Displaying publications 1 - 20 of 28 in total

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  1. Yildirim O, Baloglu UB, Acharya UR
    PMID: 30791379 DOI: 10.3390/ijerph16040599
    Sleep disorder is a symptom of many neurological diseases that may significantly affect the quality of daily life. Traditional methods are time-consuming and involve the manual scoring of polysomnogram (PSG) signals obtained in a laboratory environment. However, the automated monitoring of sleep stages can help detect neurological disorders accurately as well. In this study, a flexible deep learning model is proposed using raw PSG signals. A one-dimensional convolutional neural network (1D-CNN) is developed using electroencephalogram (EEG) and electrooculogram (EOG) signals for the classification of sleep stages. The performance of the system is evaluated using two public databases (sleep-edf and sleep-edfx). The developed model yielded the highest accuracies of 98.06%, 94.64%, 92.36%, 91.22%, and 91.00% for two to six sleep classes, respectively, using the sleep-edf database. Further, the proposed model obtained the highest accuracies of 97.62%, 94.34%, 92.33%, 90.98%, and 89.54%, respectively for the same two to six sleep classes using the sleep-edfx dataset. The developed deep learning model is ready for clinical usage, and can be tested with big PSG data.
    Matched MeSH terms: Sleep Stages*
  2. Awais M, Badruddin N, Drieberg M
    Sensors (Basel), 2017 Aug 31;17(9).
    PMID: 28858220 DOI: 10.3390/s17091991
    Driver drowsiness is a major cause of fatal accidents, injury, and property damage, and has become an area of substantial research attention in recent years. The present study proposes a method to detect drowsiness in drivers which integrates features of electrocardiography (ECG) and electroencephalography (EEG) to improve detection performance. The study measures differences between the alert and drowsy states from physiological data collected from 22 healthy subjects in a driving simulator-based study. A monotonous driving environment is used to induce drowsiness in the participants. Various time and frequency domain feature were extracted from EEG including time domain statistical descriptors, complexity measures and power spectral measures. Features extracted from the ECG signal included heart rate (HR) and heart rate variability (HRV), including low frequency (LF), high frequency (HF) and LF/HF ratio. Furthermore, subjective sleepiness scale is also assessed to study its relationship with drowsiness. We used paired t-tests to select only statistically significant features (p < 0.05), that can differentiate between the alert and drowsy states effectively. Significant features of both modalities (EEG and ECG) are then combined to investigate the improvement in performance using support vector machine (SVM) classifier. The other main contribution of this paper is the study on channel reduction and its impact to the performance of detection. The proposed method demonstrated that combining EEG and ECG has improved the system's performance in discriminating between alert and drowsy states, instead of using them alone. Our channel reduction analysis revealed that an acceptable level of accuracy (80%) could be achieved by combining just two electrodes (one EEG and one ECG), indicating the feasibility of a system with improved wearability compared with existing systems involving many electrodes. Overall, our results demonstrate that the proposed method can be a viable solution for a practical driver drowsiness system that is both accurate and comfortable to wear.
    Matched MeSH terms: Sleep Stages*
  3. Faust O, Razaghi H, Barika R, Ciaccio EJ, Acharya UR
    Comput Methods Programs Biomed, 2019 Jul;176:81-91.
    PMID: 31200914 DOI: 10.1016/j.cmpb.2019.04.032
    BACKGROUND AND OBJECTIVE: Sleep is an important part of our life. That importance is highlighted by the multitude of health problems which result from sleep disorders. Detecting these sleep disorders requires an accurate interpretation of physiological signals. Prerequisite for this interpretation is an understanding of the way in which sleep stage changes manifest themselves in the signal waveform. With that understanding it is possible to build automated sleep stage scoring systems. Apart from their practical relevance for automating sleep disorder diagnosis, these systems provide a good indication of the amount of sleep stage related information communicated by a specific physiological signal.

    METHODS: This article provides a comprehensive review of automated sleep stage scoring systems, which were created since the year 2000. The systems were developed for Electrocardiogram (ECG), Electroencephalogram (EEG), Electrooculogram (EOG), and a combination of signals.

    RESULTS: Our review shows that all of these signals contain information for sleep stage scoring.

    CONCLUSIONS: The result is important, because it allows us to shift our research focus away from information extraction methods to systemic improvements, such as patient comfort, redundancy, safety and cost.

    Matched MeSH terms: Sleep Stages*
  4. Sharma M, Goyal D, Achuth PV, Acharya UR
    Comput Biol Med, 2018 07 01;98:58-75.
    PMID: 29775912 DOI: 10.1016/j.compbiomed.2018.04.025
    Sleep related disorder causes diminished quality of lives in human beings. Sleep scoring or sleep staging is the process of classifying various sleep stages which helps to detect the quality of sleep. The identification of sleep-stages using electroencephalogram (EEG) signals is an arduous task. Just by looking at an EEG signal, one cannot determine the sleep stages precisely. Sleep specialists may make errors in identifying sleep stages by visual inspection. To mitigate the erroneous identification and to reduce the burden on doctors, a computer-aided EEG based system can be deployed in the hospitals, which can help identify the sleep stages, correctly. Several automated systems based on the analysis of polysomnographic (PSG) signals have been proposed. A few sleep stage scoring systems using EEG signals have also been proposed. But, still there is a need for a robust and accurate portable system developed using huge dataset. In this study, we have developed a new single-channel EEG based sleep-stages identification system using a novel set of wavelet-based features extracted from a large EEG dataset. We employed a novel three-band time-frequency localized (TBTFL) wavelet filter bank (FB). The EEG signals are decomposed using three-level wavelet decomposition, yielding seven sub-bands (SBs). This is followed by the computation of discriminating features namely, log-energy (LE), signal-fractal-dimensions (SFD), and signal-sample-entropy (SSE) from all seven SBs. The extracted features are ranked and fed to the support vector machine (SVM) and other supervised learning classifiers. In this study, we have considered five different classification problems (CPs), (two-class (CP-1), three-class (CP-2), four-class (CP-3), five-class (CP-4) and six-class (CP-5)). The proposed system yielded accuracies of 98.3%, 93.9%, 92.1%, 91.7%, and 91.5% for CP-1 to CP-5, respectively, using 10-fold cross validation (CV) technique.
    Matched MeSH terms: Sleep Stages/physiology*
  5. Michielli N, Acharya UR, Molinari F
    Comput Biol Med, 2019 03;106:71-81.
    PMID: 30685634 DOI: 10.1016/j.compbiomed.2019.01.013
    Automated evaluation of a subject's neurocognitive performance (NCP) is a relevant topic in neurological and clinical studies. NCP represents the mental/cognitive human capacity in performing a specific task. It is difficult to develop the study protocols as the subject's NCP changes in a known predictable way. Sleep is time-varying NCP and can be used to develop novel NCP techniques. Accurate analysis and interpretation of human sleep electroencephalographic (EEG) signals is needed for proper NCP assessment. In addition, sleep deprivation may cause prominent cognitive risks in performing many common activities such as driving or controlling a generic device; therefore, sleep scoring is a crucial part of the process. In the sleep cycle, the first stage of non-rapid eye movement (NREM) sleep or stage N1 is the transition between wakefulness and drowsiness and becomes relevant for the study of NCP. In this study, a novel cascaded recurrent neural network (RNN) architecture based on long short-term memory (LSTM) blocks, is proposed for the automated scoring of sleep stages using EEG signals derived from a single-channel. Fifty-five time and frequency-domain features were extracted from the EEG signals and fed to feature reduction algorithms to select the most relevant ones. The selected features constituted as the inputs to the LSTM networks. The cascaded architecture is composed of two LSTM RNNs: the first network performed 4-class classification (i.e. the five sleep stages with the merging of stages N1 and REM into a single stage) with a classification rate of 90.8%, and the second one obtained a recognition performance of 83.6% for 2-class classification (i.e. N1 vs REM). The overall percentage of correct classification for five sleep stages is found to be 86.7%. The objective of this work is to improve classification performance in sleep stage N1, as a first step of NCP assessment, and at the same time obtain satisfactory classification results in the other sleep stages.
    Matched MeSH terms: Sleep Stages/physiology*
  6. Yasmin Othman Mydin, Norzarina Mohd Zaharim, Syed Hassan Ahmad Almashor
    MyJurnal
    Objective: The objective of this study is to identify the correlation between psychological factors and insomnia and the impact of insomnia on daytime sleepiness. Methods and Results: The participants were recruited through convenient sampling and consist of 173 working adults in Georgetown, Penang, aged 20 to 60 years. Participants completed the General Health Questionnaire (GHQ), Athens Insomnia Scale (AIS) and Epworth Sleepiness Scale (ESS). The results revealed that the prevalent of insomnia was 34.7%. There was a positive correlation between psychological distress and insomnia r = .481, p < .001 and also a positive correlation between insomnia and daytime sleepiness r = .334, p < .001. Conclusion: It is concluded that psychological distress typically causes sleep difficulties, and sleep deprivation leads to daytime sleepiness.
    Matched MeSH terms: Sleep Stages
  7. Lee WS, Kaur P, Boey CC, Chan KC
    J Paediatr Child Health, 1998 Dec;34(6):568-70.
    PMID: 9928652
    OBJECTIVE: To describe the clinical features, management and outcome of children with cyclic vomiting syndrome (CVS) from South-East Asia.

    METHODOLOGY: Retrospective review of all children who fulfilled the diagnostic criteria of CVS and who were seen at Department of Paediatrics, University of Malaya Medical Centre, Kuala Lumpur and Paediatric Unit, Penang Hospital, Penang, Malaysia from 1987 to 1997.

    RESULTS: Eight children with CVS were seen at the two units during the study period, five girls and three boys. All had cyclical, self-limited episodes of nausea and vomiting with symptom-free intervals. The mean age of onset was 5.9 years. The clinical features were similar to other series described in the literature. Only two of the eight children were described as 'perfectionist'. Two children identified stress as precipitating factors. Therapy to reduce the number of emeses during acute attacks and to prevent subsequent attacks had been ineffective.

    CONCLUSION: There are similarities and differences in CVS from South-East Asian children as compared to those described in the literature.

    Matched MeSH terms: Sleep Stages
  8. Mousavi Z, Yousefi Rezaii T, Sheykhivand S, Farzamnia A, Razavi SN
    J Neurosci Methods, 2019 08 01;324:108312.
    PMID: 31201824 DOI: 10.1016/j.jneumeth.2019.108312
    Using a smart method for automatic diagnosis in medical applications, such as sleep stage classification is considered as one of the important challenges of the last few years which can replace the time-consuming process of visual inspection done by specialists. One of the problems regarding the automatic diagnosis of sleep patterns is extraction and selection of discriminative features generally demanding high computational burden. This paper provides a new single-channel approach to automatic classification of sleep stages from EEG signal. The main idea is to directly apply the raw EEG signal to deep convolutional neural network, without involving feature extraction/selection, which is a challenging process in the previous literature. The proposed network architecture includes 9 convolutional layers followed by 2 fully connected layers. In order to make the samples of different classes balanced, we used a preprocessing method called data augmentation. The simulation results of the proposed method for classification of 2 to 6 classes of sleep stages show the accuracy of 98.10%, 96.86%, 93.11%, 92.95%, 93.55% and Cohen's Kappa coefficient of 0.98%, 0.94%, 0.90%, 0.86% and 0.89%, respectively. Furthermore, comparing the obtained results with the state-of-the-art methods reveals the performance improvement of the proposed sleep stage classification in terms of accuracy and Cohen's Kappa coefficient.
    Matched MeSH terms: Sleep Stages/physiology*
  9. Sahayadhas A, Sundaraj K, Murugappan M
    Sensors (Basel), 2012 Dec 07;12(12):16937-53.
    PMID: 23223151 DOI: 10.3390/s121216937
    In recent years, driver drowsiness has been one of the major causes of road accidents and can lead to severe physical injuries, deaths and significant economic losses. Statistics indicate the need of a reliable driver drowsiness detection system which could alert the driver before a mishap happens. Researchers have attempted to determine driver drowsiness using the following measures: (1) vehicle-based measures; (2) behavioral measures and (3) physiological measures. A detailed review on these measures will provide insight on the present systems, issues associated with them and the enhancements that need to be done to make a robust system. In this paper, we review these three measures as to the sensors used and discuss the advantages and limitations of each. The various ways through which drowsiness has been experimentally manipulated is also discussed. We conclude that by designing a hybrid drowsiness detection system that combines non-intrusive physiological measures with other measures one would accurately determine the drowsiness level of a driver. A number of road accidents might then be avoided if an alert is sent to a driver that is deemed drowsy.
    Matched MeSH terms: Sleep Stages/physiology*
  10. Sahayadhas A, Sundaraj K, Murugappan M
    Australas Phys Eng Sci Med, 2013 Jun;36(2):243-50.
    PMID: 23719977 DOI: 10.1007/s13246-013-0200-6
    Driver drowsiness has been one of the major causes of road accidents that lead to severe trauma, such as physical injury, death, and economic loss, which highlights the need to develop a system that can alert drivers of their drowsy state prior to accidents. Researchers have therefore attempted to develop systems that can determine driver drowsiness using the following four measures: (1) subjective ratings from drivers, (2) vehicle-based measures, (3) behavioral measures and (4) physiological measures. In this study, we analyzed the various factors that contribute towards drowsiness. A total of 15 male subjects were asked to drive for 2 h at three different times of the day (00:00-02:00, 03:00-05:00 and 15:00-17:00 h) when the circadian rhythm is low. The less intrusive physiological signal measurements, ECG and EMG, are analyzed during this driving task. Statistically significant differences in the features of ECG and sEMG signals were observed between the alert and drowsy states of the drivers during different times of day. In the future, these physiological measures can be fused with vision-based measures for the development of an efficient drowsiness detection system.
    Matched MeSH terms: Sleep Stages/physiology*
  11. Deros, B.M., Khamis, N.K., Ismail, A.R., Ludin, A.
    MyJurnal
    Introduction : Shift work is practised in manufacturing industry to increase production capacity up to three times compared to the normal daily eight hours working system and able to optimize the utilization of machine and equipment. However, shift work has negatif effects on human social interaction, health and safety.
    Methodology : The study was conducted to evaluate production workers’ perception on the effects of working at night shift. The respondents of the study were production workers in Company X in Kuala Lumpur. The Data was collected using self administered questionnaires. The study objectives was to study the work schedule design, to find out their perceptions on the effects of night shift and to study on personal factors, employees’ level of acceptance on the work schedule design and personal factors that cause safety and health disruption.
    Result : A total of 200 production workers participated in the study. The result of the study shows 61% of production workers took sick leave and 43.5% were absent between 1 and 2 times a month. In terms of health and safety disruption, 77% of respondents agreed that they faced lack of focus with family and friends, 75.5% of them did not have enough sleep and 76.5% felt sleepy during working time. Regarding the work schedule and workstation design, 81.5% said they felt uncomfortable because they were required to stand during working and 77% felt that the resting period provided by the factory was too short and inadequate. More than 70% of the respondents proposed the rest period should be extended from the current 40 minutes to one hour. More than 80% of respondents agreed they would feel comfortable if standing at work is changed to sitting work system.
    Conclusion : To minimize the unwanted effect of night shift among the production.
    Matched MeSH terms: Sleep Stages
  12. Azizan A, Fard M, Azari MF, Jazar R
    Appl Ergon, 2017 Apr;60:348-355.
    PMID: 28166895 DOI: 10.1016/j.apergo.2016.12.020
    Although much research has been devoted to the characterization of the effects of whole-body vibration on seated occupants' comfort, drowsiness induced by vibration has received less attention to date. There are also little validated measurement methods available to quantify whole body vibration-induced drowsiness. Here, the effects of vibration on drowsiness were investigated. Twenty male volunteers were recruited for this experiment. Drowsiness was measured in a driving simulator, before and after 30-min exposure to vibration. Gaussian random vibration, with 1-15 Hz frequency bandwidth was used for excitation. During the driving session, volunteers were required to obey the speed limit of 100 kph and maintain a steady position on the left-hand lane. A deviation in lane position, steering angle variability, and speed deviation were recorded and analysed. Alternatively, volunteers rated their subjective drowsiness by Karolinska Sleepiness Scale (KSS) scores every 5-min. Following 30-min of exposure to vibration, a significant increase of lane deviation, steering angle variability, and KSS scores were observed in all volunteers suggesting the adverse effects of vibration on human alertness level.
    Matched MeSH terms: Sleep Stages*
  13. Tan YK, Khoo KL, Low JA, Wong ZW, Theng CT, Ong TH, et al.
    Ann Acad Med Singap, 1999 Mar;28(2):214-6.
    PMID: 10497669
    We studied the relationship between different ethnic groups, obstructive sleep apnoea (OSA) and ischaemic heart disease. Four hundred and thirty-two inpatients from the medical wards were interviewed. Limited overnight sleep studies were done in 129 of those who had habitual snoring, daytime sleepiness based on an Epworth sleepiness scale of 8 or more, or a large neck size of 40 cm or more. There were 315 Chinese (72.9%), 67 Malays (15.5%), 43 Indians (10%) and 3 from other races (1.4%). The prevalence of OSA was 19.7%, 30% and 12% among the Chinese, Malays and Indians, respectively. The prevalence ratio for OSA was 1.52 in Malays using Chinese patients as the baseline (P = 0.07). The median neck circumference was 37 cm in both racial groups. The median body mass index was 22.7 kg/m2 in Chinese compared to 23.6 kg/m2 in Malays. The median apnoea-hypopnoea index was 22.7, 19.0 and 26.9 events/hour among the Chinese, Malays and Indians, respectively. OSA was independently associated with the prevalence of IHD (adjusted prevalence ratio 1.68; 95% CI: 1.15, 2.46; P = 0.009). The prevalence of ischaemic heart disease (IHD) was 31%, 24% and 28% in Chinese, Malays and Indians, respectively. The prevalence ratio for IHD in Malays compared to Chinese was 0.77. After adjusting for OSA, there was an even greater reduction in the risk of IHD (adjusted prevalence ratio 0.70). This suggests that OSA is a confounder in the relationship between race and ischaemic heart disease.
    Matched MeSH terms: Sleep Stages
  14. Chaw S, Lo Y, Shariffuddin II, Wong J, Lee J, Leong DW, et al.
    Paediatr Anaesth, 2019 01;29(1):68-76.
    PMID: 30381868 DOI: 10.1111/pan.13528
    BACKGROUND: Effective pain management involves a cycle of continual pain assessment, good pain control strategies, and assessment of a standard quality improvement measures. A validated questionnaire that focuses on the quality of postoperative pain management in pediatric surgical patients and parental satisfaction on pain treatment is lacking. We, therefore, modified the revised American Pain Society Patient Outcome Questionnaire to evaluate the quality of postoperative pain management in a pediatric surgical setting. The primary aim of this study was to validate the modified version of revised American Pain Society Patient Outcome Questionnaire.

    METHODS: Parents whose children aged below 12 years and were scheduled for elective surgery in a teaching hospital, were approached to participate in this survey. The reliability of the modified version of revised American Pain Society Patient Outcome Questionnaire was evaluated using Cronbach's alpha test, while the construct validity was assessed with a principal component analysis using a varimax rotation. The parental satisfaction with pain treatment received was measured.

    RESULTS: A total of 108 parents completed the questionnaire. The internal consistency of the questionnaire shows a Cronbach's alpha of 0.798. Principal component analysis revealed a four-factor structure of the 12 items which explained 69.7% of the total variance. The factors are "Interference of sleep and activity," "Pain severity and drowsiness," "Perception of care," and "Adverse effects," respectively. Our study showed that this questionnaire is a valid and reliable measure for "Interference of sleep and activity" and "Pain severity and drowsiness" factors, but not for "Perception of care" and "Adverse effects." The results for "Perception of care" and "Adverse effects," therefore, should be reported as individual items instead of total score. The parental satisfaction with pain treatment given was good (median 8.0; IQR 3.0).

    CONCLUSION: The modified version of revised American Pain Society Patient Outcome Questionnaire is a feasible and easy instrument to administer. The questionnaire can be used to obtain feedback from parents about the outcomes and experiences of pain management and is helpful in continuous quality evaluation and improvement in the postoperative care in a pediatric setting.

    Matched MeSH terms: Sleep Stages
  15. Willoughby AR, de Zambotti M, Baker FC, Colrain IM
    Alcohol, 2020 May;84:1-7.
    PMID: 31539623 DOI: 10.1016/j.alcohol.2019.09.005
    There is evidence for impairment in both central nervous system (CNS) and autonomic nervous system (ANS) function with prolonged alcohol use. While these impairments persist into abstinence, partial recovery of function has been demonstrated in both systems during sleep. To investigate potential ANS dysfunction associated with cortical CNS responses (impairment in CNS-ANS coupling), we assessed phasic heart rate (HR) fluctuation associated with tones that did and those that did not elicit a K-complex (KC) during stable N2 non-rapid eye movement (NREM) sleep in a group of 16 recently abstinent alcohol use disorder (AUD) patients (41.6 ± 8.5 years) and a group of 13 sex- and age-matched control participants (46.6 ± 9.3 years). Electroencephalogram (EEG) and electrocardiogram (ECG) data were recorded throughout the night. Alcohol consumption questionnaires were also administered to the AUD patients. AUD patients had elevated HR compared to controls at baseline prior to tone presentation. The HR fluctuation associated with KCs elicited by tone presentation was significantly smaller in amplitude, and tended to be delayed in time, in the AUD group compared with the control group, and the subsequent deceleration was also smaller in AUD patients. In both groups, the increase in HR was larger and occurred earlier when KCs were produced than when they were not, and there was no difference in the magnitude of the KC effect between groups. Phasic HR changes associated with KCs elicited by tones are impaired in AUD participants, reflecting ANS dysfunction possibly caused by an alteration of cardiac vagal trafficking. However, only the timing of the HR response was found to relate to estimated lifetime alcohol consumption in AUD. The clinical meaning and implications of these novel findings need to be determined.
    Matched MeSH terms: Sleep Stages/physiology*
  16. Mohd Nor NS, Fong CY, Rahmat K, Vanessa Lee WM, Zaini AA, Jalaludin MY
    Eur Endocrinol, 2018 Apr;14(1):59-61.
    PMID: 29922355 DOI: 10.17925/EE.2018.14.1.59
    Cerebral oedema is the most common neurological complication of diabetic ketoacidosis (DKA). However, ischaemic and haemorrhagic brain injury has been reported infrequently. A 10-year old girl who was previously well presented with severe DKA. She was tachycardic with poor peripheral perfusion but normotensive. However, two fast boluses totalling 40 ml/kg normal saline were given. She was transferred to another hospital where she was intubated due to drowsiness. Rehydration fluid (maintenance and 48-hour correction for 7.5% dehydration) was started followed by insulin infusion. She was extubated within 24 hours of admission. Her ketosis resolved soon after and subcutaneous insulin was started. However, about 48 hours after admission, her Glasgow Coma Scale score dropped to 11/15 (E4M5V2) with expressive aphasia and upper motor neuron signs. One dose of mannitol was given. Her symptoms improved gradually and at 26-month follow-up she had a near-complete recovery with only minimal left lower limb weakness. Serial magnetic resonance imaging brain scans showed vascular ischaemic injury at the frontal-parietal watershed regions with haemorrhagic transformation. This case reiterates the importance of monitoring the neurological status of patient's with DKA closely for possible neurological complications including an ischaemic and haemorrhagic stroke.
    Matched MeSH terms: Sleep Stages
  17. Chuah SY, Wong NW, Goh KL
    Postgrad Med J, 1997 Mar;73(857):177-9.
    PMID: 9135840 DOI: 10.1136/pgmj.73.857.177
    Matched MeSH terms: Sleep Stages*
  18. Wan Haniza WHW, Tengku Saifudin TI
    Malays Fam Physician, 2011;6(1):2-6.
    PMID: 25606213 MyJurnal
    Obstructive sleep apnoea hypopnoea syndrome (OSAHS) is a common cause of breathing-related sleep disorder, causing excessive daytime sleepiness. Common clinical features of OSAHS include snoring, fragmented sleep, daytime somnolence and fatigue. This article aims to provide a comprehensive review of the condition, including its management.
    Matched MeSH terms: Sleep Stages
  19. Abdullah B, Othman NA, Daud MK
    Malays J Med Sci, 2008 Apr;15(2):29-32.
    PMID: 22589622
    To determine the outcome of laser-assisted uvulopalatoplasty for the management of patients with snoring in Universiti Sains Malaysia Hospital (HUSM). A retrospective review of patients who underwent LAUP with or without tonsillectomy or adenoidectomy under general anaesthesia between December 2003 to December 2006. Data was obtained from admission and follow-up records in the otorhinolaryngology clinic of USM Hospital (HUSM). A total of nineteen patients underwent procedure for the treatment of snoring. Majority of these patients presented with symptoms of loud snoring and daytime somnolence. The main operations performed were LAUP with or without tonsillectomy or adenoidectomy. The justification for LAUP were overhanging and excessive uvula or soft palate, whereas for adenotonsillectomy were the hypertrophied adenoids and tonsils. On follow-up, most of the patients claimed improvement of snoring within the first 2 months post-operation. However, majority of them defaulted follow-up after that. One patient (AHI preoperatively was mild) was reviewed up to 8 months with no snoring. One patient (AHI was severe preoperatively and normal post operatively) remained in our follow-up was satisfied with the operation. Two patients continued to experience snoring post LAUP despite trial of non-surgical methods. The outcome of LAUP in our patients showed variable results. This showed that patient selection is very important to achieve good result in LAUP. Long-term follow-up is also essential to document the success for LAUP.

    Study site: otorhinolaryngology clinic of USM Hospital (HUSM)
    Matched MeSH terms: Sleep Stages
  20. Petre-Quadens O, Hussain H, Balaratnan C
    Acta Neurol Belg, 1975 Mar;75(2):85-92.
    PMID: 168717
    The Temiars are a tribe of negroid pygmies of basically Proto-Malaysian affinities. Field-work in the Malaysian jungle provided some observations on the sleep-wakefulness cycle of two young Temiar adults. This cycle was monophasic circumstances permitting. Their rest-activity cycle at night was similar in the jungle and in the laboratory. Polygraphic total night-sleep recordings were made with both of them in the EEG laboratory in the Hospital Besar in Kuala Lumpur. The eye-movement frequencies of PS were compared with those from young adults of the West. Although the differences were not statistically significant, the Rem-densities of the Temiars were constantly at the low side. The significance of the results are being discussed.
    Matched MeSH terms: Sleep Stages
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