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: To develop and assess an automatic and robust knee musculoskeletal finite element (MSK-FE) modeling pipeline., Methods: Magnetic resonance images (MRIs) were used to train nnU-Net networks for auto-segmentation of knee bones (femur, tibia, patella, and fibula), cartilages (femur, tibia, and patella), menisci, and major knee ligaments. Two different MRI sequences were used to broaden applicability. Next, we created MSK-FE models of an unseen dataset using two MSK-FE modeling pipelines: template-based and auto-meshing. MSK models had personalized knee geometries with multi-degree-of-freedom elastic foundation contacts. FE models used fibril-reinforced poroviscoelastic swelling material models for cartilages and menisci., Results: Volumes of knee bones, cartilages, and menisci did not significantly differ (p>0.05) across MRI sequences. MSK models estimated secondary knee kinematics during passive knee flexion tests consistent with in vivo and simulation-based values from the literature. Between the template-based and auto-meshing FE models, estimated cartilage mechanics often differed significantly (p<0.05), though differences were <15% (considering peaks during walking), i.e., <1.5 MPa for maximum principal stress, <1 percentage point for collagen fibril strain, and <3 percentage points for maximum shear strain., Conclusion: The template-based modeling provided a more rapid and robust tool than the auto-meshing approach, while the estimated knee biomechanics were comparable. Nonetheless, the auto-meshing approach might provide more accurate estimates in subjects with distinct knee irregularities, e.g., cartilage lesions., Significance: The MSK-FE modeling tool provides a rapid, easy-to-use, and robust approach for investigating task- and person-specific mechanical responses of the knee cartilage and menisci, holding significant promise, e.g., in personalized rehabilitation planning.

Objective: The key characteristics of light propagation are the average penetration depth, average maximum penetration depth, average maximum lateral spread, and average path length of photons. These parameters depend on tissue optical properties and, thus, on the pathological state of the tissue. Hence, they could provide diagnostic information on tissue integrity. This study investigates these parameters for articular cartilage which has a complex structure., Methods: We utilize Monte Carlo simulation to simulate photon trajectories in articular cartilage and estimate the average values of the light propagation parameters (penetration depth, maximum penetration depth, maximum lateral spread, and path length) in the spectral band of 400-1400 nm based on the optical properties of articular cartilage zonal layers and bulk tissue., Results: Our findings suggest that photons in the visible band probe a localized small volume of articular cartilage superficial and middle zones, while those in the NIR band penetrate deeper into the tissue and have larger lateral spread. In addition, we demonstrate that a simple model of articular cartilage tissue, based on the optical properties of the bulk tissue, is capable to provide an accurate description of the light-tissue interaction in articular cartilage., Conclusion: The results indicate that as the photons in the spectral band of 400-1400 nm can reach the full depth of articular cartilage matrix, they can provide viable information on its pathological state. Therefore, diffuse optical spectroscopy holds significant importance for objectively assessing articular cartilage health., Significance: In this study, for the first time, we estimate the light propagation parameters in articular cartilage.

Objective: The aim of this study was to assess how the photoplethysmogram frequency and amplitude responses to arousals from sleep differ between arousals caused by apneas and hypopneas with and without blood oxygen desaturations, and spontaneous arousals. Stronger arousal causes were hypothesized to lead to larger and faster responses., Methods and Procedures: Photoplethysmogram signal segments during and around respiratory and spontaneous arousals of 876 suspected obstructive sleep apnea patients were analyzed. Logistic functions were fit to the mean instantaneous frequency and instantaneous amplitude of the signal to detect the responses. Response intensities and timings were compared between arousals of different causes., Results: The majority of the studied arousals induced photoplethysmogram responses. The frequency response was more intense ([Formula: see text]) after respiratory than spontaneous arousals, and after arousals caused by apneas compared to those caused by hypopneas. The amplitude response was stronger ([Formula: see text]) following hypopneas associated with blood oxygen desaturations compared to those that were not. The delays of these responses relative to the electroencephalogram arousal start times were the longest ([Formula: see text]) after arousals caused by apneas and the shortest after spontaneous arousals and arousals caused by hypopneas without blood oxygen desaturations., Conclusion: The presence and type of an airway obstruction and the presence of a blood oxygen desaturation affect the intensity and the timing of photoplethysmogram responses to arousals from sleep., Clinical Impact: The photoplethysmogram responses could be used for detecting arousals and assessing their intensity, and the individual variation in the response intensity and timing may hold diagnostically significant information., (© 2024 The Authors.)

Objective: Hypoxic load is one of the main characteristics of obstructive sleep apnea (OSA) contributing to sympathetic overdrive and weakened cardiorespiratory coupling (CRC). Whether this association changes with increasing hypoxic load has remained obscure. Therefore, we aimed to study our hypothesis that increasing hypoxic load acutely decreases the CRC., Methods: We retrospectively analyzed the electrocardiography and nasal pressure signals in 5-min segment pairs (n = 36 926) recorded during clinical polysomnographies of 603 patients with suspected OSA. The segment pairs were pooled into five groups based on the hypoxic load severity described with the the total integrated area under the blood oxygen saturation curve during desaturations. In these severity groups, we determined the frequency-domain heart rate variability (HRV) parameters, the HRV and respiratory high-frequency (HF, 0.15-0.4 Hz) peaks, and the difference between those peaks. We also computed the spectral HF coherence between HRV and respiration in the HF band., Results: The ratio of low-frequency (LF, 0.04-0.15 Hz) to HF power increased from 1.047 to 1.805 (p < 0.001); the difference between the HRV and respiratory HF peaks increased from 0.001 Hz to 0.039 Hz (p < 0.001); and the spectral coherence between HRV and respiration in the HF band decreased from 0.813 to 0.689 (p < 0.001) as the hypoxic load increased., Conclusion and Significance: The vagal modulation decreases and CRC weakens significantly with increasing hypoxic load. Thus, the hypoxic load could be utilized more thoroughly in contemporary OSA diagnostics to better assess the severity of OSA-related cardiac stress.

Objective: We aimed to investigate how acute and long-term effects of atrial arrhythmias affect the desaturation severity and characteristics determined from the oxygen saturation signal in obstructive sleep apnea (OSA) patients., Methods: 520 suspected OSA patients were included in retrospective analyses. Eight desaturation area and slope parameters were calculated from blood oxygen saturation signals recorded during polysomnographic recordings. Patients were grouped based on whether they had previously diagnosed atrial arrhythmia (i.e., atrial fibrillation (AFib) or atrial flutter) or not. Furthermore, patients with a previous atrial arrhythmia diagnosis were sub-grouped based on whether they had continuous AFib or sinus rhythm during the polysomnographic recordings. Empirical cumulative distribution functions and linear mixed models were utilized to investigate the connection between diagnosed atrial arrhythmia and the desaturation characteristics., Results: Patients with previous atrial arrhythmia diagnosis had greater desaturation recovery area when the 100% oxygen saturation baseline reference was considered (β = 0.150--0.127, p ≤ 0.039) and more gradual recovery slopes (β = -0.181 to -0.199, p < 0.004) than patients without a previous atrial arrhythmia diagnosis. Furthermore, patients with AFib had more gradual oxygen saturation fall and recovery slopes than patients with sinus rhythm., Conclusion: Desaturation recovery characteristics in the oxygen saturation signal contains essential information about the cardiovascular response to hypoxemic periods., Significance: More comprehensive consideration of the desaturation recovery section could provide more detailed information about OSA severity, for example when developing new diagnostic parameters.

Obstructive sleep apnea is a disorder characterized by partial or complete airway obstructions during sleep. Our previously published algorithms use the minimally invasive nasal pressure signal routinely collected during diagnostic polysomnography (PSG) to segment breaths and estimate airflow limitation (using flow:drive) and minute ventilation for each breath. The first aim of this study was to investigate the effect of airflow signal quality on these algorithms, which can be influenced by oronasal breathing and signal-to-noise ratio (SNR). It was hypothesized that these algorithms would make inaccurate estimates when the expiratory portion of breaths is attenuated to simulate oronasal breathing, and pink noise is added to the airflow signal to reduce SNR. At maximum SNR and 0% expiratory amplitude, the average error was 2.7% for flow:drive, -0.5% eupnea for ventilation, and 19.7 milliseconds for breath duration (n = 257,131 breaths). At 20 dB and 0% expiratory amplitude, the average error was -15.1% for flow:drive, 0.1% eupnea for ventilation, and 28.4 milliseconds for breath duration (n = 247,160 breaths). Unexpectedly, simulated oronasal breathing had a negligible effect on flow:drive, ventilation, and breath segmentation algorithms across all SNRs. Airflow SNR ≥ 20 dB had a negligible effect on ventilation and breath segmentation, whereas airflow SNR ≥ 30 dB had a negligible effect on flow:drive. The second aim of this study was to explore the possibility of correcting these algorithms to compensate for airflow signal asymmetry and low SNR. An offset based on estimated SNR applied to individual breath flow:drive estimates reduced the average error to ≤ 1.3% across all SNRs at patient and breath levels, thereby facilitating for flow:drive to be more accurately estimated from PSGs with low airflow SNR.Clinical Relevance- This study demonstrates that our airflow limitation, ventilation, and breath segmentation algorithms are robust to reduced airflow signal quality.

Objective: Obstructive sleep apnea (OSA) is diagnosed using the apnea-hypopnea index (AHI), which is the average number of respiratory events per hour of sleep. Recently, machine learning algorithms for automatic AHI assessment have been developed, but many of them do not consider the individual sleep stages or events. In this study, we aimed to develop a deep learning model to simultaneously score both sleep stages and respiratory events. The hypothesis was that the scoring and subsequent AHI calculation could be performed utilizing pulse oximetry data only., Methods: Polysomnography recordings of 877 individuals with suspected OSA were used to train the deep learning models. The same architecture was trained with three different input signal combinations (model 1: photoplethysmogram (PPG) and oxygen saturation (SpO 2 ); model 2: PPG, SpO 2 , and nasal pressure; model 3: SpO 2 , nasal pressure, electroencephalogram (EEG), oronasal thermocouple, and respiratory belts)., Results: Model 1 reached comparative performance with models 2 and 3 for estimating the AHI (model 1 intraclass correlation coefficient (ICC) = 0.946; model 2 ICC = 0.931; model 3 ICC = 0.945), and REM-AHI (model 1 ICC = 0.912; model 2 ICC = 0.921; model 3 ICC = 0.883). The automatic sleep staging accuracies (wake/N1/N2/N3/REM) were 69%, 70%, and 79% with models 1, 2, and 3, respectively., Conclusion: AHI can be estimated using pulse oximetry-based automatic scoring. Explicit scoring of sleep stages and respiratory events allows visual validation of the automatic analysis, and provides information on OSA phenotypes., Significance: Automatic scoring of sleep stages and respiratory events with a simple pulse oximetry setup could allow cost-effective, large-scale screening of OSA.

Objective: We aimed to investigate the differences in electroencephalogram (EEG) gamma power (30-40 Hz) of respiratory arousals between varying types and severities of respiratory events, and in different sleep stages., Methods: Power spectral densities of EEG signals from diagnostic Type I polysomnograms of 869 patients with clinically suspected obstructive sleep apnea were investigated. Arousal gamma powers were compared between sleep stages, and between the type (obstructive apnea and hypopnea) and duration (10-20 s, 20-30 s, and >30 s) of the related respiratory event. Moreover, we investigated whether the presence of a ≥3% blood oxygen desaturation influenced the arousal gamma power., Results: Gamma power of respiratory arousals was the lowest in Stage R sleep and increased from Stage N1 towards Stage N3. Gamma power was higher when the arousals were caused by obstructive apneas compared to hypopneas. Moreover, arousal gamma power increased when the duration of the related apnea increased, whereas an increase in the hypopnea duration did not have a similar effect. Furthermore, respiratory events associated with desaturations increased the arousal gamma power more than respiratory events not associated with desaturations., Conclusion: Gamma power of respiratory arousals increased towards deeper sleep and as the severity of the related respiratory event increased in terms of type and duration of obstruction, and presence of desaturation., Significance: As increased gamma power might indicate a greater shift towards wakefulness, the present findings demonstrate that the respiratory arousal intensity and the magnitude of sleep disruption may vary depending on the event type and severity.

The diagnosis of obstructive sleep apnea is based on daytime symptoms and the frequency of respiratory events during the night. The respiratory events are scored manually from polysomnographic recordings, which is time-consuming and expensive. Therefore, automatic scoring methods could considerably improve the efficiency of sleep apnea diagnostics and release the resources currently needed for manual scoring to other areas of sleep medicine. In this study, we trained a long short-term memory neural network for automatic scoring of respiratory events using input signals from peripheral blood oxygen saturation, thermistor-airflow, nasal pressure -airflow, and thorax respiratory effort. The signals were extracted from 887 in-lab polysomnography recordings. 787 patients with suspected sleep apnea were used to train the neural network and 100 patients were used as an independent test set. The epoch-wise agreement between manual and automatic neural network scoring was high (88.9%, κ = 0.728). In addition, the apnea-hypopnea index (AHI) calculated from the automated scoring was close to the manually determined AHI with a mean absolute error of 3.0 events/hour and an intraclass correlation coefficient of 0.985. The neural network approach for automatic scoring of respiratory events achieved high accuracy and good agreement with manual scoring. The presented neural network could be used for analysis of large research datasets that are unfeasible to score manually, and has potential for clinical use in the future In addition, since the neural network scores individual respiratory events, the automatic scoring can be easily reviewed manually if desired.

Traditional sleep staging with non-overlapping 30-second epochs overlooks multiple sleep-wake transitions. We aimed to overcome this by analyzing the sleep architecture in more detail with deep learning methods and hypothesized that the traditional sleep staging underestimates the sleep fragmentation of obstructive sleep apnea (OSA) patients. To test this hypothesis, we applied deep learning-based sleep staging to identify sleep stages with the traditional approach and by using overlapping 30-second epochs with 15-, 5-, 1-, or 0.5-second epoch-to-epoch duration. A dataset of 446 patients referred for polysomnography due to OSA suspicion was used to assess differences in the sleep architecture between OSA severity groups. The amount of wakefulness increased while REM and N3 decreased in severe OSA with shorter epoch-to-epoch duration. In other OSA severity groups, the amount of wake and N1 decreased while N3 increased. With the traditional 30-second epoch-to-epoch duration, only small differences in sleep continuity were observed between the OSA severity groups. With 1-second epoch-to-epoch duration, the hazard ratio illustrating the risk of fragmented sleep was 1.14 (p = 0.39) for mild OSA, 1.59 (p < 0.01) for moderate OSA, and 4.13 (p < 0.01) for severe OSA. With shorter epoch-to-epoch durations, total sleep time and sleep efficiency increased in the non-OSA group and decreased in severe OSA. In conclusion, more detailed sleep analysis emphasizes the highly fragmented sleep architecture in severe OSA patients which can be underestimated with traditional sleep staging. The results highlight the need for a more detailed analysis of sleep architecture when assessing sleep disorders.

The identification of sleep stages is essential in the diagnostics of sleep disorders, among which obstructive sleep apnea (OSA) is one of the most prevalent. However, manual scoring of sleep stages is time-consuming, subjective, and costly. To overcome this shortcoming, we aimed to develop an accurate deep learning approach for automatic classification of sleep stages and to study the effect of OSA severity on the classification accuracy. Overnight polysomnographic recordings from a public dataset of healthy individuals (Sleep-EDF, n = 153) and from a clinical dataset (n = 891) of patients with suspected OSA were used to develop a combined convolutional and long short-term memory neural network. On the public dataset, the model achieved sleep staging accuracy of 83.7% (κ = 0.77) with a single frontal EEG channel and 83.9% (κ = 0.78) when supplemented with EOG. For the clinical dataset, the model achieved accuracies of 82.9% (κ = 0.77) and 83.8% (κ = 0.78) with a single EEG channel and two channels (EEG+EOG), respectively. The sleep staging accuracy decreased with increasing OSA severity. The single-channel accuracy ranged from 84.5% (κ = 0.79) for individuals without OSA diagnosis to 76.5% (κ = 0.68) for patients with severe OSA. In conclusion, deep learning enables automatic sleep staging for suspected OSA patients with high accuracy and expectedly, the accuracy decreased with increasing OSA severity. Furthermore, the accuracies achieved in the public dataset were superior to previously published state-of-the-art methods. Adding an EOG channel did not significantly increase the accuracy. The automatic, single-channel-based sleep staging could enable easy, accurate, and cost-efficient integration of EEG recording into diagnostic ambulatory recordings.

Using sleep laboratory polysomnography (PSG) is restricted for the diagnosis of only the most severe sleep disorders due to its low availability and high cost. Home PSG is more affordable, but applying conventional electroencephalography (EEG) electrodes increases its overall complexity and lowers the availability. Simple, self-administered single-channel EEG monitors on the other hand suffer from poor reliability. In this study, we aimed to quantify the reliability of self-administrated home PSG recordings conducted with a newly designed ambulatory electrode set (AES) that enables multichannel EEG, electrooculography, electromyography, and electrocardiography recordings. We assessed the sleep study success rate and technical quality of the recordings performed in subjects with possible sleep bruxism (SB). Thirty-two females and five males aged 39.6 ± 11.6 years (mean±SD) with self-reported SB were recruited in the study. Self-administrated home PSG recordings with two AES designs were conducted (n = 19 and 21). The technical quality of the recordings was graded based on the proportion of interpretable data. Technical failure rate for AES (both designs) was 5% and SB was scorable for 96.9% of all recorded data. Only one recording failed due to mistakes in self-applying the AES. We found that the proportion of good quality self-administrated EEG recordings is significantly higher when multiple channels are used compared to using a single channel. Sleep study success rates and proportion of recordings with high quality interpretable data from EEG channels of AES were comparable to that of conventional home PSG. Self-applicable AES has potential to become a reliable tool for widely available home PSG.

There is a growing need for an easy to use screening tool for the assessment of brain's electrical function in patients with altered mental status (AMS). The purpose of this study is to give a brief overview of the state-of-the-art in electrode technology, and to present a novel sub-hairline electrode set developed in our research group. Screen-printing technology was utilized to construct the electrode set consisting of ten electroencephalography (EEG) electrodes, two electrooculography (EOG) electrodes, two ground electrodes and two reference electrodes. Electrical characteristics of hydrogel-coated silver ink electrodes were found adequate for clinical EEG recordings as assessed by electrical impedance spectroscopy (EIS). The skin-electrode impedances remain stable and low enough at least two days enabling high-quality long-term recordings. Due to the proper material selection, thin ink layers and detachable zero insertion force (ZIF) - connector, electrode was observed to be CT- and MRI-compatible allowing imaging without removing the electrodes. Pilot EEG recordings gave very promising results and an on-going clinical trial with larger number of patients will show the true feasibility of this approach.

Quantitative measurement of articular cartilage using optical coherence tomography (OCT) is a potential approach for diagnosing the early degeneration of cartilage and assessing the quality of its repair. However, a non-perpendicular angle of the incident optical beam with respect to the tissue surface may cause uncertainty to the quantitative analysis, and therefore, significantly affect the reliability of measurement. This non-perpendicularity was systematically investigated in the current study using bovine articular cartilage with and without mechanical degradation. Ten fresh osteochondral disks were quantitatively measured before and after artificially induced surface degradation by mechanical grinding. The following quantitative OCT parameters were determined with a precise control of the surface inclination up to an angle of 10° using a step of 2°: optical reflection coefficient (ORC), variation of surface reflection (VSR) along the surface profile, optical roughness index (ORI) and optical backscattering (OBS). It was found that non-perpendicularity caused systematic changes to all of the parameters. ORC was the most sensitive and OBS the most insensitive to the inclination angle. At the optimal perpendicular angle, all parameters could detect significant changes after surface degradation (p < 0.01), except OBS (p > 0.05). Nonsignificant change of OBS after surface degradation was expected since OBS reflected properties of the internal cartilage tissue and was not affected by the superficial mechanical degradation. As a conclusion, quantitative OCT parameters are diagnostically potential for characterizing the cartilage degeneration. However, efforts through a better controlled operation or corrections based on computational compensation mechanism should be made to minimize the effects of non-perpendicularity of the incident optical beam when clinical use of quantitative OCT is considered for assessing the articular cartilage.

Background: Most bone ultrasound devices are designed for through-transmission measurements of the calcaneus. In principle, ultrasound backscattering measurements are possible at more typical fracture sites of the central skeleton. Unfortunately, soft tissue overlying the bones diminishes reliability of these measurements., Purpose: To apply the single-transducer dual-frequency ultrasound (DFUS) technique to eliminate the errors induced by soft tissue on the measurements of integrated reflection coefficient (IRC) in human distal femur in vivo., Material and Methods: Ultrasound and dual-energy X-ray absorptiometry (DXA) examinations were conducted on a bodybuilder during a 21-week training and dieting period., Results: Significant changes in quantity and composition of soft tissue took place during the diet. However, DXA measurements showed no significant effects on bone density measurements. The single transducer DFUS technique enabled the determination of local soft-tissue composition, as verified by comparison with the DXA (r=0.91, n=8, p<0.01). Further, the technique eliminated the soft-tissue-induced error from IRC measured for the bone. The uncorrected IRC associated significantly with the change in local soft-tissue composition (r=-0.83, n=8, p<0.05), whereas the corrected IRC values showed no significant dependence (r=-0.30, n=8, p=0.46) on local soft-tissue composition., Conclusion: The DFUS technique may significantly enhance the accuracy of clinical ultrasound measurements of bone.