea, while a fourth group, 9.4%, showed an increase in both apnea and Hypopnea. The great majority of OSA suffers benefit from non-supine sleep position; it is recommended, therefore, that different measures be utilized to enhance sleep in the lateral position in these patients. However, the group of patients (group 4) who increase their apnea and Hypopnea in the non-supine position should be discouraged to adopt this sleeping posture; however, they should be encouraged to use different therapeutic methodology such as a mandibular splint and/or the tongue restraining device.
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Recognizing central and obstructive sleep apnea events from normal breathing events in ECG recordings
2008 Computers in Cardiology, 2008Co-Authors: Ahsan H. Khandoker, Jayavardhana Gubbi, Marimuthu PalaniswamiAbstract:Obstructive sleep apnea (OSA) causes a pause in airflow with continuing breathing effort. In contrast, central sleep apnea (CSA) event is not accompanied with breathing effort. The aim of this study is to differentiate CSA and OSA events from normal breathing events using wavelet based features of ECG signal over 5 second period. Total 164880 epochs(each of 5-second duration) from normal breathing events, 196 epochs from 116 CSA, 5281 epochs from 2173 OSA and 3073 epochs from 1563 Hypopnea events were selected from single lead ECGs (sampling rate=250 Hz). At the first stage of classification, apnea events were classified from normal breathing events and at the second stage, Hypopneas were identified from all apnea events and at final stage, CSA and OSA types were recognized at 98.96% accuracy. Results indicate the possibility of recognizing OSA/CSA events based on shorter segments of ECG signals.
Ahsan H. Khandoker - One of the best experts on this subject based on the ideXlab platform.
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Characterization of chimeric surface submentalis EMG activity during Hypopneas in obstructive sleep apnea patients
2009 IEEE Toronto International Conference Science and Technology for Humanity (TIC-STH), 2009Co-Authors: MAK A. Daulatzai, Chandan K. Karmakar, Ahsan H. Khandoker, Marimuthu Palaniswami, Neela KhanAbstract:Polysomnogram (PSG) is the standard diagnostic test for the evaluation of sleep disorders. The current rules require surface (s) electromyography (EMG) of the submentalis muscle (SM) in order to document atonia during REM sleep. The sSM EMG signals reflect contracting motor units; the firing of the latter is a function of intrinsic neuromuscular characteristics of its component muscle fibers, and indeed forms the basis for the spectral properties. Here we have studied OSA patients with apnea-Hypopnea index (AHI) of <5, 5-10, 30-35, and 60+, and document, for the first time, a ¿Chimeric¿ sSM EMG activity phenotype during Hypopneas in Non-REM sleep. This unique pattern characteristically displays contiguous tonic-phasic segments of high activity and low activity or vice versa. We have analyzed the total duration, and other attributes of these hybrids in comparison with the normal awake and apnea/Hypopnea-free sleep periods. We document an inherent heterogeneity between Hypopneas, and between heterogeneous segments of the chimeras in OSA patients of varying AHI. This study emphasizes that rectified and filtered sSM EMG activity signals provide a novel, valid and useful metric in PSG evaluation which may be of clinical significance in sleep-related and other pathological conditions.
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Automated Scoring of Obstructive Sleep Apnea and Hypopnea Events Using Short-Term Electrocardiogram Recordings
IEEE Transactions on Information Technology in Biomedicine, 2009Co-Authors: Ahsan H. Khandoker, Jayavardhana Gubbi, Marimuthu PalaniswamiAbstract:Obstructive sleep apnea or Hypopnea causes a pause or reduction in airflow with continuous breathing effort. The aim of this study is to identify individual apnea and Hypopnea events from normal breathing events using wavelet-based features of 5-s ECG signals (sampling rate = 250 Hz) and estimate the surrogate apnea index (AI)/Hypopnea index (HI) (AHI). Total 82 535 ECG epochs (each of 5-s duration) from normal breathing during sleep, 1638 ECG epochs from 689 Hypopnea events, and 3151 ECG epochs from 1862 apnea events were collected from 17 patients in the training set. Two-staged feedforward neural network model was trained using features from ECG signals with leave-one-patient-out cross-validation technique. At the first stage of classification, events (apnea and Hypopnea) were classified from normal breathing events, and at the second stage, Hypopneas were identified from apnea. Independent test was performed on 16 subjects' ECGs containing 483 Hypopnea and 1352 apnea events. The cross-validation and independent test accuracies of apnea and Hypopnea detection were found to be 94.84% and 76.82%, respectively, for training set, and 94.72% and 79.77%, respectively, for test set. The Bland-Altman plots showed unbiased estimations with standard deviations of plusmn 2.19, plusmn 2.16, and plusmn 3.64 events/h for AI, HI, and AHI, respectively. Results indicate the possibility of recognizing apnea/Hypopnea events based on shorter segments of ECG signals.
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Lateral decubitus posture during sleep: Sub-groups of obstructive sleep apnea patients — therapeutic value of vertical position in OSA
2009 International Conference on Intelligent Sensors Sensor Networks and Information Processing (ISSNIP), 2009Co-Authors: MAK A. Daulatzai, Neela Khan, Ahsan H. Khandoker, Chandan Karmakar, Marimuthu PalaniswamiAbstract:The incidence of obstructive sleep apnea (OSA) is steadily increasing, but to date there is no efficacious universal treatment. Therefore, measures that ameliorate this condition need special attention. Avoidance of sleep in the supine (horizontal) position has a positive influence on frequency and severity of OSA. OSA patients are ¿Responders¿ when they respond to positional therapeutic measure, while those in whom sleeping vertically does not result in lowering of apnea Hypopnea index (AHI) are ¿Non-Responders¿. The aim of this study was to evaluate the number of apnea and Hypopnea in responders and non-responders. One hundred and seven adult OSA patients with varying AHI, i.e. from 5 to 117, were included. We recorded the number of apnea and Hypopnea in the supine position as well as in the lateral position of sleep. We categorized them in to different groups. We report, for the first time, four different groups of OSA patients in a seemingly so-called homogeneous population. One group of OSA patients, 57.0 %, responded to the lateral position in sleep and showed a decrease in their apnea and Hypopnea; a second group, 23.3 %, exhibited a decrease in their apnea but not in Hypopnea; a third group, 10.3 %, in whom the apnea increased but not Hypopnea, while a fourth group, 9.4%, showed an increase in both apnea and Hypopnea. The great majority of OSA suffers benefit from non-supine sleep position; it is recommended, therefore, that different measures be utilized to enhance sleep in the lateral position in these patients. However, the group of patients (group 4) who increase their apnea and Hypopnea in the non-supine position should be discouraged to adopt this sleeping posture; however, they should be encouraged to use different therapeutic methodology such as a mandibular splint and/or the tongue restraining device.
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Recognizing central and obstructive sleep apnea events from normal breathing events in ECG recordings
2008 Computers in Cardiology, 2008Co-Authors: Ahsan H. Khandoker, Jayavardhana Gubbi, Marimuthu PalaniswamiAbstract:Obstructive sleep apnea (OSA) causes a pause in airflow with continuing breathing effort. In contrast, central sleep apnea (CSA) event is not accompanied with breathing effort. The aim of this study is to differentiate CSA and OSA events from normal breathing events using wavelet based features of ECG signal over 5 second period. Total 164880 epochs(each of 5-second duration) from normal breathing events, 196 epochs from 116 CSA, 5281 epochs from 2173 OSA and 3073 epochs from 1563 Hypopnea events were selected from single lead ECGs (sampling rate=250 Hz). At the first stage of classification, apnea events were classified from normal breathing events and at the second stage, Hypopneas were identified from all apnea events and at final stage, CSA and OSA types were recognized at 98.96% accuracy. Results indicate the possibility of recognizing OSA/CSA events based on shorter segments of ECG signals.
Bijoylaxmi Koley - One of the best experts on this subject based on the ideXlab platform.
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Adaptive classification system for real-time detection of apnea and Hypopnea events
2013 IEEE Point-of-Care Healthcare Technologies (PHT), 2013Co-Authors: Bijoylaxmi KoleyAbstract:This paper presents a real time portable apnea and Hypopnea event detection system from the measurement of oronasal airflow signal only. The system uses a combined classification system for detection of events on the basis of personalized normal breathing pattern. Events are detected first, by identifying some abnormal breathing segments with the help of a binary classifier and then, the identified abnormal segments are further classified into any one of the two classes, i.e., apnea (A) and Hypopnea (H). The second stage classification system is adaptive in nature, implemented to improve separation of apnea from Hypopnea events. The proposed real time system was implemented in personal computer and was clinically validated by offline and online test, the event detection accuracy 93.4% and 91.8% was achieved on 8 different subjects in each case.
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Real-Time Adaptive Apnea and Hypopnea Event Detection Methodology for Portable Sleep Apnea Monitoring Devices
IEEE Transactions on Biomedical Engineering, 2013Co-Authors: Bijoylaxmi KoleyAbstract:This paper presents a novel real-time adaptive sleep apnea monitoring methodology, suitable for portable devices used in home care applications. The proposed method identifies apnea/Hypopnea events with the help of oronasal airflow signal and aimed to meet clinical standards in the assessment mechanism of apnea severity. It uses a strategically combined adaptive two stage classifier model to detect apnea or Hypopnea events on the basis of personalized breathing patterns. For the detection of events, optimum set of time, frequency, and nonlinear measures, extracted from overlapping segments of typical 8 s were fed to support vector machine-based classifiers model to identify the possible origin of the segments, i.e., whether from normal or abnormal (apnea/Hypopnea) episodes, and then the decision of the classifier model on the time sequenced successive segments have been used to detect an event. The performance of the proposed real-time algorithm is validated on clinical tests online. Average accuracies of Hypopnea, apnea, and combined event detection when compared with polysomnography-based respective indices on unseen subjects during online tests were found to be 91.8%, 94.9%, and 96.5%, respectively, which are quite acceptable.
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Automated detection of apnea and Hypopnea events
2012 Third International Conference on Emerging Applications of Information Technology, 2012Co-Authors: Bijoylaxmi KoleyAbstract:This paper presents an automatic method for detection of apnea and Hypopnea events occurred during sleep from the single channel recording of oronasal airflow signal. For the identification of events, three time domain measures were extracted from each of the overlapping short segment windows of respiration signal. The feature set includes area, upper 90th percentile and variance, which were used to characterize changes in the airflow signal during normal and abnormal breathing events (i.e., apnea, Hypopnea). An ensemble of three binary Support Vector Machine (SVM) based classifiers arranged in one-against-all strategy, were used to classify the feature vector among three categories, according to its origin from some breathing events like normal, apnea and Hypopnea. The consecutive decisions of classifier model on time sequenced consecutive overlapped windows were combined by some heuristic rules to identify abnormal breathing events from normal breathings. In this study, 14 polysomnography (PSG) recordings diagnosed as obstructive sleep apnea syndrome were analyzed. Independent test was performed on 6 recordings. The cross-validation and independent test accuracies of apneic event detection were found to be 93.3% and 92.8%, respectively. For Hypopnea event these two accuracies were 90.1% and 89.6%. The proposed system can be used for home based monitoring of suspected apneic subject, and can count total number of apnea and Hypopnea events occurred during sleep.
N J Douglas - One of the best experts on this subject based on the ideXlab platform.
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randomized placebo controlled crossover trial of continuous positive airway pressure for mild sleep apnea Hypopnea syndrome
American Journal of Respiratory and Critical Care Medicine, 1999Co-Authors: Heather M Engleman, Ian J Deary, Ruth Kingshott, P K Wraith, Thomas W Mackay, N J DouglasAbstract:The minimal disease severity at which patients with the sleep apnea/Hypopnea syndrome (SAHS) gain benefit from treatment is not well characterized, although a pilot study of continuous positive airway pressure (CPAP) therapy showed daytime improvements in patients with 5 to 15 apneas + Hypopneas per hour slept (AHI). We have thus performed a second, larger, randomized, placebo- controlled study in a prospective series of 34 patients (13 female) with mild SAHS (AHI 5 to 15) and daytime sleepiness. Patients spent 4 wk on CPAP treatment and 4 wk on an oral placebo, with randomization of treatment order, and daytime assessments on the last day of each treatment. Effective CPAP use averaged 2.8 ± 2.1 h (mean ± SD) per night. Compared with placebo, CPAP improved symptom score (p 0.2) sleepiness, performances on 2 of 7 cognitive tasks (p < 0.02), depression score (p < 0.01), and five subscales of the SF-36 health/func...
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factors impairing daytime performance in patients with sleep apnea Hypopnea syndrome
JAMA Internal Medicine, 1992Co-Authors: Katherine Cheshire, C M Shapiro, Heather M Engleman, Ian J Deary, N J DouglasAbstract:Patients with sleep apnea/Hypopnea syndrome commonly demonstrate impaired daytime performance. In a prospective study, 29 patients with sleep apnea/Hypopnea syndrome were assessed polysomnographically to determine the relationship of cognitive performance and daytime sleepiness with sleep disruption, hypoxemia, and mood. Deterioration of cognitive performance correlated significantly with increasing severity of nocturnal breathing irregularity, magnitude of nocturnal hypoxemia, and extent of sleep disruption. Multiple regression analysis identified frequency of apneas plus Hypopneas and of arousal and the extent of nocturnal hypoxemia as the variables most strongly associated with cognitive deficits. Anxiety and depression also contributed to this impairment. Objective daytime sleepiness was not significantly associated with nocturnal variables. This study showed that the frequency of breathing irregularities and the extent of both sleep disruption and nocturnal hypoxemia are important in determining daytime function in patients with sleep apnea/Hypopnea syndrome. All of these factors should be considered when deciding which patients require treatment. (Arch Intern Med. 1992;152:538-541)
Kwang Suk Park - One of the best experts on this subject based on the ideXlab platform.
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Real-Time Automatic Apneic Event Detection Using Nocturnal Pulse Oximetry
IEEE Transactions on Biomedical Engineering, 2018Co-Authors: Dawoon Jung, Su Hwan Hwang, Byung Hun Choi, Hyun Jae Baek, Do-un Jeong, Kwang Suk ParkAbstract:Objective: Nocturnal pulse oximetry has been proposed as a simpler alternative to polysomnography in diagnosing sleep apnea. However, existing techniques are limited in terms of inability to provide time information on sleep apnea occurrence. This study aimed to propose a new strategy for near real-time automatic detection of apneic events and reliable estimation of apnea-Hypopnea index using nocturnal pulse oximetry. Methods: Among 230 polysomnographic recordings with apnea-Hypopnea index values ranging from 0 to 86.5 events/h, 138 (60%) and the remaining 92 recordings (40%) were categorized as training and test sets, respectively. By extracting the quantitative characteristics caused by the apneic event for the amount and duration of the change in blood oxygen saturation value, we established the criteria to determine the occurrence of apneic event. Regression modeling was used to estimate the apnea-Hypopnea index from the apneic event detection results. Results: The minute-by-minute apneic segment detection exhibited an average accuracy of 91.0% and an average Cohen's kappa coefficient of 0.71. Between the apnea-Hypopnea index estimations and reference values, the mean absolute error was 2.30 events/h. The average accuracy of our diagnosis of sleep apnea was 96.7% for apnea-Hypopnea index cutoff values of ≥5, 10, 15, and 30 events/h. Conclusion: We developed an effective strategy to detect apneic events by using morphometric characteristics in the fluctuation of blood oxygen saturation values. Significance: Our study could be potentially useful in home-based multinight apneic event monitoring for purposes of therapeutic intervention and follow-up study on sleep apnea.
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Apnea-Hypopnea index estimation using quantitative analysis of sleep macrostructure.
Physiological Measurement, 2016Co-Authors: Dawoon Jung, Su Hwan Hwang, Un Jeong, Kwang Suk ParkAbstract:Obstructive sleep apnea, characterized by recurrent cessation or substantial reduction in breathing during sleep, is a prevalent and serious medical condition. Although a significant relationship between obstructive sleep apnea and sleep macrostructure has been revealed in several studies, useful applications of this relationship have been limited. The aim of this study was to suggest a novel approach using quantitative analysis of sleep macrostructure to estimate the apnea–Hypopnea index, which is commonly used to assess obstructive sleep apnea. Without being bound by conventional sleep macrostructure parameters, various new sleep macrostructure parameters were extracted from the polysomnographic recordings of 132 subjects. These recordings were split into training and validation sets, each with 66 recordings including 48 recordings with an apnea–Hypopnea index greater than 5 events h−1. The nonlinear regression analysis, performed using the percentage transition probability from non-rapid eye movement sleep stage 2 to stage 1, was most effective in estimating the apnea–Hypopnea index. Between the apnea–Hypopnea index estimates and the reference values reported from polysomnography, a root mean square error of 7.30 events h−1 was obtained in the validation set. At an apnea–Hypopnea index cut-off of ≥30 events h−1, the obstructive sleep apnea diagnostic performance was provided with a sensitivity of 90.0%, a specificity of 93.5%, and an accuracy of 92.4% by our method. The developed apnea–Hypopnea index estimation model has the potential to be utilized in circumstances in which it is not possible to acquire or analyze respiration signal but it is possible to obtain information on sleep macrostructure.
