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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).
JMIR Biomedical Engineering publishes since 2016 and features a rapid and thorough peer-review process. Articles are carefully copyedited and XML-tagged, ready for submission in PubMed Central.
Background: Maturity-onset diabetes of the young (MODY) is a group of dominantly inherited monogenic diabetes, with HNF4A-MODY, GCK-MODY, and HNF1A-MODY as the three most common forms based on the causal genes. Molecular diagnosis of MODY is important for precise treatment. Although a DNA variant causing MODY can be assessed based on the criteria of the American College of Medical Genetics and Genomics (ACMG) guidelines, gene-specific assessment of disease-causing mutations is important to differentiate among MODY subtypes. As the ACMG criteria were not originally designed for machine-learning algorithms, they are not true independent variables.
Objective: The aim of this study was to develop machine-learning models for interpretation of DNA variants and MODY diagnosis using the ACMG criteria.
Methods: We applied machine-learning models for interpretation of DNA variants in MODY genes defined by the ACMG criteria based on the Human Gene Mutation Database (HGMD) and ClinVar database.
Results: With a machine-learning procedure, we found that the weight matrix of the ACMG criteria was significantly different between the three MODY genes HNF1A, HNF4A, and GCK. The models showed high predictive abilities with accuracy over 95%.
Conclusions: Our results highlight the need for applying different weights of the ACMG criteria in relation to different MODY genes for accurate functional classification. As proof of principle, we applied the ACMG criteria as feature vectors in a machine-learning model and obtained a precision-based result.
Background: Physical activity has been shown to decrease cardiovascular mortality and morbidity. Walking, a simple physical activity which is an integral part of daily life, is a feasible and safe activity for patients with heart failure (HF). A step counter, measuring daily walking activity, might be a motivational factor for increased activity.
Objective: The aim of this study was to examine the association between walking activity and demographical and clinical data of patients with HF, and whether these associations could be used as predictors of walking activity.
Methods: A total of 65 patients with HF from the Future Patient Telerehabilitation (FPT) program were included in this study. The patients monitored their daily activity using a Fitbit step counter for 1 year. This monitoring allowed for continuous and safe data transmission of self-monitored activity data.
Results: A higher walking activity was associated with younger age, lower New York Heart Association (NYHA) classification, and higher ejection fraction (EF). There was a statistically significant correlation between the number of daily steps and NYHA classification at baseline (P=.01), between the increase in daily steps and EF at baseline (P<.001), and between the increase in daily steps and improvement in EF (P=.005). The patients’ demographic, clinical, and activity data could predict 81% of the variation in daily steps.
Conclusions: This study demonstrated an association between demographic, clinical, and activity data for patients with HF that could predict daily steps. A step counter can thus be a useful tool to help patients monitor their own physical activity.
Trial Registration: ClinicalTrials.gov NCT03388918; https://clinicaltrials.gov/ct2/show/NCT03388918
Background: Rhythm, brain, and body are closely linked. Humans can synchronize their movement to auditory rhythms in ways that can improve the regularity of movement while reducing perceived effort. However, the ability to perform rhythmic movement may be disrupted by various neurological conditions. Many such conditions impair mechanisms that control movement, such as gait, but typically without rhythmic perception being affected. This paper focuses on hemiparetic stroke, a neurological condition that affects one side of the body. Hemiparetic stroke can cause severe asymmetries in gait, leading to numerous physical problems ranging from muscle degeneration to bone fractures. Movement synchronization via entrainment to auditory metronomes is known to improve asymmetry and related gait problems; this paper presents the first systematic study of entrainment for gait rehabilitation via the haptic modality.
Objective: This paper explores the gait rehabilitation of people with hemiparesis following a stroke or brain injury, by a process of haptic entrainment to rhythmic cues. Various objective measures, such as stride length and stride time, are considered.
Methods: This study is a quantitative gait study combining temporal and spatial data on haptically cued participants with hemiparetic stroke and brain injury. We designed wearable devices to deliver the haptic rhythm, called Haptic Bracelets, which were placed on the leg near the knee. Spatial data were recorded using a Qualisys optical motion capturing system, consisting of 8 optoelectronic cameras, and 20 markers placed on anatomical lower limb landmarks and 4 additional tracking clusters placed on the right and left shank and thigh. Gait characteristics were measured before, during, and after cueing.
Results: All 11 successfully screened participants were able to synchronize their steps to a haptically presented rhythm. Specifically, 6 participants demonstrated immediate improvements regarding their temporal gait characteristics, and 3 of the 6 improved their gait in terms of spatial characteristics.
Conclusions: Considering the great variability between survivors of stroke and brain injury and the limited number of available participants in our study, there is no claim of statistical evidence that supports a formal experimental result of improved gait. However, viewing this empirical gait investigation as a set of 11 case studies, more modest empirical claims can be made. All participants were able to synchronize their steps to a haptically presented rhythm. For a substantial proportion of participants, an immediate (though not necessarily lasting) improvement of temporal gait characteristics was found during cueing. Some improvements over baseline occurred immediately after, rather than during, haptic cueing. Design issues and trade-offs are identified, and interactions between perception, sensory deficit, attention, memory, cognitive load, and haptic entrainment are noted.
Background: Heart rate variability (HRV) is used to assess cardiac health and autonomic nervous system capabilities. With the growing popularity of commercially available wearable technologies, the opportunity to unobtrusively measure HRV via photoplethysmography (PPG) is an attractive alternative to electrocardiogram (ECG), which serves as the gold standard. PPG measures blood flow within the vasculature using color intensity. However, PPG does not directly measure HRV; it measures pulse rate variability (PRV). Previous studies comparing consumer-grade PRV with HRV have demonstrated mixed results in short durations of activity under controlled conditions. Further research is required to determine the efficacy of PRV to estimate HRV under free-living conditions.
Objective: This study aims to compare PRV estimates obtained from a consumer-grade PPG sensor with HRV measurements from a portable ECG during unsupervised free-living conditions, including sleep, and examine factors influencing estimation, including measurement conditions and simple editing methods to limit motion artifacts.
Methods: A total of 10 healthy adults were recruited. Data from a Microsoft Band 2 and a Shimmer3 ECG unit were recorded simultaneously using a smartphone. Participants wore the devices for >90 min during typical day-to-day activities and while sleeping. After filtering, ECG data were processed using a combination of discrete wavelet transforms and peak-finding methods to identify R-R intervals. P-P intervals were edited for deletion using methods based on outlier detection and by removing sections affected by motion artifacts. Common HRV metrics were compared, including mean N-N, SD of N-N intervals, percentage of subsequent differences >50 ms (pNN50), root mean square of successive differences, low-frequency power (LF), and high-frequency power. Validity was assessed using root mean square error (RMSE) and Pearson correlation coefficient (R2).
Results: Data sets for 10 days and 9 corresponding nights were acquired. The mean RMSE was 182 ms (SD 48) during the day and 158 ms (SD 67) at night. R2 ranged from 0.00 to 0.66, with 2 of 19 (2 nights) trials considered moderate, 7 of 19 (2 days, 5 nights) fair, and 10 of 19 (8 days, 2 nights) poor. Deleting sections thought to be affected by motion artifacts had a minimal impact on the accuracy of PRV measures. Significant HRV and PRV differences were found for LF during the day and R-R, SDNN, pNN50, and LF at night. For 8 of the 9 matched day and night data sets, R2 values were higher at night (P=.08). P-P intervals were less sensitive to rapid R-R interval changes.
Conclusions: Owing to overall poor concurrent validity and inconsistency among participant data, PRV was found to be a poor surrogate for HRV under free-living conditions. These findings suggest that free-living HRV measurements would benefit from examining alternate sensing methods, such as multiwavelength PPG and wearable ECG.
Background: Electrogastrography is a noninvasive electrophysiological procedure used to measure gastric myoelectrical activity. EGG methods have been used to investigate the mechanisms of the human digestive system and as a clinical tool. Abnormalities in gastric myoelectrical activity have been observed in subjects with diabetes.
Objective: The objective of this study was to use the electrogastrograms (EGGs) from healthy individuals and subjects with diabetes to identify potentially informative features for the diagnosis of diabetes using EGG signals.
Methods: A total of 30 features were extracted from the EGGs of 30 healthy individuals and 30 subjects with diabetes. Of these, 20 potentially informative features were selected using a genetic algorithm–based feature selection process. The selected features were analyzed for further classification of EGG signals from healthy individuals and subjects with diabetes.
Results: This study demonstrates that there are distinct variations between the EGG signals recorded from healthy individuals and those from subjects with diabetes. Furthermore, the study reveals that the features Maragos fractal dimension and Hausdorff box-counting fractal dimension have a high degree of correlation with the mobility of EGGs from healthy individuals and subjects with diabetes.
Conclusions: Based on the analysis on the extracted features, the selected features are suitable for the design of automated classification systems to identify healthy individuals and subjects with diabetes.
Background: Electroconvulsive therapy (ECT) is one of the oldest, most effective, and potentially life-saving noninvasive brain stimulation treatments for psychiatric illnesses such as severe depression, mania, and catatonia. The decision-making process to use ECT involves well-informed discussion between the clinician and the client. A platform, like an app, which provides this information in an easy-to-understand format may be of benefit to various stakeholders in making an informed decision. Apps developed by clinicians/hospitals taking into consideration user perspectives will filter and provide trustworthy information to the users. In this regard, the ECT app, an app which is freely available for download at the Apple Store, was developed by the Leicestershire Partnership National Health Service (NHS) Trust and Leicestershire Health Informatics Service (LHIS).
Objective: The objective of this study is to evaluate and demonstrate the accessibility of the ECT app to the chosen audiences it was created for, via a paper and electronic questionnaire.
Methods: A survey was conducted between January 2017 and March 2019. A survey questionnaire designed for the study was sent to mental health professionals, medical students, patients, carers, and members of the public via post, email, and SurveyMonkey or informed via posts shared in Psychiatry online groups and face-to-face contact. All participants who were willing to participate in the study were included.
Results: Results were collected via paper forms, email responses, and SurveyMonkey and all were inputted into SurveyMonkey to facilitate analysis. A total of 20 responses were received during the study period (January 2017 to March 2019). The participants of the survey, which included a mixed group of professionals (12/20, 60%), patients (3/20, 15%), and carers (1/20, 5%), opined that the app was easy to download (14/20, 70%) and use (9/20, 45%); contained adequate information (19/20, 95%); they felt more informed after having used the app (9/20, 45%); and they would recommend it to others (19/20, 95%). The participants of the survey also provided suggestions on the app (10/20, 50%).
Conclusions: The ECT app can be beneficial in sharing appropriate information to professionals and the public alike and help in gathering unbiased and nonjudgmental information on the current use of ECT as a treatment option.
Background: Our aims were to determine the usability and practicality of the endoscope-i system, a novel mobile attachment for aural endoscopy. This incorporated assessing the ease of use of the endoscope-i for different professionals, and ultimately improving the system by receiving constructive feedback.
Objective: Our objectives were to assess the ease of the endoscope-i system in conducting an aural examination and to assess its feasibility for integrating its use into clinical practice. We looked to assess its ease, effectiveness, and efficiency; to compare this to current practices with otoscopes; and to determine whether participants perceived the system to be able to produce an image of sufficient quality to make a clinical assessment. Finally, we wanted to assess the usefulness of the current training given for using the system, and we sought to gain feedback for the product from the differing specialists.
Methods: A formative usability study of the endoscope-i system was conducted with 5 health care professionals. Each session lasted 40 minutes and involved audio/video consent, a hands-on session, a private semistructured interview, and an option to discuss the device with a company representative.
Results: All participants found the endoscope-i system easy to use. The image quality was perceived to be greater than that achieved by current otoscopes. The ability to record images and view them retrospectively was also seen as a positive.
Conclusions: This study has not identified any significant issues relating to the design, functionality, or application of the endoscope-i. Participants perceived the system as superior to current options with a directly positive impact on their clinical practice.
Biometric systems use scanners to verify the identity of human beings by measuring the patterns of their behavioral or physiological characteristics. Some biometric systems are contactless and do not require direct touch to perform these measurements; others, such as fingerprint verification systems, require the user to make direct physical contact with the scanner for a specified duration for the biometric pattern of the user to be properly read and measured. This may increase the possibility of contamination with harmful microbial pathogens or of cross-contamination of food and water by subsequent users. Physical contact also increases the likelihood of inoculation of harmful microbial pathogens into the respiratory tract, thereby triggering infectious diseases. In this viewpoint, we establish the likelihood of infectious disease transmission through touch-based fingerprint biometric devices and discuss control measures to curb the spread of infectious diseases, including COVID-19.
Background: Sleep is essential for human health. Considerable effort has been put into academic and industrial research and in the development of wireless body area networks for sleep monitoring in terms of nonintrusiveness, portability, and autonomy. With the help of rapid advances in smart sensing and communication technologies, various sleep monitoring systems (hereafter, sleep monitoring systems) have been developed with advantages such as being low cost, accessible, discreet, contactless, unmanned, and suitable for long-term monitoring.
Objective: This paper aims to review current research in sleep monitoring to serve as a reference for researchers and to provide insights for future work. Specific selection criteria were chosen to include articles in which sleep monitoring systems or devices are covered.
Methods: This review investigates the use of various common sensors in the hardware implementation of current sleep monitoring systems as well as the types of parameters collected, their position in the body, the possible description of sleep phases, and the advantages and drawbacks. In addition, the data processing algorithms and software used in different studies on sleep monitoring systems and their results are presented. This review was not only limited to the study of laboratory research but also investigated the various popular commercial products available for sleep monitoring, presenting their characteristics, advantages, and disadvantages. In particular, we categorized existing research on sleep monitoring systems based on how the sensor is used, including the number and type of sensors, and the preferred position in the body. In addition to focusing on a specific system, issues concerning sleep monitoring systems such as privacy, economic, and social impact are also included. Finally, we presented an original sleep monitoring system solution developed in our laboratory.
Results: By retrieving a large number of articles and abstracts, we found that hotspot techniques such as big data, machine learning, artificial intelligence, and data mining have not been widely applied to the sleep monitoring research area. Accelerometers are the most commonly used sensor in sleep monitoring systems. Most commercial sleep monitoring products cannot provide performance evaluation based on gold standard polysomnography.
Conclusions: Combining hotspot techniques such as big data, machine learning, artificial intelligence, and data mining with sleep monitoring may be a promising research approach and will attract more researchers in the future. Balancing user acceptance and monitoring performance is the biggest challenge in sleep monitoring system research.
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
