A novel TASK channel antagonist nasal spray reduces sleep apnea severity in physiological responders: a randomized, blinded, trial.

Preclinical and human physiological studies indicate that topical, selective TASK 1/3 K+ channel antagonism increases upper airway dilator muscle activity and reduces pharyngeal collapsibility during anesthesia and nasal breathing during sleep. The primary aim of this study was to determine the effects of BAY2586116 nasal spray on obstructive sleep apnea (OSA) severity and whether individual responses vary according to differences in physiological responses and route of breathing. Ten people (5 females) with OSA [apnea-hypopnea index (AHI) = 47 ± 26 events/h (means ± SD)] who completed previous sleep physiology studies with BAY2586116 were invited to return for three polysomnography studies to quantify OSA severity. In random order, participants received either placebo nasal spray (saline), BAY2586116 nasal spray (160 µg), or BAY2586116 nasal spray (160 µg) restricted to nasal breathing (chinstrap or mouth tape). Physiological responders were defined a priori as those who had improved upper airway collapsibility (critical closing pressure ≥2 cmH2O) with BAY2586116 nasal spray (NCT04236440). There was no systematic change in apnea-hypopnea index (AHI3) from placebo versus BAY2586116 with either unrestricted or nasal-only breathing versus placebo (47 ± 26 vs. 43 ± 27 vs. 53 ± 33 events/h, P = 0.15). However, BAY2586116 (unrestricted breathing) reduced OSA severity in physiological responders compared with placebo (e.g., AHI3 = 28 ± 11 vs. 36 ± 12 events/h, P = 0.03 and ODI3 = 18 ± 10 vs. 28 ± 12 events/h, P = 0.02). Morning blood pressure was also lower in physiological responders after BAY2586116 versus placebo (e.g., systolic blood pressure = 137 ± 24 vs. 147 ± 21 mmHg, P < 0.01). In conclusion, BAY2586116 reduces OSA severity during sleep in people who demonstrate physiological improvement in upper airway collapsibility. These findings highlight the therapeutic potential of this novel pharmacotherapy target in selected individuals.NEW & NOTEWORTHY Preclinical findings in pigs and humans indicate that blocking potassium channels in the upper airway with topical nasal application increases pharyngeal dilator muscle activity and reduces upper airway collapsibility. In this study, BAY2586116 nasal spray (potassium channel blocker) reduced sleep apnea severity in those who had physiological improvement in upper airway collapsibility. BAY2586116 lowered the next morning's blood pressure. These findings highlight the potential for this novel therapeutic approach to improve sleep apnea in certain people.

Associations of baseline obstructive sleep apnea and sleep macroarchitecture with cognitive function after 8 years in middle-aged and older men from a community-based cohort study.

Previous prospective studies examining associations of obstructive sleep apnea and sleep macroarchitecture with future cognitive function recruited older participants, many demonstrating baseline cognitive impairment. This study examined obstructive sleep apnea and sleep macroarchitecture predictors of visual attention, processing speed, and executive function after 8 years among younger community-dwelling men. Florey Adelaide Male Ageing Study participants (n = 477) underwent home-based polysomnography, with 157 completing Trail-Making Tests A and B and the Mini-Mental State Examination. Associations of obstructive sleep apnea (apnea-hypopnea index, oxygen desaturation index, and hypoxic burden index) and sleep macroarchitecture (sleep stage percentages and total sleep time) parameters with future cognitive function were examined using regression models adjusted for baseline demographic, biomedical, and behavioural factors, and cognitive task performance. The mean (standard deviation) age of the men at baseline was 58.9 (8.9) years, with severe obstructive sleep apnea (apnea-hypopnea index ≥30 events/h) in 9.6%. The median (interquartile range) follow-up was 8.3 (7.9-8.6) years. A minority of men (14.6%) were cognitively impaired at baseline (Mini-Mental State Examination score <28/30). A higher percentage of light sleep was associated with better Trail-Making Test A performance (B = -0.04, 95% confidence interval [CI] -0.06, -0.01; p = 0.003), whereas higher mean oxygen saturation was associated with worse performance (B = 0.11, 95% CI 0.02, 0.19; p = 0.012). While obstructive sleep apnea and sleep macroarchitecture might predict cognitive decline, future studies should consider arousal events and non-routine hypoxaemia measures, which may show associations with cognitive decline.

Methanol as a Potential Hydrogen Source for Reduction Reactions Enabled by a Commercial Pt/C Catalyst.

Catalytic reduction reactions using methanol as a transfer hydrogenating agent is gaining significant attention because this simple alcohol is inexpensive and produced on a bulk scale. Herein, we report the catalytic utilization of methanol as a hydrogen source for the reduction of different functional organic compounds such as nitroarenes, olefins, and carbonyl compounds. The key to the success of this transformation is the use of a commercially available Pt/C catalyst, which enabled the transfer hydrogenation of a series of simple and functionalized nitroarenes-to-anilines, alkenes-to-alkanes, and aldehydes-to-alcohols using methanol as both the solvent and hydrogen donor. The practicability of this Pt-based protocol is showcased by demonstrating catalyst recycling and reusability as well as reaction upscaling. In addition, the Pt/C catalytic system was also adaptable for the N-methylation and N-alkylation of anilines via the borrowing hydrogen process. This work provides a simple and flexible approach to prepare a variety of value-added products from readily available methanol, Pt/C, and other starting materials.

Multinight Prevalence, Variability, and Diagnostic Misclassification of Obstructive Sleep Apnea.

Rationale: Recent studies suggest that obstructive sleep apnea (OSA) severity can vary markedly from night to night, which may have important implications for diagnosis and management. Objectives: This study aimed to assess OSA prevalence from multinight in-home recordings and the impact of night-to-night variability in OSA severity on diagnostic classification in a large, global, nonrandomly selected community sample from a consumer database of people that purchased a novel, validated, under-mattress sleep analyzer. Methods: A total of 67,278 individuals aged between 18 and 90 years underwent in-home nightly monitoring over an average of approximately 170 nights per participant between July 2020 and March 2021. OSA was defined as a nightly mean apnea-hypopnea index (AHI) of more than 15 events/h. Outcomes were multinight global prevalence and likelihood of OSA misclassification from a single night's AHI value. Measurements and Main Results: More than 11.6 million nights of data were collected and analyzed. OSA global prevalence was 22.6% (95% confidence interval, 20.9-24.3%). The likelihood of misdiagnosis in people with OSA based on a single night ranged between approximately 20% and 50%. Misdiagnosis error rates decreased with increased monitoring nights (e.g., 1-night F1-score = 0.77 vs. 0.94 for 14 nights) and remained stable after 14 nights of monitoring. Conclusions: Multinight in-home monitoring using novel, noninvasive under-mattress sensor technology indicates a global prevalence of moderate to severe OSA of approximately 20%, and that approximately 20% of people diagnosed with a single-night study may be misclassified. These findings highlight the need to consider night-to-night variation in OSA diagnosis and management.

Combined Cardiac and Respiratory Monitoring from a Single Signal: A Case Study Employing the Fantasia Database.

This study proposes a novel method for obtaining the electrocardiogram (ECG) derived respiration (EDR) from a single lead ECG and respiration-derived cardiogram (RDC) from a respiratory stretch sensor. The research aims to reconstruct the respiration waveform, determine the respiration rate from ECG QRS heartbeat complexes data, locate heartbeats, and calculate a heart rate (HR) using the respiration signal. The accuracy of both methods will be evaluated by comparing located QRS complexes and inspiration maxima to reference positions. The findings of this study will ultimately contribute to the development of new, more accurate, and efficient methods for identifying heartbeats in respiratory signals, leading to better diagnosis and management of cardiovascular diseases, particularly during sleep where respiration monitoring is paramount to detect apnoea and other respiratory dysfunctions linked to a decreased life quality and known cause of cardiovascular diseases. Additionally, this work could potentially assist in determining the feasibility of using simple, no-contact wearable devices for obtaining simultaneous cardiology and respiratory data from a single device.

Morpho-molecular diversity and avirulence genes distribution among the diverse isolates of Magnaporthe oryzae from Southern India.

AIMS: To investigate the diversity of eco-distinct isolates of Magnaporthe oryzae for their morphological, virulence and molecular diversity and relative distribution of five Avr genes. METHODS AND RESULTS: Fifty-two M. oryzae isolates were collected from different rice ecosystems of southern India. A majority of them (n = 28) formed a circular colony on culture media. Based on the disease reaction on susceptible cultivar (cv. HR-12), all 52 isolates were classified in to highly virulent (n = 28), moderately virulent (n = 11) and less-virulent (13) types. Among the 52 isolates, 38 were selected for deducing internal transcribed spacer (ITS) sequence diversity. For deducing phylogeny, another set of 36 isolates from other parts of the world was included, which yielded two distinct phylogenetic clusters. We identified eight haplotype groups and 91 variable sites within the ITS sequences, and haplotype-group-2 (Hap_2) was predominant (n = 24). The Tajima's and Fu's Fs neutrality tests exhibited many rare alleles. Furthermore, PCR analysis for detecting the presence of five Avr genes in the different M. oryzae isolates using Avr gene-specific primers in PCR revealed that Avr-Piz-t, Avr-Pik, Avr-Pia and Avr-Pita were present in 73.68%, 73.68%, 63.16% and 47.37% of the isolates studied, respectively; whereas, Avr-Pii was identified only in 13.16% of the isolates. CONCLUSIONS: Morpho-molecular and virulence studies revealed the significant diversity among eco-distinct isolates. PCR detection of Avr genes among the M. oryzae population revealed the presence of five Avr genes. Among them, Avr-Piz-t, Avr-Pik and Avr-Pia were more predominant. SIGNIFICANCE AND IMPACT OF THE STUDY: The study documented the morphological and genetic variability of eco-distinct M. oryzae isolates. This is the first study demonstrating the distribution of the Avr genes among the eco-distinct population of M. oryzae from southern India. The information generated will help plant breeders to select appropriate resistant gene/s combinations to develop blast disease-resistant rice cultivars.

Seizure Detection: A Low Computational Effective Approach without Classification Methods.

Epilepsy is a severe neurological disorder that is usually diagnosed by using an electroencephalogram (EEG). However, EEG signals are complex, nonlinear, and dynamic, thus generating large amounts of data polluted by many artefacts, lowering the signal-to-noise ratio, and hampering expert interpretation. The traditional seizure-detection method of professional review of long-term EEG signals is an expensive, time-consuming, and challenging task. To reduce the complexity and cost of the task, researchers have developed several seizure-detection approaches, primarily focusing on classification systems and spectral feature extraction. While these methods can achieve high/optimal performances, the system may require retraining and following up with the feature extraction for each new patient, thus making it impractical for real-world applications. Herein, we present a straightforward manual/automated detection system based on the simple seizure feature amplification analysis to minimize these practical difficulties. Our algorithm (a simplified version is available as additional material), borrowing from the telecommunication discipline, treats the seizure as the carrier of information and tunes filters to this specific bandwidth, yielding a viable, computationally inexpensive solution. Manual tests gave 93% sensitivity and 96% specificity at a false detection rate of 0.04/h. Automated analyses showed 88% and 97% sensitivity and specificity, respectively. Moreover, our proposed method can accurately detect seizure locations within the brain. In summary, the proposed method has excellent potential, does not require training on new patient data, and can aid in the localization of seizure focus/origin.

Biosignal-Based Human-Machine Interfaces for Assistance and Rehabilitation: A Survey.

As a definition, Human-Machine Interface (HMI) enables a person to interact with a device. Starting from elementary equipment, the recent development of novel techniques and unobtrusive devices for biosignals monitoring paved the way for a new class of HMIs, which take such biosignals as inputs to control various applications. The current survey aims to review the large literature of the last two decades regarding biosignal-based HMIs for assistance and rehabilitation to outline state-of-the-art and identify emerging technologies and potential future research trends. PubMed and other databases were surveyed by using specific keywords. The found studies were further screened in three levels (title, abstract, full-text), and eventually, 144 journal papers and 37 conference papers were included. Four macrocategories were considered to classify the different biosignals used for HMI control: biopotential, muscle mechanical motion, body motion, and their combinations (hybrid systems). The HMIs were also classified according to their target application by considering six categories: prosthetic control, robotic control, virtual reality control, gesture recognition, communication, and smart environment control. An ever-growing number of publications has been observed over the last years. Most of the studies (about 67%) pertain to the assistive field, while 20% relate to rehabilitation and 13% to assistance and rehabilitation. A moderate increase can be observed in studies focusing on robotic control, prosthetic control, and gesture recognition in the last decade. In contrast, studies on the other targets experienced only a small increase. Biopotentials are no longer the leading control signals, and the use of muscle mechanical motion signals has experienced a considerable rise, especially in prosthetic control. Hybrid technologies are promising, as they could lead to higher performances. However, they also increase HMIs' complexity, so their usefulness should be carefully evaluated for the specific application.

Wearable Bluetooth Triage Healthcare Monitoring System.

Triage is the first interaction between a patient and a nurse/paramedic. This assessment, usually performed at Emergency departments, is a highly dynamic process and there are international grading systems that according to the patient condition initiate the patient journey. Triage requires an initial rapid assessment followed by routine checks of the patients' vitals, including respiratory rate, temperature, and pulse rate. Ideally, these checks should be performed continuously and remotely to reduce the workload on triage nurses; optimizing tools and monitoring systems can be introduced and include a wearable patient monitoring system that is not at the expense of the patient's comfort and can be remotely monitored through wireless connectivity. In this study, we assessed the suitability of a small ceramic piezoelectric disk submerged in a skin-safe silicone dome that enhances contact with skin, to detect wirelessly both respiration and cardiac events at several positions on the human body. For the purposes of this evaluation, we fitted the sensor with a respiratory belt as well as a single lead ECG, all acquired simultaneously. To complete Triage parameter collection, we also included a medical-grade contact thermometer. Performances of cardiac and respiratory events detection were assessed. The instantaneous heart and respiratory rates provided by the proposed sensor, the ECG and the respiratory belt were compared via statistical analyses. In all considered sensor positions, very high performances were achieved for the detection of both cardiac and respiratory events, except for the wrist, which provided lower performances for respiratory rates. These promising yet preliminary results suggest the proposed wireless sensor could be used as a wearable, hands-free monitoring device for triage assessment within emergency departments. Further tests are foreseen to assess sensor performances in real operating environments.

Forcecardiography: A Novel Technique to Measure Heart Mechanical Vibrations onto the Chest Wall.

This paper presents forcecardiography (FCG), a novel technique to measure local, cardiac-induced vibrations onto the chest wall. Since the 19th century, several techniques have been proposed to detect the mechanical vibrations caused by cardiovascular activity, the great part of which was abandoned due to the cumbersome instrumentation involved. The recent availability of unobtrusive sensors rejuvenated the research field with the most currently established technique being seismocardiography (SCG). SCG is performed by placing accelerometers onto the subject's chest and provides information on major events of the cardiac cycle. The proposed FCG measures the cardiac-induced vibrations via force sensors placed onto the subject's chest and provides signals with a richer informational content as compared to SCG. The two techniques were compared by analysing simultaneous recordings acquired by means of a force sensor, an accelerometer and an electrocardiograph (ECG). The force sensor and the accelerometer were rigidly fixed to each other and fastened onto the xiphoid process with a belt. The high-frequency (HF) components of FCG and SCG were highly comparable (r > 0.95) although lagged. The lag was estimated by cross-correlation and resulted in about tens of milliseconds. An additional, large, low-frequency (LF) component, associated with ventricular volume variations, was observed in FCG, while not being visible in SCG. The encouraging results of this feasibility study suggest that FCG is not only able to acquire similar information as SCG, but it also provides additional information on ventricular contraction. Further analyses are foreseen to confirm the advantages of FCG as a technique to improve the scope and significance of pervasive cardiac monitoring.

Detection of Atrial Fibrillation from Single Lead ECG Signal Using Multirate Cosine Filter Bank and Deep Neural Network.

Atrial fibrillation (AF) is a cardiac arrhythmia which is characterized based on the irregsular beating of atria, resulting in, the abnormal atrial patterns that are observed in the electrocardiogram (ECG) signal. The early detection of this pathology is very helpful for minimizing the chances of stroke, other heart-related disorders, and coronary artery diseases. This paper proposes a novel method for the detection of AF pathology based on the analysis of the ECG signal. The method adopts a multi-rate cosine filter bank architecture for the evaluation of coefficients from the ECG signal at different subbands, in turn, the Fractional norm (FN) feature is evaluated from the extracted coefficients at each subband. Then, the AF detection is carried out using a deep learning approach known as the Hierarchical Extreme Learning Machine (H-ELM) from the FN features. The proposed method is evaluated by considering normal and AF pathological ECG signals from public databases. The experimental results reveal that the proposed multi-rate cosine filter bank based on FN features is effective for the detection of AF pathology with an accuracy, sensitivity and specificity values of 99.40%, 98.77%, and 100%, respectively. The performance of the proposed diagnostic features of the ECG signal is compared with other existing features for the detection of AF. The low-frequency subband FN features found to be more significant with a difference of the mean values as 0.69 between normal and AF classes.

Characterisation of Morphic Sensors for Body Volume and Shape Applications.

Stretchable conductive materials are originally conceived as radio frequency (RF) and electromagnetic interference (EMI) shielding materials, and, under stretch, they generally function as distributed strain-gauges. These commercially available conductive elastomers have found their space in low power health monitoring systems, for example, to monitor respiratory and cardiac functions. Conductive elastomers do not behave linearly due to material constraints; hence, when used as a sensor, a full characterisation to identify ideal operating ranges are required. In this paper, we studied how the continuous stretch cycles affected the material electrical and physical properties in different embodiment impressed by bodily volume change. We simulated the stretch associated with breathing using a bespoke stress rig to ensure reproducibility of results. The stretch rig is capable of providing constant sinusoidal waves in the physiological ranges of extension and frequency. The material performances is evaluated assessing the total harmonic distortion (THD), signal-to-noise ratio (SNR), correlation coefficient, peak to peak (P-P) amplitude, accuracy, repeatability, hysteresis, delay, and washability. The results showed that, among the three controlled variables, stretch length, stretch frequency and fabric width, the most significant factor to the signal quality is the stretch length. The ideal working region is within 2% of the original length. The material cut in strips of > 3 show more reliable to handle a variety of stretch parameter without losing its internal characteristics and electrical properties.

Towards Real-Time Heartbeat Classification: Evaluation of Nonlinear Morphological Features and Voting Method.

Abnormal heart rhythms are one of the significant health concerns worldwide. The current state-of-the-art to recognize and classify abnormal heartbeats is manually performed by visual inspection by an expert practitioner. This is not just a tedious task; it is also error prone and, because it is performed, post-recordings may add unnecessary delay to the care. The real key to the fight to cardiac diseases is real-time detection that triggers prompt action. The biggest hurdle to real-time detection is represented by the rare occurrences of abnormal heartbeats and even more are some rare typologies that are not fully represented in signal datasets; the latter is what makes it difficult for doctors and algorithms to recognize them. This work presents an automated heartbeat classification based on nonlinear morphological features and a voting scheme suitable for rare heartbeat morphologies. Although the algorithm is designed and tested on a computer, it is intended ultimately to run on a portable i.e., field-programmable gate array (FPGA) devices. Our algorithm tested on Massachusetts Institute of Technology- Beth Israel Hospital(MIT-BIH) database as per Association for the Advancement of Medical Instrumentation(AAMI) recommendations. The simulation results show the superiority of the proposed method, especially in predicting minority groups: the fusion and unknown classes with 90.4% and 100%.

A Comparison of Reflective Photoplethysmography for Detection of Heart Rate, Blood Oxygen Saturation, and Respiration Rate at Various Anatomical Locations.

Monitoring of vital signs is critical for patient triage and management. Principal assessments of patient conditions include respiratory rate heart/pulse rate and blood oxygen saturation. However, these assessments are usually carried out with multiple sensors placed in different body locations. The aim of this paper is to identify a single location on the human anatomy whereby a single 1 cm × 1 cm non-invasive sensor could simultaneously measure heart rate (HR), blood oxygen saturation (SpO2), and respiration rate (RR), at rest and while walking. To evaluate the best anatomical location, we analytically compared eight anatomical locations for photoplethysmography (PPG) sensors simultaneously acquired by a single microprocessor at rest and while walking, with a comparison to a commercial pulse oximeter and respiration rate ground truth. Our results show that the forehead produced the most accurate results for HR and SpO2 both at rest and walking, however, it had poor RR results. The finger recorded similar results for HR and SpO2, however, it had more accurate RR results. Overall, we found the finger to be the best location for measurement of all three parameters at rest; however, no site was identified as capable of measuring all parameters while walking.

Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation.

Upper limb amputation is a condition that significantly restricts the amputees from performing their daily activities. The myoelectric prosthesis, using signals from residual stump muscles, is aimed at restoring the function of such lost limbs seamlessly. Unfortunately, the acquisition and use of such myosignals are cumbersome and complicated. Furthermore, once acquired, it usually requires heavy computational power to turn it into a user control signal. Its transition to a practical prosthesis solution is still being challenged by various factors particularly those related to the fact that each amputee has different mobility, muscle contraction forces, limb positional variations and electrode placements. Thus, a solution that can adapt or otherwise tailor itself to each individual is required for maximum utility across amputees. Modified machine learning schemes for pattern recognition have the potential to significantly reduce the factors (movement of users and contraction of the muscle) affecting the traditional electromyography (EMG)-pattern recognition methods. Although recent developments of intelligent pattern recognition techniques could discriminate multiple degrees of freedom with high-level accuracy, their efficiency level was less accessible and revealed in real-world (amputee) applications. This review paper examined the suitability of upper limb prosthesis (ULP) inventions in the healthcare sector from their technical control perspective. More focus was given to the review of real-world applications and the use of pattern recognition control on amputees. We first reviewed the overall structure of pattern recognition schemes for myo-control prosthetic systems and then discussed their real-time use on amputee upper limbs. Finally, we concluded the paper with a discussion of the existing challenges and future research recommendations.

Hybrid brain-computer interface for biomedical cyber-physical system application using wireless embedded EEG systems.

BACKGROUND: One of the key challenges of the biomedical cyber-physical system is to combine cognitive neuroscience with the integration of physical systems to assist people with disabilities. Electroencephalography (EEG) has been explored as a non-invasive method of providing assistive technology by using brain electrical signals. METHODS: This paper presents a unique prototype of a hybrid brain computer interface (BCI) which senses a combination classification of mental task, steady state visual evoked potential (SSVEP) and eyes closed detection using only two EEG channels. In addition, a microcontroller based head-mounted battery-operated wireless EEG sensor combined with a separate embedded system is used to enhance portability, convenience and cost effectiveness. This experiment has been conducted with five healthy participants and five patients with tetraplegia. RESULTS: Generally, the results show comparable classification accuracies between healthy subjects and tetraplegia patients. For the offline artificial neural network classification for the target group of patients with tetraplegia, the hybrid BCI system combines three mental tasks, three SSVEP frequencies and eyes closed, with average classification accuracy at 74% and average information transfer rate (ITR) of the system of 27 bits/min. For the real-time testing of the intentional signal on patients with tetraplegia, the average success rate of detection is 70% and the speed of detection varies from 2 to 4 s.

Regular snoring is associated with uncontrolled hypertension.

Snoring may be a risk factor for cardiovascular disease independent of other co-morbidities. However, most prior studies have relied on subjective, self-report, snoring evaluation. This study assessed snoring prevalence objectively over multiple months using in-home monitoring technology, and its association with hypertension prevalence. In this study, 12,287 participants were monitored nightly for approximately six months using under-the-mattress sensor technology to estimate the average percentage of sleep time spent snoring per night and the estimated apnea-hypopnea index (eAHI). Blood pressure cuff measurements from multiple daytime assessments were averaged to define uncontrolled hypertension based on mean systolic blood pressure≥140 mmHg and/or a mean diastolic blood pressure ≥90 mmHg. Associations between snoring and uncontrolled hypertension were examined using logistic regressions controlled for age, body mass index, sex, and eAHI. Participants were middle-aged (mean ± SD; 50 ± 12 y) and most were male (88%). There were 2467 cases (20%) with uncontrolled hypertension. Approximately 29, 14 and 7% of the study population snored for an average of >10, 20, and 30% per night, respectively. A higher proportion of time spent snoring (75th vs. 5th; 12% vs. 0.04%) was associated with a ~1.9-fold increase (OR [95%CI]; 1.87 [1.63, 2.15]) in uncontrolled hypertension independent of sleep apnea. Multi-night objective snoring assessments and repeat daytime blood pressure recordings in a large global consumer sample, indicate that snoring is common and positively associated with hypertension. These findings highlight the potential clinical utility of simple, objective, and noninvasive methods to detect snoring and its potential adverse health consequences.

Are we getting enough sleep? Frequent irregular sleep found in an analysis of over 11 million nights of objective in-home sleep data.

OBJECTIVES: Evidence-based guidelines recommend that adults should sleep 7-9 h/night for optimal health and function. This study used noninvasive, multinight, objective sleep monitoring to determine average sleep duration and sleep duration variability in a large global community sample, and how often participants met the recommended sleep duration range. METHODS: Data were analyzed from registered users of the Withings under-mattress Sleep Analyzer (predominantly located in Europe and North America) who had ≥28 nights of sleep recordings, averaging ≥4 per week. Sleep durations (the average and standard deviation) were assessed across a ∼9-month period. Associations between age groups, sex, and sleep duration were assessed using linear and logistic regressions, and proportions of participants within (7-9 hours) or outside (<7 hours or >9 hours) the recommended sleep duration range were calculated. RESULTS: The sample consisted of 67,254 adults (52,523 males, 14,731 females; aged mean ± SD 50 ± 12 years). About 30% of adults demonstrated an average sleep duration outside the recommended 7-9 h/night. Even in participants with an average sleep duration within 7-9 hours, about 40% of nights were outside this range. Only 15% of participants slept between 7 and 9 hours for at least 5 nights per week. Female participants had significantly longer sleep durations than male participants, and middle-aged participants had shorter sleep durations than younger or older participants. CONCLUSIONS: These findings indicate that a considerable proportion of adults are not regularly sleeping the recommended 7-9 h/night. Even among those who do, irregular sleep is prevalent. These novel data raise several important questions regarding sleep requirements and the need for improved sleep health policy and advocacy.

Unraveling the complexities of pathological voice through saliency analysis.

The human voice is an essential communication tool, but various disorders and habits can disrupt it. Diagnosis of pathological and abnormal voices is very important. Conventional diagnosis of these voice pathologies can be invasive and costly. Voice pathology disorders can be effectively detected using Artificial Intelligence and computer-aided voice pathology classification tools. Previous studies focused primarily on binary classification, leaving limited attention to multi-class classification. This study proposes three different neural network architectures to investigate the feature characteristics of three voice pathologies-Hyperkinetic Dysphonia, Hypokinetic Dysphonia, Reflux Laryngitis, and healthy voices using multi-class classification and the Voice ICar fEDerico II (VOICED) dataset. The study proposes UNet++ autoencoder-based denoiser techniques for accurate feature extraction to overcome noisy data. The architectures include a Multi-Layer Perceptron (MLP) trained on structured feature sets, a Short-Time Fourier Transform (STFT) model, and a Mel-Frequency Cepstral Coefficients (MFCC) model. The MLP model on 143 features achieved 97.1% accuracy, while the STFT model showed similar performance with increased sensitivity of 99.8%. The MFCC model maintained 97.1% accuracy but with a smaller model size and improved accuracy on the Reflux Laryngitis class. The study identifies crucial features through saliency analysis and reveals that detecting voice abnormalities requires the identification of regions of inaudible high-pitch sounds. Additionally, the study highlights the challenges posed by limited and disjointed pathological voice databases and proposes solutions for enhancing the performance of voice abnormality classification. Overall, the study's findings have potential applications in clinical applications and specialized audio-capturing tools.