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Fulltext Detecting driver drowsiness based on sensors: a review

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*
Fulltext A Hybrid Approach to Detect Driver Drowsiness Utilizing Physiological Signals to Improve System Performance and Wearability

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*
Deep convolutional neural network for classification of sleep stages from single-channel EEG signals

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*
Fulltext Obstructive sleep apnoea hypopnoea syndrome – an overview

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
Fulltext The disappearance of sleep-related changes of serum prolactin in pregnant women

Rajikin MH, Abdullah R, Hamid Arshat

Med J Malaysia, 1983 Dec;38(4):311-4.
PMID: 6599989

Serum prolactin (hPRL) levels in nonpregnant, pregnant and postpartum women during sleep were investigated. The study showed that in non-pregnant women, there is an immediate shift of hPRL release with reversal of sleeping period. Thus, the nocturnal surge for prolactin is sleep related. In pregnant women, however, while there is an increase in hPRL level during pregnancy, the nocturnal rise of this hormone is not detected and this is observed as early as the first trimester of pregnancy.

Matched MeSH terms: Sleep Stages*
Cascaded LSTM recurrent neural network for automated sleep stage classification using single-channel EEG signals

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*
An accurate sleep stages classification system using a new class of optimally time-frequency localized three-band wavelet filter bank

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*
Fulltext A Deep Learning Model for Automated Sleep Stages Classification Using PSG Signals

Yildirim O, Baloglu UB, Acharya UR

Int J Environ Res Public Health, 2019 02 19;16(4).
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*
A review of automated sleep stage scoring based on physiological signals for the new millennia

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*
Fulltext SleepEEGNet: Automated sleep stage scoring with sequence to sequence deep learning approach

Mousavi S, Afghah F, Acharya UR

PLoS One, 2019;14(5):e0216456.
PMID: 31063501 DOI: 10.1371/journal.pone.0216456

Electroencephalogram (EEG) is a common base signal used to monitor brain activities and diagnose sleep disorders. Manual sleep stage scoring is a time-consuming task for sleep experts and is limited by inter-rater reliability. In this paper, we propose an automatic sleep stage annotation method called SleepEEGNet using a single-channel EEG signal. The SleepEEGNet is composed of deep convolutional neural networks (CNNs) to extract time-invariant features, frequency information, and a sequence to sequence model to capture the complex and long short-term context dependencies between sleep epochs and scores. In addition, to reduce the effect of the class imbalance problem presented in the available sleep datasets, we applied novel loss functions to have an equal misclassified error for each sleep stage while training the network. We evaluated the performance of the proposed method on different single-EEG channels (i.e., Fpz-Cz and Pz-Oz EEG channels) from the Physionet Sleep-EDF datasets published in 2013 and 2018. The evaluation results demonstrate that the proposed method achieved the best annotation performance compared to current literature, with an overall accuracy of 84.26%, a macro F1-score of 79.66% and κ = 0.79. Our developed model can be applied to other sleep EEG signals and aid the sleep specialists to arrive at an accurate diagnosis. The source code is available at https://github.com/SajadMo/SleepEEGNet.

Matched MeSH terms: Sleep Stages*
Fulltext Effects of night shift work towards health and safety of production workers. [Kesan bekerja shif malam terhadap kesihatan dan keselamatan di kalangan pekerja pengeluaran]

Deros, B.M., Khamis, N.K., Ismail, A.R., Ludin, A.

Malaysian Journal of Community Health, 2009;15(1):64-75.
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
Fulltext Severe acute poisoning by intentional self-harm with cypermethrin

Kamarul Aryffin Baharuddin, Mohd Hashairi Fauzi, Mohd Boniami Yazid, Mohammad Zikri Ahmad, Wan Hazuraini Wan Zain

Malaysian Journal of Emergency Medicine, 2016;1(1):24-39.
MyJurnal

Severe acutepoisoning of cypermethrin is rare. We reportedthiscase about a47-year old man who was brought to the Emergency Departmentwith drowsiness and drooling of saliva after intentional self-harm with 2.25gram of cypermethrin.His initial condition was stable. However, nine hours after admission, he developed seizures and reduced conscious level. He was ventilated overnight for airway protection. Management of acute severe poisoning is discussed in this case report.

Matched MeSH terms: Sleep Stages
Fulltext Lethargy in a patient with cirrhosis

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*
Effects of vibration on occupant driving performance under simulated driving conditions

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*
Cyclic vomiting syndrome in South-East Asian children

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
Fulltext Correlates between insomnia, psychological distress and daytime sleepiness of malaysian adults with symptoms of insomnia

Yasmin Othman Mydin, Norzarina Mohd Zaharim, Syed Hassan Ahmad Almashor

ASEAN Journal of Psychiatry, 2012;13(2):0-0.
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
Sleep problems among children with neurological disorders

Khoo, T.B., Muhammad Ismail, H.I., Abdul Manaf, A.M.

Malaysian Journal of Paediatrics and Child Health, 2000;12(1):22-28.
MyJurnal

A study was conducted to evaluate the extent of sleep problems among children aged between 6 to 15 years old who were followed up at Penang Hospital Paediatric Clinic for various neurological disorders and compared to those with other paediatric illnesses and their healthy siblings. A parental questionnaire was used to assess sleep problems in 48 children with neurological disorders and compared to 46 of their healthy siblings, 59 children with non-neurological paediatric illnesses and 67 of their healthy siblings. Sleep problems were clustered into five subscales: bedtime difficulties, parental involvement at time of sleep, sleep fragmentation, parasomnias and daytime drowsiness. Children with neurological disorders had significantly more sleep problems than did their siblings, those with non-neurological paediatric illnesses and their healthy siblings (p < 0.001). This was particularly so in areas of bedtime difficulties (p>0.001), the amount of parental involvement (p

Study site: Penang Hospital Paediatric Clinic

Matched MeSH terms: Sleep Stages
Fulltext Sick building syndrome and mental health among university laboratory staffs

Zuliza M, Irniza R, Emilia Z

Malaysian Journal of Public Health Medicine, 2017;17 Special(1):133-139.

The aim of this study was to determine the prevalence of sick building syndrome (SBS) and other factors contributing to probable mental health problems among university laboratory staffs. A cross-sectional study was conducted among 264 laboratory staffs in UPM. Data was collected using validated self-administrated questionnaires consists of Job Content Questionnaire (JCQ), General Health Questionnaire (GHQ) and SBS. Data was analyzed using SPSS version 22.0. In total, about 28% of the participants reported having probable mental health problems. The prevalence of SBS was 31.4%. After controlling for confounders, the significant factors for probable mental health problems were job insecurity (AOR 2.33, 95% CI 0.212- 0.867), job demand (AOR 1.12, 95% CI 0.445-0.921), fatigue (AOR 0.94, 95% CI 0.162-1.425), drowsiness (AOR 0.75, 95% CI 1.023-4.647) and household income (AOR 0.339, 95% CI0.166-0.995).Results visibly showed that psychosocial factors and symptoms of SBS at their working environment contribute to probable mental health problems among laboratory staffs. The strongest predictors in this study were job insecurity. Hence, further assessment and preventive measures should be carried out to reduce the risk factors of probable mental health problems and to improve working environment among university laboratory staffs.

Matched MeSH terms: Sleep Stages
Drowsiness detection during different times of day using multiple features

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*
Fulltext Evoked K-complexes and altered interaction between the central and autonomic nervous systems during sleep in alcohol use disorder

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*

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