Patients' Experience to MRI Examinations-A Systematic Qualitative Review With Meta-Synthesis.

BACKGROUND: Patients often mention distress, anxiety, or claustrophobia related to MRI, resulting in no-shows, disturbances of the workflow, and lasting psychological effects. Patients' experience varies and is moderated by socio-demographic aspects alongside the clinical condition. While qualitative studies help understand individuals' experiences, to date a systematic review and aggregation of MRI individuals' experience is lacking. PURPOSE: To investigate how adult patients experience MRI, and the characterizing factors. STUDY TYPE: Systematic review with meta-aggregation and meta-synthesis. POPULATION: 220 patients' reported experience of adults undergoing clinical MRI and 144 quotes from eight qualitative studies. ASSESSMENT: Systematic search in PubMed, Scopus, Web of Science, and PsycInfo databases according to the PRISMA guidelines. For quality appraisal, the Joanna Briggs Institute (JBI) tools were used. Convergent segregated approach was undertaken. DATA ANALYSIS: Participant recruitment, setting of exploration, type of interview, and analysis extracted through Joana Briggs Qualitative Assessment and Review Instrument (JBI QARI) tool. Meta-synthesis was supported by a concept map. For meta-aggregation, direct patient quotes were extracted, findings grouped, themes and characterizing factors at each stage abstracted, and categories coded in two cycles. Frequency of statements was quantified. Interviews' raw data unavailability impeded computer-aided analysis. RESULTS: Eight articles out of 12,755 initial studies, 220 patients, were included. Meta-aggregation of 144 patient quotes answered: (1) experiences before, at the scanning table, during, and after an MRI, (2) differences based on clinical condition, and (3) characterizing factors, including coping strategies, look-and-feel of medical technology, interaction with professionals, and information. Seven publications lack participants' health literacy level, occupation, and eight studies lack developmental conditions, ethnicity, or country of origin. Six studies were conducted in university hospitals. DATA CONCLUSION: Aggregation of patients' quotes provide a foundational description of adult patients' MRI experience across the stages of an MRI process. Insufficient raw data of individual quotes and limited socio-demographic diversity may constrain the understanding of individual experience and characterizing factors. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 5.

The Psychological, Physiological, and Behavioral Responses of Patients to Magnetic Resonance Imaging (MRI): A Systematic Review and Meta-Analysis.

BACKGROUND: MRI is generally well-tolerated although it may induce physiological stress responses and anxiety in patients. PURPOSE: Investigate the psychological, physiological, and behavioral responses of patients to MRI, their evolution over time, and influencing factors. STUDY TYPE: Systematic review with meta-analysis. POPULATION: 181,371 adult patients from 44 studies undergoing clinical MRI. ASSESSMENT: Pubmed, PsycInfo, Web of Science, and Scopus were systematically searched according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Quality appraisal was conducted with the Joanna Briggs Institute critical appraisal tools. Meta-analysis was conducted via Meta-Essentials workbooks when five studies were available for an outcome. Psychological and behavioral outcomes could be analyzed. Psychological outcomes were anxiety (State-Trait-Anxiety Inventory, STAI-S; 37) and willingness to undergo MRI again. Behavioral outcomes included unexpected behaviors: No shows, sedation, failed scans, and motion artifacts. Year of publication, sex, age, and positioning were examined as moderators. STATISTICAL TESTS: Meta-analysis, Hedge's g. A P value <0.05 was considered to indicate statistical significance. RESULTS: Of 12,755 initial studies, 104 studies were included in methodological review and 44 (181,371 patients) in meta-analysis. Anxiety did not significantly reduce from pre- to post-MRI (Hedge's g = -0.20, P = 0.051). Pooled values of STAI-S (37) were 44.93 (pre-MRI) and 40.36 (post-MRI). Of all patients, 3.9% reported unwillingness to undergo MRI again. Pooled prevalence of unexpected patient behavior was 11.4%; rates for singular behaviors were: Failed scans, 2.1%; no-shows, 11.5%; sedation, 3.3%; motion artifacts, 12.2%. Year of publication was not a significant moderator (all P > 0.169); that is, the patients' response was not improved in recent vs. older studies. Meta-analysis of physiological responses was not feasible since preconditions were not met for any outcome. DATA CONCLUSION: Advancements of MRI technology alone may not be sufficient to eliminate anxiety in patients undergoing MRI and related unexpected behaviors. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 5.

Comparison of non-pulsating reflective PPG signals in skin phantom, wearable device prototype, and Monte Carlo simulations.

We obtain and compare the non-pulsating part of reflective Photoplethysmogram (PPG) measurements in a porcine skin phantom and a wearable device prototype with Monte Carlo simulations and analyse the received signal. In particular, we investigate typical PPG wavelengths at 520, 637 and 940 nm and source-detector distances between 1.5 and 8.0 mm. We detail the phantom's optical parameters, the wearable device design, and the simulation setup. Monte Carlo simulations were using layer-based and voxel-based structures. Pattern of the detected photon weights showed comparable trends. PPG signal, differential pathlength factor (DPF), mean maximum penetration depth, and signal level showed dependencies on the source-detector distance d for all wavelengths. We demonstrate the signal dependence of emitter and detection angles, which is of interest for the development of wearables.

Segment-Based Spotting of Bowel Sounds Using Pretrained Models in Continuous Data Streams.

We analyse pretrained and non-pretrained deep neural models to detect 10-seconds Bowel Sounds (BS) audio segments in continuous audio data streams. The models include MobileNet, EfficientNet, and Distilled Transformer architectures. Models were initially trained on AudioSet and then transferred and evaluated on 84 hours of labelled audio data of eighteen healthy participants. Evaluation data was recorded in a semi-naturalistic daytime setting including movement and background noise using a smart shirt with embedded microphones. The collected dataset was annotated for individual BS events by two independent raters with substantial agreement (Cohen's Kappa κ = 0.74). Leave-One-Participant-Out cross-validation for detecting 10-second BS audio segments, i.e. segment-based BS spotting, yielded a best F1 score of 73% and 67%, with and without transfer learning respectively. The best model for segment-based BS spotting was EfficientNet-B2 with an attention module. Our results show that pretrained models could improve F1 score up to 26%, in particular, increasing robustness against background noise. Our segment-based BS spotting approach reduces the amount of audio data to be reviewed by experts from 84 h to 11 h, thus by  âˆ¼ 87%.

Co-simulation of human digital twins and wearable inertial sensors to analyse gait event estimation.

We propose a co-simulation framework comprising biomechanical human body models and wearable inertial sensor models to analyse gait events dynamically, depending on inertial sensor type, sensor positioning, and processing algorithms. A total of 960 inertial sensors were virtually attached to the lower extremities of a validated biomechanical model and shoe model. Walking of hemiparetic patients was simulated using motion capture data (kinematic simulation). Accelerations and angular velocities were synthesised according to the inertial sensor models. A comprehensive error analysis of detected gait events versus reference gait events of each simulated sensor position across all segments was performed. For gait event detection, we considered 1-, 2-, and 4-phase gait models. Results of hemiparetic patients showed superior gait event estimation performance for a sensor fusion of angular velocity and acceleration data with lower nMAEs (9%) across all sensor positions compared to error estimation with acceleration data only. Depending on algorithm choice and parameterisation, gait event detection performance increased up to 65%. Our results suggest that user personalisation of IMU placement should be pursued as a first priority for gait phase detection, while sensor position variation may be a secondary adaptation target. When comparing rotatory and translatory error components per body segment, larger interquartile ranges of rotatory errors were observed for all phase models i.e., repositioning the sensor around the body segment axis was more harmful than along the limb axis for gait phase detection. The proposed co-simulation framework is suitable for evaluating different sensor modalities, as well as gait event detection algorithms for different gait phase models. The results of our analysis open a new path for utilising biomechanical human digital twins in wearable system design and performance estimation before physical device prototypes are deployed.

Feasibility and Acceptability of Wearable Sensor Placement for Measuring Screen Time of Children.

Introduction/Purpose: Wearables that include a color light sensor are a promising measure of electronic screen use in adults. However, to extend this approach to children, we need to understand feasibility of wear placement. The purpose of this study was to examine parent perceptions of children's acceptability of different sensor placements and feasibility of free-living 3- to 7-day wear protocols. Methods: This study was conducted in three phases. In phase 1, caregivers (n=161) of 3- to 8-year-old children completed an online survey to rate aspects of fitting and likelihood of wear for seven methods (headband, eyeglasses, skin adhesive patch, shirt clip/badge, mask, necklace, and vest). In phase 2, children (n=31) were recruited to wear one of the top five prototypes for three days (n=6 per method). In phase 3, children (n=23) were recruited to wear prototypes of the top three prototypes from phase 2 (n=8 per method) for 7 days. In phases 2 and 3, parents completed wear logs and surveys about their experiences. Parents scored each wearable on three domains (ease of use, likelihood of wear, and child enjoyment). Scores were averaged to compute an everyday "usability" score (0, worst, to 200, best). Results: Phase 1 results suggested that the headband, eyeglasses, patch, clip/badge, and vest had the best potential for long-term wear. In phase 2, time spent wearing prototypes and usability scores were highest for the eyeglasses (10.4 hours/day, score=155.4), clip/badge (9.8 hours/day, score=145.8), and vest (7.1 hours/day, score=141.7). In phase 3, wearing time and usability scores were higher for the clip/badge (9.4 hours/day, score=169.6) and eyeglasses (6.5 hours/day, score=145.3) compared to the vest (4.8 hours/day, score=112.5). Conclusion: Results indicate that wearable sensors clipped to a child's shirt or embedded into eyeglasses are feasible and acceptable wear methods in free-living settings. The next step is to asses the quality, validity, and reliability of data captured using these wear methods.

A computational roadmap to electronic drugs.

A growing number of complex neurostimulation strategies promise symptom relief and functional recovery for several neurological, psychiatric, and even multi-organ disorders. Although pharmacological interventions are currently the mainstay of treatment, neurostimulation offers a potentially effective and safe alternative, capable of providing rapid adjustment to short-term variation and long-term decline of physiological functions. However, rapid advances made by clinical studies have often preceded the fundamental understanding of mechanisms underlying the interactions between stimulation and the nervous system. In turn, therapy design and verification are largely driven by clinical-empirical evidence. Even with titanic efforts and budgets, it is infeasible to comprehensively explore the multi-dimensional optimization space of neurostimulation through empirical research alone, especially since anatomical structures and thus outcomes vary dramatically between patients. Instead, we believe that the future of neurostimulation strongly depends on personalizable computational tools, i.e. Digital Neuro Twins (DNTs) to efficiently identify effective and safe stimulation parameters. DNTs have the potential to accelerate scientific discovery and hypothesis-driven engineering, and aid as a critical regulatory and clinical decision support tool. We outline here how DNTs will pave the way toward effective, cost-, time-, and risk-limited electronic drugs with a broad application bandwidth.

Correction: Schiboni et al. DynDSE: Automated Multi-Objective Design Space Exploration for Context-Adaptive Wearable IoT Edge Devices. Sensors 2020, 20, 6104.

Due to formal academic regulations, the affiliation of the university has been amended, and an "Acknowledgements" section has been added to the original publication [...].

Predicting Anxiety in Routine Palliative Care Using Bayesian-Inspired Association Rule Mining.

We propose a novel knowledge extraction method based on Bayesian-inspired association rule mining to classify anxiety in heterogeneous, routinely collected data from 9,924 palliative patients. The method extracts association rules mined using lift and local support as selection criteria. The extracted rules are used to assess the maximum evidence supporting and rejecting anxiety for each patient in the test set. We evaluated the predictive accuracy by calculating the area under the receiver operating characteristic curve (AUC). The evaluation produced an AUC of 0.89 and a set of 55 atomic rules with one item in the premise and the conclusion, respectively. The selected rules include variables like pain, nausea, and various medications. Our method outperforms the previous state of the art (AUC = 0.72). We analyzed the relevance and novelty of the mined rules. Palliative experts were asked about the correlation between variables in the data set and anxiety. By comparing expert answers with the retrieved rules, we grouped rules into expected and unexpected ones and found several rules for which experts' opinions and the data-backed rules differ, most notably with the patients' sex. The proposed method offers a novel way to predict anxiety in palliative settings using routinely collected data with an explainable and effective model based on Bayesian-inspired association rule mining. The extracted rules give further insight into potential knowledge gaps in the palliative care field.

DynDSE: Automated Multi-Objective Design Space Exploration for Context-Adaptive Wearable IoT Edge Devices.

We describe a simulation-based Design Space Exploration procedure (DynDSE) for wearable IoT edge devices that retrieve events from streaming sensor data using context-adaptive pattern recognition algorithms. We provide a formal characterisation of the design space, given a set of system functionalities, components and their parameters. An iterative search evaluates configurations according to a set of requirements in simulations with actual sensor data. The inherent trade-offs embedded in conflicting metrics are explored to find an optimal configuration given the application-specific conditions. Our metrics include retrieval performance, execution time, energy consumption, memory demand, and communication latency. We report a case study for the design of electromyographic-monitoring eyeglasses with applications in automatic dietary monitoring. The design space included two spotting algorithms, and two sampling algorithms, intended for real-time execution on three microcontrollers. DynDSE yielded configurations that balance retrieval performance and resource consumption with an F1 score above 80% at an energy consumption that was 70% below the default, non-optimised configuration. We expect that the DynDSE approach can be applied to find suitable wearable IoT system designs in a variety of sensor-based applications.

Estimating wearable motion sensor performance from personal biomechanical models and sensor data synthesis.

We present a fundamentally new approach to design and assess wearable motion systems based on biomechanical simulation and sensor data synthesis. We devise a methodology of personal biomechanical models and virtually attach sensor models to body parts, including sensor positions frequently considered for wearable devices. The simulation enables us to synthesise motion sensor data, which is subsequently considered as input for gait marker estimation algorithms. We evaluated our methodology in two case studies, including running athletes and hemiparetic patients. Our analysis shows that running speed affects gait marker estimation performance. Estimation error of stride duration varies between athletes across 834 simulated sensor positions and can soar up to 54%, i.e. 404 ms. In walking patients after stroke, we show that gait marker performance differs between affected and less-affected body sides and optimal sensor positions change over a period of movement therapy intervention. For both case studies, we observe that optimal gait marker estimation performance benefits from personally selected sensor positions and robust algorithms. Our methodology enables wearable designers and algorithm developers to rapidly analyse the design options and create personalised systems where needed, e.g. for patients with movement disorders.

Mobile Health Usage, Preferences, Barriers, and eHealth Literacy in Rheumatology: Patient Survey Study.

BACKGROUND: Mobile health (mHealth) defines the support and practice of health care using mobile devices and promises to improve the current treatment situation of patients with chronic diseases. Little is known about mHealth usage and digital preferences of patients with chronic rheumatic diseases. OBJECTIVE: The aim of the study was to explore mHealth usage, preferences, barriers, and eHealth literacy reported by German patients with rheumatic diseases. METHODS: Between December 2018 and January 2019, patients (recruited consecutively) with rheumatoid arthritis, psoriatic arthritis, and axial spondyloarthritis were asked to complete a paper-based survey. The survey included questions on sociodemographics, health characteristics, mHealth usage, eHealth literacy using eHealth Literacy Scale (eHEALS), and communication and information preferences. RESULTS: Of the patients (N=193) who completed the survey, 176 patients (91.2%) regularly used a smartphone, and 89 patients (46.1%) regularly used social media. Patients (132/193, 68.4%) believed that using medical apps could be beneficial for their own health. Out of 193 patients, only 8 (4.1%) were currently using medical apps, and only 22 patients (11.4%) stated that they knew useful rheumatology websites/mobile apps. Nearly all patients (188/193, 97.4%) would agree to share their mobile app data for research purposes. Out of 193 patients, 129 (66.8%) would regularly enter data using an app, and 146 patients (75.6%) would welcome official mobile app recommendations from the national rheumatology society. The preferred duration for data entry was not more than 15 minutes (110/193, 57.0%), and the preferred frequency was weekly (59/193, 30.6%). Medication information was the most desired app feature (150/193, 77.7%). Internet was the most frequently utilized source of information (144/193, 74.6%). The mean eHealth literacy was low (26.3/40) and was positively correlated with younger age, app use, belief in benefit of using medical apps, and current internet use to obtain health information. CONCLUSIONS: Patients with rheumatic diseases are very eager to use mHealth technologies to better understand their chronic diseases. This open-mindedness is counterbalanced by low mHealth usage and competency. Personalized mHealth solutions and clear implementation recommendations are needed to realize the full potential of mHealth in rheumatology.

Retrieval and Timing Performance of Chewing-Based Eating Event Detection in Wearable Sensors.

We present an eating detection algorithm for wearable sensors based on first detecting chewing cycles and subsequently estimating eating phases. We term the corresponding algorithm class as a bottom-up approach. We evaluated the algorithm using electromyographic (EMG) recordings from diet-monitoring eyeglasses in free-living and compared the bottom-up approach against two top-down algorithms. We show that the F1 score was no longer the primary relevant evaluation metric when retrieval rates exceeded approx. 90%. Instead, detection timing errors provided more important insight into detection performance. In 122 hours of free-living EMG data from 10 participants, a total of 44 eating occasions were detected, with a maximum F1 score of 99.2%. Average detection timing errors of the bottom-up algorithm were 2.4 ± 0.4 s and 4.3 ± 0.4 s for the start and end of eating occasions, respectively. Our bottom-up algorithm has the potential to work with different wearable sensors that provide chewing cycle data. We suggest that the research community report timing errors (e.g., using the metrics described in this work).

Synthesising motion sensor data from biomechanical simulations to investigate motion sensor placement and orientation variations.

We propose a motion sensor data synthesis approach to investigate the performance effect of sensor placement and orientation variation on health marker estimation. Using OpenSim, we simulate walking motion of patients after stroke and synthesise inertial sensor data. We analyse 384 sensor positions with 192 sensors simulated at each leg's thigh. To demonstrate how synthesised sensor data could be used to analyse the performance of functional ability estimation, we estimated scores from the Lower-Extremity Fugl-Meyer-Assessment (LE-FMA) using regression methods. We evaluated our approach using a public dataset, including 8 stroke patients and showed that LE-FMA scores could be estimated with an error below 0.12 score points on average, compared to manually derived scores. We further show that sensors should be preferably placed at the thigh front. Our approach demonstrates the potential of combining biomechanical simulations and motion sensor data synthesis with algorithms for health marker estimation, thus providing rapid insight into sensor positioning and orientation variation.

Physical Activity Comparison Between Body Sides in Hemiparetic Patients Using Wearable Motion Sensors in Free-Living and Therapy: A Case Series.

Background: Physical activity (PA) is essential in stroke rehabilitation of hemiparetic patients to avoid health risks, and moderate to vigorous PA could promote patients' recovery. However, PA assessments are limited to clinical environments. Little is known about PA in unguided free-living. Wearable sensors could reveal patients' PA during rehabilitation, and day-long long-term measurements over several weeks might reveal recovery trends of affected and less-affected body sides. Methods: We investigated PA in an observation study during outpatient rehabilitation in a day-care center. PA of affected and less-affected body sides, including upper and lower limbs were derived using wearable motion sensors. In this analysis we focused on PA during free-living and clinician guided therapies, and investigated differences between body-sides. Linear regressions were used to estimate metabolic equivalents for each limb at comparable scale. Non-parametric statistics were derived to quantify PA differences between body sides. Results: We analyzed 102 full-day movement data recordings from eleven hemiparetic patients during individual rehabilitation periods up to 79 days. The comparison between free-living and clinician guided therapy showed on average 16.1 % higher PA in the affected arm during therapy and 5.3 % higher PA in the affected leg during therapy. Average differences between free-living and therapy in the less-affected side were below 4.5 %. Conclusion: We analyzed PA of patients with a hemiparesis in two distinct rehabilitation settings, including free-living and clinician guided therapies over several weeks and compared MET values of affected and less-affected body sides. In particular, we investigated PA using individual regression models for each limb. We demonstrated that wearable motion sensors provide insights in patient's PA during rehabilitation. Although, no clear PA trends were found, our analysis showed patients' tendency to sedentary behavior, confirming previous lab study results. Our PA analysis approach could be used beyond clinical rehabilitation to devise personalized patient and limb-specific exercise recommendations in future remote rehabilitation.

Estimating Running Performance Combining Non-invasive Physiological Measurements and Training Patterns in Free-Living.

In this work, we use data acquired longitudinally, in free-living, to provide accurate estimates of running performance. In particular, we used the HRV4Training app and integrated APIs (e.g. Strava and TrainingPeaks) to acquire different sets of parameters, either via user input, morning measurements of resting physiology, or running workouts to estimate running 10 km running time. Our unique dataset comprises data on 2113 individuals, from world class triathletes to individuals just getting started with running, and it spans over 2 years. Analyzed predictors of running performance include anthropometrics, resting heart rate (HR) and heart rate variability (HRV), training physiology (heart rate during exercise), training volume, training patterns (training intensity distribution over multiple workouts, or training polarization) and previous performance. We build multiple linear regression models and highlight the relative impact of different predictors as well as trade-offs between the amount of data required for features extraction and the models accuracy in estimating running performance (10 km time). Cross-validated root mean square error (RMSE) for 10 km running time estimation was 2.6 minutes (4% mean average error, MAE, 0.87 R2), an improvement of 58% with respect to estimation models using anthropometrics data only as predictors. Finally, we provide insights on the relationship between training and performance, including further evidence of the importance of training volume and a polarized training approach to improve performance.

Longitudinal Walking Analysis in Hemiparetic Patients Using Wearable Motion Sensors: Is There Convergence Between Body Sides?

Background: Longitudinal movement parameter analysis of hemiparetic patients over several months could reveal potential recovery trends and help clinicians adapting therapy strategies to maximize recovery outcome. Wearable sensors offer potential for day-long movement recordings in realistic rehabilitation settings including activities of daily living, e.g., walking. The measurement of walking-related movement parameters of affected and non-affected body sides are of interest to determine mobility and investigate recovery trends. Methods: By comparing movement of both body sides, recovery trends across the rehabilitation duration were investigated. We derived and validated selected walking segments from free-living, day-long movement by using rules that do not require data-based training or data annotations. Automatic stride segmentation using peak detection was applied to walking segments. Movement parameters during walking were extracted, including stride count, stride duration, cadence, and sway. Finally, linear regression models over each movement parameter were derived to forecast the moment of convergence between body sides. Convergence points were expressed as duration and investigated in a patient observation study. Results: Convergence was analyzed in walking-related movement parameters in an outpatient study including totally 102 full-day recordings of inertial movement data from 11 hemiparetic patients. The recordings were performed over several months in a day-care centre. Validation of the walking extraction method from sensor data yielded sensitivities up to 80 % and specificity above 94 % on average. Comparison of automatically and manually derived movement parameters showed average relative errors below 6 % between affected and non-affected body sides. Movement parameter variability within and across patients was observed and confirmed by case reports, reflecting individual patient behavior. Conclusion: Convergence points were proposed as intuitive metric, which could facilitate training personalization for patients according to their individual needs. Our continuous movement parameter extraction and analysis, was feasible for realistic, day-long recordings without annotations. Visualizations of movement parameter trends and convergence points indicated that individual habits and patient therapies were reflected in walking and mobility. Context information of clinical case reports supported trend and convergence interpretation. Inconsistent convergence point estimation suggested individually varying deficiencies. Long-term recovery monitoring using convergence points could support patient-specific training strategies in future remote rehabilitation.

Monitoring Chewing and Eating in Free-Living Using Smart Eyeglasses.

We propose to 3-D-print personal fitted regular-look smart eyeglasses frames equipped with bilateral electromyography recording to monitor temporalis muscles' activity for automatic dietary monitoring. Personal fitting supported electrode-skin contacts are at temple ear bend and temple end positions. We evaluated the smart monitoring eyeglasses during in-lab and free-living studies of food chewing and eating event detection with ten participants. The in-lab study was designed to explore three natural food hardness levels and determine parameters of an energy-based chewing cycle detection. Our free-living study investigated whether chewing monitoring and eating event detection using smart eyeglasses is feasible in free-living. An eating event detection algorithm was developed to determine intake activities based on the estimated chewing rate. Results showed an average food hardness classification accuracy of 94% and chewing cycle detection precision and recall above 90% for the in-lab study and above 77% for the free-living study covering 122 hours of recordings. Eating detection revealed the 44 eating events with an average accuracy above 95%. We conclude that smart eyeglasses are suitable for monitoring chewing and eating events in free-living and even could provide further insights into the wearer's natural chewing patterns.

Detecting Disordered Breathing and Limb Movement Using In-Bed Force Sensors.

We present and evaluate measurement fusion and decision fusion for recognizing apnea and periodic limb movement in sleep episodes. We used an in-bed sensor system composed of an array of strain gauges to detect pressure changes corresponding to respiration and body movement. The sensor system was placed under the bed mattress during sleep and continuously recorded pressure changes. We evaluated both fusion frameworks in a study with nine adult participants that had mixed occurrences of normal sleep, apnea, and periodic limb movement. Both frameworks yielded similar recognition accuracies of 72.1 ± âˆ¼  12% compared to 63.7 ± 17.4% for a rule-based detection reported in the literature. We concluded that the pattern recognition methods can outperform previous rule-based detection methods for classifying disordered breathing and period limb movements simultaneously.