JMIR Publications

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  Biomedical Engineering Articles

  Jan 30, 2019

  Automatic Near Real-Time Outlier Detection and Correction in Cardiac Interbeat Interval Series for Heart Rate Variability Analysis: Singular Spectrum...

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  • Michael Lang  

  Abstract:

  Background: Heart rate variability (HRV) is derived from the series of R-R intervals extracted from an electrocardiographic (ECG) measurement. Ideally all components of the R-R series are the result of sinoatrial node depolarization. However, the actual R-R series are contaminated by outliers due to heart rhythm disturbances such as ectopic beats, which ought to be detected and corrected appropriately before HRV analysis. Objective: We have introduced a novel, lightweight, and near real-time method to detect and correct anomalies in the R-R series based on the singular spectrum analysis (SSA). This study aimed to assess the performance of the proposed method in terms of (1) detection performance (sensitivity, specificity, and accuracy); (2) root mean square error (RMSE) between the actual N-N series and the approximated outlier-cleaned R-R series; and (3) how it benchmarks against a competitor in terms of the relative RMSE. Methods: A lightweight SSA-based change-point detection procedure, improved through the use of a cumulative sum control chart with adaptive thresholds to reduce detection delays, monitored the series of R-R intervals in real time. Upon detection of an anomaly, the corrupted segment was substituted with the respective outlier-cleaned approximation obtained using recurrent SSA forecasting. Next, N-N intervals from a 5-minute ECG segment were extracted from each of the 18 records in the MIT-BIH Normal Sinus Rhythm Database. Then, for each such series, a number (randomly drawn integer between 1 and 6) of simulated ectopic beats were inserted at random positions within the series and results were averaged over 1000 Monte Carlo runs. Accordingly, 18,000 R-R records corresponding to 5-minute ECG segments were used to assess the detection performance whereas another 180,000 (10,000 for each record) were used to assess the error introduced in the correction step. Overall 198,000 R-R series were used in this study. Results: The proposed SSA-based algorithm reliably detected outliers in the R-R series and achieved an overall sensitivity of 96.6%, specificity of 98.4% and accuracy of 98.4%. Furthermore, it compared favorably in terms of discrepancies of the cleaned R-R series compared with the actual N-N series, outperforming an established correction method on average by almost 30%. Conclusions: The proposed algorithm, which leverages the power and versatility of the SSA to both automatically detect and correct artifacts in the R-R series, provides an effective and efficient complementary method and a potential alternative to the current manual-editing gold standard. Other important characteristics of the proposed method include the ability to operate in near real-time, the almost entirely model-free nature of the framework which does not require historical training data, and its overall low computational complexity.

  • JMIR Biomed Eng 2019;4(1):e10740

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  Biomedical Engineering Articles

  Nov 21, 2018

  Auralife Instant Blood Pressure App in Measuring Resting Heart Rate: Validation Study

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  Authors List:

  • Timothy B Plante  
  • Anna C O'Kelly  
  • Bruno Urrea  
  • Zane T Macfarlane  
  • Lawrence J Appel  
  • Edgar R Miller III  
  • Roger S Blumenthal  
  • Seth S Martin  

  Abstract:

  Background: mHealth apps that measure heart rate using pulse photoplethysmography (PPG) are classified as class II (moderate-risk) Food and Drug Administration devices; therefore, these devices need clinical validation prior to public release. The Auralife Instant Blood Pressure app (AuraLife IBP app) is an mHealth app that measures blood pressure inaccurately based on a previous validation study. Its ability to measure heart rate has not been previously reported. Objective: The objective of our study was to assess the accuracy and precision of the AuraLife IBP app in measuring heart rate. Methods: We enrolled 85 adults from ambulatory clinics. Two measurements were obtained using the AuraLife IBP app, and 2 other measurements were achieved with a oscillometric device. The order of devices was randomized. Accuracy was assessed by calculating the relative and absolute mean differences between heart rate measurements obtained using each AuraLife IBP app and an average of both standard heart rate measurements. Precision was assessed by calculating the relative and absolute mean differences between individual measurements in the pair for each device. Results: The relative and absolute mean (SD) differences between the devices were 1.1 (3.5) and 2.8 (2.4) beats per minute (BPM), respectively. Meanwhile, the within-device relative and absolute mean differences, respectively, were <0.1 (2.2) and 1.7 (1.4) BPM for the standard device and −0.1 (3.2) and 2.2 (2.3) BPM for the AuraLife IBP app. Conclusions: The AuraLife IBP app had a high degree of accuracy and precision in the measurement of heart rate. This supports the use of PPG technology in smartphones for monitoring resting heart rate.

  • JMIR Biomed Eng 2018;3(1):e11057

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  Biomedical Engineering Articles

  Oct 2, 2018

  Relationship Between the Applied Occlusal Load and the Size of Markings Produced Due to Occlusal Contact Using Dental Articulating Paper and T-Scan:...

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  • Shravya Reddy  
  • Preeti S Kumar  
  • Vyoma V Grandhi  

  Abstract:

  Background: The proposed experimental design was devised to determine whether a relationship exists between the occlusal load applied and the size of the markings produced from tooth contact when dental articulating paper and T-Scan are interposed alternatively. Objective: The objective of our study was to compare the relationship between contact markings on an articulating paper and T-Scan for an applied occlusal load. Methods: In this in vitro study, dentulous maxillary and mandibular dies were mounted on a metal jig and articulating paper and T-Scan sensor were placed alternatively between the casts. Loads simulating occlusal loads began at 25 N and incrementally continued up to 450 N. The resultant markings (180 marks resulting from articulating paper and 138 from T-Scan) were photographed, and the marks were analyzed using MOTIC image analysis and sketching software. Descriptive statistical analyses were performed using one-way analysis of variance, Student t test, and Pearson correlation coefficient method. Results: Statistical interpretation of the data indicated that with articulating paper, the mark area increased nonlinearly with increasing load and there was a false-positive result. The characteristics of the paper mark appearance did not describe the amount of occlusal load present on a given tooth. The contact marking obtained using T-Scan for an applied occlusal load indicated that the mark area increased with increase in the load and provided more predictable results of actual load content within the occlusal contact. Conclusions: The size of an articulating paper mark may not be a reliable predictor of the actual load content within the occlusal contact, whereas a T-Scan provides more predictable results of the actual load content within the occlusal contact.

  • JMIR Biomed Eng 2018;3(1):e11347

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  Biomedical Engineering Articles

  Feb 23, 2018

  Wireless Surface Electromyography and Skin Temperature Sensors for Biofeedback Treatment of Headache: Validation Study with Stationary Control Equipment

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  Authors List:

  • Anker Stubberud  
  • Petter Moe Omland  
  • Erling Tronvik  
  • Alexander Olsen  
  • Trond Sand  
  • Mattias Linde  

  Abstract:

  Background: The use of wearables and mobile phone apps in medicine is gaining attention. Biofeedback has the potential to exploit the recent advances in mobile health (mHealth) for the treatment of headaches. Objectives: The aim of this study was to assess the validity of selected wireless wearable health monitoring sensors (WHMS) for measuring surface electromyography (SEMG) and peripheral skin temperature in combination with a mobile phone app. This proof of concept will form the basis for developing innovative mHealth delivery of biofeedback treatment among young persons with primary headache. Methods: Sensors fulfilling the following predefined criteria were identified: wireless, small size, low weight, low cost, and simple to use. These sensors were connected to an app and used by 20 healthy volunteers. Validity was assessed through the agreement with simultaneous control measurements made with stationary neurophysiological equipment. The main variables were (1) trapezius muscle tension during different degrees of voluntary contraction and (2) voluntary increase in finger temperature. Data were statistically analyzed using Bland-Altman plots, intraclass correlation coefficient (ICC), and concordance correlation coefficient (CCC). Results: The app was programmed to receive data from the wireless sensors, process them, and feed them back to the user through a simple interface. Excellent agreement was found for the temperature sensor regarding increase in temperature (CCC .90; 95% CI 0.83-0.97). Excellent to fair agreement was found for the SEMG sensor. The ICC for the average of 3 repetitions during 4 different target levels ranged from .58 to .81. The wireless sensor showed consistency in muscle tension change during moderate muscle activity. Electrocardiography artifacts were avoided through right-sided use of the SEMG sensors. Participants evaluated the setup as usable and tolerable. Conclusions: This study confirmed the validity of wireless WHMS connected to a mobile phone for monitoring neurophysiological parameters of relevance for biofeedback therapy.

  • JMIR Biomed Eng 2018;3(1):e1

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  Biomedical Engineering Articles

  Dec 7, 2017

  Measurement of Skin Induration Size Using Smartphone Images and Photogrammetric Reconstruction: Pilot Study

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  Authors List:

  • Ronald Dendere  
  • Tinashe Mutsvangwa  
  • Rene Goliath  
  • Molebogeng X Rangaka  
  • Ibrahim Abubakar  
  • Tania S Douglas  

  Abstract:

  Background: The tuberculin skin test (TST) is the most common method for detecting latent tuberculosis infection (LTBI). The test requires that a patient return to the health facility or be visited by a health care worker 48 to 72 hours after the intradermal placement of tuberculin so that the size of the resulting skin induration, if any, can be measured. Objective: This study aimed to propose and evaluate an image-based method for measuring induration size from images captured using a smartphone camera. Methods: We imaged simulated skin indurations, ranging from 4.0 to 19 mm, in 10 subjects using a handheld smartphone, and performed three-dimensional reconstruction of the induration sites using photogrammetry software. An experienced TST reader measured the size of each induration using the standard clinical method. The experienced reader and an inexperienced observer both measured the size of each induration using the software. The agreement between measurements generated by the standard clinical and image-based methods was assessed using the intraclass correlation coefficient (ICC). Inter- and intraobserver agreement for the image-based method was similarly evaluated. Results: Results showed excellent agreement between the standard and image-based measurements performed by the experienced reader with an ICC value of .965. Inter- and intraobserver agreements were also excellent, indicating that experience in reading TSTs is not required with our proposed method. Conclusions: We conclude that the proposed smartphone image-based method is a potential alternative to standard induration size measurement and would enable remote data collection for LTBI screening.

  • JMIR Biomed Eng 2017;2(1):e3

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  Mobile Apps for Cardiology

  Aug 18, 2017

  Heart Rate Monitoring Apps: Information for Engineers and Researchers About the New European Medical Devices Regulation 2017/745

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  Authors List:

  • Michael Lang  

  Abstract:

  Background: After years in the making, on April 5, 2017, the European Parliament and Council finally adopted Regulation (EU) 2017/745, the new Medical Devices Regulation (MDR), repealing the existing Medical Device Directive (MDD) 93/42/EEC. Though long anticipated, this shift in policy will have strong and lasting effects in the medical devices industry. Objective: This paper focuses specifically on the classification of software as a potential medical device under MDD and MDR and examines whether or not the regulatory framework for health apps has changed substantially and what, if any, impact is to expected. A particular emphasis will be on the issue of classification uncertainty raised by borderline cases such as heart rate monitoring and well-being apps. The paper primarily targets researchers and engineers unfamiliar with regulatory requirements for medical devices and aims to provide a concise, yet accurate, overview of the European regulatory framework. This is of particular relevance as with the exponential growth of fitness and health-related apps, the lines between toys, lifestyle products, and medical devices have increasingly blurred. Methods: The recently published European Medical Device Regulation is analyzed and compared to the preceding MDD. Results: The previous regulatory framework already provided for the possibility of apps to fall under the definition of medical devices, in which case classification rules for active medical devices applied. However, while applicability of the new regulatory framework still hinges on whether the intended purpose is medical or not, the threshold for classifying as a medical device has been considerably lowered due to a broader interpretation of what constitutes a medical purpose. Conclusions: The adoption of the new European regulation on medical devices entails the risk that manufacturers previously unaffected by the medical devices regulatory framework may now unwillingly and unwittingly find themselves in the arena of medical device manufacturing.

  • JMIR Biomed Eng 2017;2(1):e2

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  Biomedical Engineering Articles

  Jun 26, 2017

  Motivations and Key Features for a Wearable Device for Continuous Monitoring of Breathing: A Web-Based Survey

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  • Joseph Barry Prinable  
  • Juliet M Foster  
  • Alistair L McEwan  
  • Paul M Young  
  • Euan Tovey  
  • Cindy Thamrin  

  Abstract:

  Background: Analysis of patterns of breathing over time may provide novel information on respiratory function and dysfunction. Devices that continuously record and analyze breathing rates may provide new options for the management of respiratory diseases. However, there is a lack of information about design characteristics that would make such devices user-friendly and suitable for this purpose. Objective: Our aim was to determine key device attributes and user requirements for a wearable device to be used for long-term monitoring of breathing. Methods: An online survey was conducted between June and July 2016. Participants were predominantly recruited via the Woolcock Institute of Medical Research database of volunteers, as well as staff and students. Information regarding the survey, a consent form, and a link to a Web-based questionnaire were sent to participants via email. All participants received an identical survey; those with doctor-diagnosed asthma completed an extra questionnaire on asthma control (Asthma Control Test). Survey responses were examined as a group using descriptive statistics. Responses were compared between those with and without asthma using the chi-square test. Results: The survey was completed by 134 participants (males: 39%, median age group: 50-59 years, asthma: 57%). Of those who completed the Asthma Control Test, 61% (47/77) had suboptimal asthma control. Of the 134 participants, 61.9% (83/134) would be willing to wear a device to monitor their breathing, in contrast to 6.7% (9/134) who would not. The remaining 31.3% (42/134) stated that their willingness depended on specific factors. Participants with asthma most commonly cited their asthma as motivation for using a wearable; the most common motivation for use in those without asthma was curiosity. More than 90% of total participants would use the device during the day, night, or both day and night. Design preferences among all users included a wrist watch (nominated by 92.5% [124/134] for both day and night use, out of four body sites), the ability to synchronize breathing data with a mobile phone or tablet (81.3%, 109/134), overnight power charging (33.6%, 45/134), and a cost of ≤Aus $100 (53.7%, 72/134). Conclusions: We have explored the motivations and likelihood for adopting wearable technologies for the purpose of monitoring breathing and identified user preferences for key design features. We found participants were motivated to adopt a wearable breathing monitor irrespective of health status, though rationale for use differed between those with and without asthma. These findings will help inform the design of a user-acceptable wearable device that will facilitate its eventual uptake in both healthy and asthma populations.

  • JMIR Biomed Eng 2017;2(1):e1

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  Biomedical Engineering Articles

  Oct 17, 2016

  A Six-Step Framework on Biomedical Signal Analysis for Tackling Noncommunicable Diseases: Current and Future Perspectives

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  Authors List:

  • Mohamed Elgendi  
  • Newton Howard  
  • Nigel Lovell  
  • Andrzej Cichocki  
  • Matt Brearley  
  • Derek Abbott  
  • Ian Adatia  

  Abstract:

  Low- and middle-income countries (LMICs) continue to face major challenges in providing high-quality and universally accessible health care. Researchers, policy makers, donors, and program implementers consistently strive to develop and provide innovative approaches to eliminate geographical and financial barriers to health care access. Recently, interest has increased in using mobile health (mHealth) as a potential solution to overcome barriers to improving health care in LMICs. Moreover, with use increasing and cost decreasing for mobile phones and Internet, mHealth solutions are becoming considerably more promising and efficient. As part of mHealth solutions, biomedical signals collection and processing may play a major role in improving global health care. Information extracted from biomedical signals might increase diagnostic precision while augmenting the robustness of health care workers’ clinical decision making. This paper presents a high-level framework using biomedical signal processing (BSP) for tackling diagnosis of noncommunicable diseases, especially in LMICs. Researchers can consider each of these elements during the research and design of BSP-based devices, enabling them to elevate their work to a level that extends beyond the scope of a particular application and use. This paper includes technical examples to emphasize the applicability of the proposed framework, which is relevant to a wide variety of stakeholders, including researchers, policy makers, clinicians, computer scientists, and engineers.

  • JMIR Biomed Eng 2016;1(1):e1

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