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JMIR Biomedical Engineering
Engineering for health technologies, medical devices, and innovative medical treatments and procedures
JMIR Biomedical Engineering (JBME) is a new sister journal of JMIR (the leading open-access journal in health informatics), focusing on the application of engineering principles, technologies, and medical devices to medicine and biology.
As an open access journal, we are read by clinicians and patients alike and have (as are all JMIR journals) a focus on readable and applied science reporting the design and evaluation of health innovations and emerging technologies. We publish original research, viewpoints, and reviews (both literature reviews and medical device/technology/app reviews).
During a limited period of time, there are no fees to publish in this journal. Articles are carefully copyedited and XML-tagged, ready for submission in PubMed Central.
The amount of medical video data that has to be securely stored has been growing exponentially. This rapid expansion is mainly caused by the introduction of higher video resolution such as 4K and 8K to medical devices and the growing usage of telemedicine services, along with a general trend toward increasing transparency with respect to medical treatment, resulting in more and more medical procedures being recorded. Such video data, as medical data, must be maintained for many years, resulting in datasets at the exabytes scale that each hospital must be able to store in the future. Currently, hospitals do not have the required information and communications technology infrastructure to handle such large amounts of data in the long run. In this paper, we discuss the challenges and possible solutions to this problem. We propose a generic architecture for a holistic, end-to-end recording and storage platform for hospitals, define crucial components, and identify existing and future solutions to address all parts of the system. This paper focuses mostly on the recording part of the system by introducing the major challenges in the area of bioinformatics, with particular focus on three major areas: video encoding, video quality, and video metadata.
Background: Parkinson disease (PD) is a common neurodegenerative disorder that affects between 7 and 10 million people worldwide. No objective test for PD currently exists, and studies suggest misdiagnosis rates of up to 34%. Machine learning (ML) presents an opportunity to improve diagnosis; however, the size and nature of data sets make it difficult to generalize the performance of ML models to real-world applications.
Objective: This study aims to consolidate prior work and introduce new techniques in feature engineering and ML for diagnosis based on vowel phonation. Additional features and ML techniques were introduced, showing major performance improvements on the large mPower vocal phonation data set.
Methods: We used 1600 randomly selected /aa/ phonation samples from the entire data set to derive rules for filtering out faulty samples from the data set. The application of these rules, along with a joint age-gender balancing filter, results in a data set of 511 PD patients and 511 controls. We calculated features on a 1.5-second window of audio, beginning at the 1-second mark, for a support vector machine. This was evaluated with 10-fold cross-validation (CV), with stratification for balancing the number of patients and controls for each CV fold.
Results: We showed that the features used in prior literature do not perform well when extrapolated to the much larger mPower data set. Owing to the natural variation in speech, the separation of patients and controls is not as simple as previously believed. We presented significant performance improvements using additional novel features (with 88.6% certainty, derived from a Bayesian correlated t test) in separating patients and controls, with accuracy exceeding 58%.
Conclusions: The results are promising, showing the potential for ML in detecting symptoms imperceptible to a neurologist.
Background: Limited attention has been given to the effectiveness of the platform swing walkway, which is a common way to improve gait pattern through activation of sensory stimuli (visual, auditory, vestibular, and somatosensory).
Objective: The objective of this study was to determine the effect of a platform swing walkway on gait parameters in children with diplegic cerebral palsy (CP).
Methods: A total of 30 children of both sexes (aged 6-8 years) with diplegic CP were enrolled in this study. They were randomly assigned into two groups of equal number: the control group (n=15) and the study group (n=15). The control group received the conventional physical therapy plan, whereas the study group received the same conventional physical therapy program in addition to gait training on a platform swing walkway. Temporal parameters during the gait cycle were collected using gait tracker video analysis, and the Growth Motor Function Measure Scale (GMFM-88) was used to assess standing and walking (Dimensions D and E) before and after the treatment program.
Results: A statistically significant improvement in both groups was noted when comparing the mean values of all measured variables before and after treatment (P≤.05). There were significant differences between the control and study groups with respect to all measured variables, which favored the study group when comparing the posttreatment outcomes (P≤.05).
Conclusions: Results suggest that gait training on platform swing walkways can be included as an alternative therapeutic modality to enhance gait parameters and gross motor function in children with diplegic CP.
Trial Registration: ClinicalTrials.gov NTC04246658; https://clinicaltrials.gov/ct2/show/NTC04246658
Background: Telerehabilitation programs are designed with the aim of improving the quality of services as well as overcoming existing limitations in terms of resource management and accessibility of services. This review will collect recent studies investigating telerehabilitation programs for patients with knee osteoarthritis while focusing on the technologies and services provided in the programs.
Objective: The main objective of this review is to identify and discuss the modes of service delivery and technologies in telerehabilitation programs for patients with knee osteoarthritis. The gaps, strengths, and weaknesses of programs will be discussed individually.
Methods: Studies published in English since 2000 were retrieved from the EMBASE, Scopus, Web of Science, Cumulative Index to Nursing and Allied Health Literature (CINAHL), PubMed, Physiotherapy Evidence Database (PEDro), and PsycINFO databases. The search words “telerehabilitation,” “telehealth,” “telemedicine,” “teletherapy,” and “ehealth” were combined with “knee” and “rehabilitation” to generate a data set of studies for screening and review. The final group of studies reviewed here includes those that implemented teletreatment for patients for at least 2 weeks of rehabilitation.
Results: In total, 1198 studies were screened, and the full text of 154 studies was reviewed. Of these, 38 studies were included, and data were extracted accordingly. Four modes of telerehabilitation service delivery were identified: phone-based, video-based, sensor-based, and expert system–based telerehabilitation. The intervention services provided in the studies included information, training, communication, monitoring, and tracking. Video-based telerehabilitation programs were frequently used. Among the identified services, information and educational material were introduced in only one-quarter of the studies.
Conclusions: Video-based telerehabilitation programs can be considered the best alternative solution to conventional treatment. This study shows that, in recent years, sensor-based solutions have also become more popular due to rapid developments in sensor technology. Nevertheless, communication and human-generated feedback remain as important as monitoring and intervention services.
Pediatric medical devices cover a broad array of indications and risk profiles, and have helped to reduce disease burden and improve quality of life for numerous children. However, many of the devices used in pediatrics are not intended for or tested on children. Several barriers have been identified that pose difficulties in bringing pediatric medical devices to the market. These include a small market and small sample size; unique design considerations; regulatory complexities; lack of infrastructure for research, development, and evaluation; and low return on investment. In 2007, the Food and Drug Administration (FDA) created the Pediatric Device Consortia (PDC) Grants Program under the administration of the Office of Orphan Products Development. In 2018, the FDA awarded over US $30 million to five new PDCs. The West Coast Consortium for Technology & Innovation in Pediatrics (CTIP) is one of these PDCs and is centered at the Children’s Hospital Los Angeles. In February 2019, CTIP convened its primary stakeholders to discuss its priorities and activities for the new grant cycle. In this paper, we have presented a report of the summit proceedings to raise awareness and advocate for patients and pediatric medical device innovators as well as to inform the activities and priorities of other organizations and agencies engaged in pediatric medical device development.
Background: Ambulatory assessment of electrodermal activity (EDA) is an emerging technique for capturing individuals’ autonomic responses to real-life events. There is currently little guidance available for processing and analyzing such data in an ambulatory setting.
Objective: This study aimed to describe and implement several methods for preprocessing and constructing features for use in modeling ambulatory EDA data, particularly for measuring stress.
Methods: We used data from a study examining the effects of stressful tasks on EDA of adolescent mothers (AMs). A biosensor band recorded EDA 4 times per second and was worn during an approximately 2-hour assessment that included a 10-min mother-child videotaped interaction. The initial processing included filtering noise and motion artifacts.
Results: We constructed the features of the EDA data, including the number of peaks and their amplitude as well as EDA reactivity, quantified as the rate at which AMs returned to baseline EDA following an EDA peak. Although the pattern of EDA varied substantially across individuals, various features of EDA may be computed for all individuals enabling within- and between-individual analyses and comparisons.
Conclusions: The algorithms we developed can be used to construct features for dry-electrode ambulatory EDA, which can be used by other researchers to study stress and anxiety.
Background: Heart rate (HR) and heart rate variability (HRV) measurements are widely used to monitor stress and recovery status in sedentary people and athletes. However, effective HRV monitoring should occur on a daily basis because sparse measurements do not allow for a complete view of the stress-recovery balance. Morning electrocardiography (ECG) measurements with HR straps are time-consuming and arduous to perform every day, and thus compliance with regular measurements is poor. Contact-free, ballistocardiography (BCG)-based Emfit QS is effortless for daily monitoring. However, to the best of our knowledge, there is no study on the accuracy of nocturnal HR and HRV measured via BCG under real-life conditions.
Objective: The aim of this study was to evaluate the accuracy of Emfit QS in measuring nocturnal HR and HRV.
Methods: Healthy participants (n=20) completed nocturnal HR and HRV recordings at home using Emfit QS and an ECG-based reference device (Firstbeat BG2) during sleep. Emfit QS measures BCG by a ferroelectret sensor installed under a bed mattress. HR and the root mean square of successive differences between RR intervals (RMSSD) were determined for 3-minute epochs and the sleep period mean.
Results: A trivial mean bias was observed in the mean HR (mean –0.8 bpm [beats per minute], SD 2.3 bpm, P=.15) and Ln (natural logarithm) RMSSD (mean –0.05 ms, SD 0.25 ms, P=.33) between Emfit QS and ECG. In addition, very large correlations were found in the mean values of HR (r=0.90, P<.001) and Ln RMSSD (r=0.89, P<.001) between the devices. A greater amount of erroneous or missing data (P<.001) was observed in the Emfit QS measurements (28.3%, SD 14.4%) compared with the reference device (1.1%, SD 2.3%). The results showed that 5.0% of the mean HR and Ln RMSSD values were outside the limits of agreement.
Conclusions: Based on the present results, Emfit QS provides nocturnal HR and HRV data with an acceptable, small mean bias when calculating the mean of the sleep period. Thus, Emfit QS has the potential to be used for the long-term monitoring of nocturnal HR and HRV. However, further research is needed to assess reliability in HR and HRV detection.
Background: Continuous cardiac monitoring with wireless sensors is an attractive option for early detection of arrhythmia and conduction disturbances and the prevention of adverse events leading to patient deterioration. We present a new sensor design (SmartCardia), a wearable wireless biosensor patch, for continuous cardiac and oxygen saturation (SpO2) monitoring.
Objective: This study aimed to test the clinical value of a new wireless sensor device (SmartCardia) and its usefulness in monitoring the heart rate (HR) and SpO2 of patients.
Methods: We performed an observational study and monitored the HR and SpO2 of patients admitted to the intensive care unit (ICU). We compared the device under test (SmartCardia) with the ICU-grade monitoring system (Dräger-Healthcare). We defined optimal correlation between the gold standard and the wireless system as <10% difference for HR and <4% difference for SpO2. Data loss and discrepancy between the two systems were critically analyzed.
Results: A total of 58 ICU patients (42 men and 16 women), with a mean age of 71 years (SD 11), were included in this study. A total of 13.49 (SD 5.53) hours per patient were recorded. This represents a total recorded period of 782.3 hours. The mean difference between the HR detected by the SmartCardia patch and the ICU monitor was 5.87 (SD 16.01) beats per minute (bias=–5.66, SD 16.09). For SpO2, the average difference was 3.54% (SD 3.86; bias=2.9, SD 4.36) for interpretable values. SmartCardia’s patch measures SpO2 only under low-to-no activity conditions and otherwise does not report a value. Data loss and noninterpretable values of SpO2 represented 26% (SD 24) of total measurements.
Conclusions: The SmartCardia device demonstrated clinically acceptable accuracy for HR and SpO2 monitoring in ICU patients.
Background: Alzheimer disease (AD) is a degenerative progressive brain disorder where symptoms of dementia and cognitive impairment intensify over time. Numerous factors exist that may or may not be related to the lifestyle of a patient that result in a higher risk for AD. Diagnosing the disorder in its beginning period is important, and several techniques are used to diagnose AD. A number of studies have been conducted on the detection and diagnosis of AD. This paper reports the empirical study performed on the longitudinal-based magnetic resonance imaging (MRI) Open Access Series of Brain Imaging dataset. Furthermore, the study highlights several factors that influence the prediction of AD.
Objective: This study aimed to correlate the effect of various factors such as age, gender, education, and socioeconomic background of patients with the development of AD. The effect of patient-related factors on the severity of AD was assessed on the basis of MRI features, Mini-Mental State Examination (MMSE), Clinical Dementia Rating (CDR), estimated total intracranial volume (eTIV), normalized whole brain volume (nWBV), and Atlas Scaling Factor (ASF).
Methods: In this study, we attempted to establish the role of longitudinal MRI in an exploratory data analysis (EDA) of AD patients. EDA was performed on the dataset of 150 patients for 343 MRI sessions (mean age 77.01 [SD 7.64] years). The T1-weighted MRI of each subject on a 1.5-Tesla Vision (Siemens) scanner was used for image acquisition. Scores of three features, MMSE, CDR, and ASF, were used to characterize the AD patients included in this study. We assessed the role of various features (ie, age, gender, education, socioeconomic status, MMSE, CDR, eTIV, nWBV, and ASF) on the prognosis of AD.
Results: The analysis further establishes the role of gender in the prevalence and development of AD in older people. Moreover, a considerable relationship has been observed between education and socioeconomic position on the progression of AD. Also, outliers and linearity of each feature were determined to rule out the extreme values in measuring the skewness. The differences in nWBV between CDR=0 (nondemented), CDR=0.5 (very mild dementia), and CDR=1 (mild dementia) are significant (ie, P<.01).
Conclusions: A substantial correlation has been observed between the pattern and other related features of longitudinal MRI data that can significantly assist in the diagnosis and determination of AD in older patients.
Dementia is a neurocognitive disorder, which affects older adults. There are currently no medication treatments available to cure dementia, but a number of biomedical technologies could be useful in assisting patients with dementia. With the continued growth of electronic commerce (e-commerce), online shopping for aging and health-related products will only continue to increase. Using the Tmall marketplace as an example, the purpose of this viewpoint is to describe the current trends of dementia-related products and devices available on an e-commerce platform. Feedback and critiques in the form of consumer reviews should be used to improve the design of dementia-related products. Online medical product consumers, however, must be vigilant about the effectiveness and risks of these biomedical devices.
Background: Stroke is increasingly one of the main causes of impairment and disability. Contextual and empirical evidence demonstrate that, mainly due to service delivery constraints, but also due to a move toward personalized health care in the comfort of patients’ homes, more stroke survivors undergo rehabilitation at home with minimal or no supervision. Due to this trend toward telerehabilitation, systems for stroke patient self-rehabilitation have become increasingly popular, with many solutions recently proposed based on technological advances in sensing, machine learning, and visualization. However, by targeting generic patient profiles, these systems often do not provide adequate rehabilitation service, as they are not tailored to specific patients’ needs. Objective: Our objective was to review state-of-the-art home rehabilitation systems and discuss their effectiveness from a patient-centric perspective. We aimed to analyze engagement enhancement of self-rehabilitation systems, as well as motivation, to identify the challenges in technology uptake. Methods: We performed a systematic literature search with 307,550 results. Then, through a narrative review, we selected 96 sources of existing home rehabilitation systems and we conducted a critical analysis. Based on the critical analysis, we formulated new criteria to be used when designing future solutions, addressing the need for increased patient involvement and individualism. We categorized the criteria based on (1) motivation, (2) acceptance, and (3) technological aspects affecting the incorporation of the technology in practice. We categorized all reviewed systems based on whether they successfully met each of the proposed criteria. Results: The criteria we identified were nonintrusive, nonwearable, motivation and engagement enhancing, individualized, supporting daily activities, cost-effective, simple, and transferable. We also examined the motivation method, suitability for elderly patients, and intended use as supplementary criteria. Through the detailed literature review and comparative analysis, we found no system reported in the literature that addressed all the set criteria. Most systems successfully addressed a subset of the criteria, but none successfully addressed all set goals of the ideal self-rehabilitation system for home use. Conclusions: We identified a gap in the state-of-the-art in telerehabilitation and propose a set of criteria for a novel patient-centric system to enhance patient engagement and motivation and deliver better self-rehabilitation commitment.
