Posts Tagged medical technology

[WEB SITE] Flint Rehab Introduces MiGo Wearable for Stroke Recovery

MiGo

Flint Rehab announces the launch of MiGo, a wearable activity tracker specifically designed for stroke survivors. The device makes its official debut at the 2019 Consumer Electronics Show in Las Vegas.

MiGo is designed to track upper extremity activity — in addition to walking — and is optimized for the movement patterns performed by individuals with stroke. The device is accompanied by a smartphone app that provides motivational support through digital coaching, progressive goal setting, and social networking with other stroke survivors, according to the company in a media release.

“Most wearable fitness trackers are designed to help people get into shape. MiGo is a new type of wearable that helps people regain their independence after a stroke,” says Dr Nizan Friedman, co-founder and CEO of Irvine, Calif-based Flint Rehab, in the release.

“Traditionally, innovation in medical technology has been limited by what insurance companies are willing to cover. As a consumer-level digital health technology, MiGo avoids these constraints, empowering stroke survivors to take their recovery into their own hands.”

A common outcome of stroke is hemiparesis, or impaired movement on one side of the body. One of the leading causes of this lifelong disability is a phenomenon called “learned non-use,” where stroke survivors neglect to use their impaired arm or leg, causing their brain to lose the ability to control those limbs altogether.

MiGo directly addresses the problem of learned non-use by motivating stroke survivors to use their impaired side as much as possible. Using deep-learning algorithms, MiGo accurately tracks how much the wearer is using their impaired side, providing them with an easy-to-understand rep count throughout the day.

MiGo also provides an intelligent activity goal that updates every day based on the wearer’s actual movement ability, ensuring every user stays continuously challenged at the level appropriate for them. Then, the device acts as the wearer’s personal cheerleader, giving them rewards and positive feedback right on their wrist as they work to hit their daily goal, the release explains.

“Suffering a stroke is a traumatic, life-changing event. Many survivors do not have the proper support network to deal with the event, and they may find it difficult to relate with friends and family who don’t understand what they are going through,” states Dan Zondervan, co-founder and vice president of Flint Rehab.

“Using the MiGo app, users can join groups to share their activity data and collaborate with other stroke survivors to achieve group goals. Group members can also share their experiences and offer encouraging support to each other — right in the app,” he adds.

For more information, visit Flint Rehab.

[Source(s0): Flint Rehab, Business Wire]

 

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[WEB SITE] New epilepsy warning device detects more seizures than current methods – Video

epilepsy warning

A recent study developed and evaluated the effectiveness of a new device to detect epileptic seizures and act as an epilepsy warning.

Despite receiving medication, approximately 30% of people with epilepsy continue to have seizures. Seizures occurring during sleep can be especially dangerous and are often unwitnessed. These epileptic patients are also at high risk for sudden unexpected death in epilepsy (SUDEP), a major cause of death in epileptic patients. Also, patients with an intellectual disability and severe therapy-resistant epilepsy have an estimated 20% lifetime risk of dying from epilepsy. Therefore, the development of an epilepsy warning device could help not only improve the quality of care for patients but also help prevent SUDEP.

One of the current techniques to monitor epilepsy at night is a sensor that reacts to vibrations from rhythmic jerks. Despite techniques, many seizures are still being missed. However, a new device developed by scientists from the Netherlands may be the solution to help reduce the number of epileptic patient deaths occurring during the night. This new high-tech device is a bracelet known as Nightwatch.

Bracelet recognizes two warning signs of severe seizures

The researchers developed the bracelet to recognize two important warnings signs of severe epileptic seizures: an abnormally fast heartbeat and rhythmic jolting movements. When these seizure warnings are detected the bracelet will send an alert to the patient’s caretaker or nurse.

The researchers recently conducted a prospective trial to test the bracelet in 28 intellectually handicapped epilepsy patients. They observed the patients wearing the bracelet for an average of 65 nights. In the event of a severe seizure, the bracelet sounded an alarm. Patients were also filmed to see whether any false alarms occurred or if the bracelet missed any attacks. The results of the trial were recently published in Neurology.

The new technology performed better than current methods 

The bracelet was compared to bed sensors, which are the current standard detection method. The findings showed that the bed sensor only detected 21% of serious attacks and on average remained silent once every four nights per patient. In comparison, the Nightwatch bracelet only missed a serious attack every 25 nights (on average) per patient.

The comparison showed the bracelet detected 85% of all serious night-time epilepsy seizures and 96% of the most severe ones. These results show combining patterns that can trigger seizures, such as heart rate and movement, is a reliable method of detection for night-time seizures.  The care staff of patients during the study reported being positive about the use of the bracelet and patients did not experience discomfort from wearing the bracelet at night.

The researchers expect through the use of the bracelet, the number of SUDEP cases may be reduced by two-thirds and applied globally, this epilepsy warning device could save thousands of lives. However, saving the life of the patient also depends on how quickly caretakers or nurses respond to the alarm. The Nightwatch is now available and can be used by adults at home and in institutions.

Written by Lacey Hizartzidis, PhD

References:

  1. Johan Arends, Roland D. Thijs, Thea Gutter, Constantin Ungureanu, Pierre Cluitmans, Johannes Van Dijk, Judith van Andel, Francis Tan, Al de Weerd, Ben Vledder, Wytske Hofstra, Richard Lazeron, Ghislaine van Thiel, Kit C.B. Roes, Frans Leijten, and the Dutch Tele-Epilepsy Consortium. Multimodal nocturnal seizure detection in a residential care setting. Neurology Nov 2018, 91 (21) e2010-e2019; DOI:10.1212/WNL.0000000000006545.
  2. New epilepsy warning device could save thousands of lives. EurekAlert website https://www.eurekalert.org/pub_releases/2018-11/euot-new110518.php. Accessed January 12, 2019.
  3. Photo credit: LivAssured https://www.eurekalert.org/multimedia/pub/185082.php?from=411220

Disclaimer: Not a sponsored post.

via New epilepsy warning device detects more seizures than current methods – Medical News Bulletin | Health News and Medical Research

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[CES 2019] Medical Technology Making Inroads at CES – Augmented reality virtual caregiver, Wearable fitness tracker and Restoring Balance

Although TVs and other consumer electronic gadgets continue to occupy center stage at the Consumer Electronics Show, this year’s showcase in Las Vegas will also feature a number of products and technologies in the healthcare area. While consumer-oriented products such as Fitbit immediately come to mind, many of the technological innovations at CES combine hardware and artificial intelligence (AI) to monitor personal health and in some cases aid their recovery from diseases or falls.

One interesting technology is Addison Care™, an augmented reality virtual caregiver from SameDay Security Inc. that engages aging and chronically ill clients throughout the home to supplement their care and provide various health and safety features. Designed to appear on 15-in. monitors, Addison Care™ (Figure 1) carries on live two-way conversations with users, monitoring their activities around the clock.

 

Flint Rehab, a neuro-rehabilitation device company, is showing its MiGo wearable fitness tracker (Figure 2) . The device is reportedly the first commercially available wearable activity tracker specifically designed for stroke survivors. MiGo tracks upper extremity activity—in addition to walking—and is optimized for the movement patterns performed by individuals with stroke. The device is accompanied by a smartphone app that provides motivational support through digital coaching, progressive goal setting, and social networking with other stroke survivors.

According to the company, the device uses deep learning algorithms to measure the amount of “learned non-use,” where stroke survivors neglect to use their impaired arm or leg, causing their brain to lose the ability to control those limbs altogether. To speed recovery, the device encourages patients to use their impaired limbs every day, enabling them to regain their lost abilities over time. It provides them with an easy-to-understand rep count throughout the day and sets an intelligent activity goal that updates every day based on the wearer’s actual movement ability, Patients are encouraged and rewarded for meeting goals.

 

Figure 2: The MiGo wearable fitness tracker helps stroke survivors regain use of impaired body limbs. Image Source: Flint Rehab

Restoring Balance

Scale-1 Portal is unveiling MoveR, an applications for treating balance disorders. Designed for vestibular rehabilitation therapy, the technology transports patients in virtual scenarios controlled live by a health care professional (see video). It gives patients an immersive experience without any headset and without carrying a motion capture device. MoveR offers two experiences immersing the user in a virtual environment with only a pair of 3D glasses.

Using a touch screen with a simple and clear interface, the health care professional can directly control these scenarios in order to adapt them to the patient. Dedicated to reducing visual dependence in disorders of the balancing system, the two experiments will generate a sensory conflict in order to make greater use of somesthesia and the vestibular system.

One of these immersive experiences also encourages the user to perform movements in response to the physician’s choices. This means, for example, trying to catch virtual objects or avoid obstacles in a scrolling path.

 

 

AerBetic Inc. is demonstrating a non-invasive, wearable diabetes alert system containing nanosensors that detect gases, given off through breath or skin, that are symptomatic of high or low blood sugar. The device will pair with smartphone apps, aiding the ability to push alerts to patients and caregivers.

According to AerBetic CEO Arnar Thors, the device was inspired by his family pet, a yellow Labrador retriever. The sensors will use patient data and feedback to improve and fine tune over time, Thors says, using machine learning and artificial intelligence to increase fidelity at the individual user level and network-wide.

The device is in the final stages of development, with testing slated to begin the first quarter of this year.

 

via Medical Technology Making Inroads at CES

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[ARTICLE] Identifying and Quantifying Neurological Disability via Smartphone – Full Text

Embedded sensors of the smartphones offer opportunities for granular, patient-autonomous measurements of neurological dysfunctions for disease identification, management, and for drug development. We hypothesized that aggregating data from two simple smartphone tests of fine finger movements with differing contribution of specific neurological domains (i.e., strength & cerebellar functions, vision, and reaction time) will allow establishment of secondary outcomes that reflect domain-specific deficit. This hypothesis was tested by assessing correlations of smartphone-derived outcomes with relevant parts of neurological examination in multiple sclerosis (MS) patients. We developed MS test suite on Android platform, consisting of several simple functional tests. This paper compares cross-sectional and longitudinal performance of Finger tapping and Balloon popping tests by 76 MS patients and 19 healthy volunteers (HV). The primary outcomes of smartphone tests, the average number of taps (per two 10-s intervals) and the average number of pops (per two 26-s intervals) differentiated MS from HV with similar power to traditional, investigator-administered test of fine finger movements, 9-hole peg test (9HPT). Additionally, the secondary outcomes identified patients with predominant cerebellar dysfunction, motor fatigue and poor eye-hand coordination and/or reaction time, as evidenced by significant correlations between these derived outcomes and relevant parts of neurological examination. The intra-individual variance in longitudinal sampling was low. In the time necessary for performing 9HPT, smartphone tests provide much richer and reliable measurements of several distinct neurological functions. These data suggest that combing more creatively-construed smartphone apps may one day recreate the entire neurological examination.

Introduction

Neurological examination measures diverse functions of the central (CNS) and peripheral nervous systems to diagnose neurological diseases and guide treatment decisions. Thorough neurological examination takes between 30 and 60 min to complete and years of training to master. This poses problem both for developing countries, which often lack neurologists, and for developed countries where cost-hikes and administrative requirements severely limit the time clinicians spend examining patients.

Additionally, clinical scales derived from traditional neurological examination are rather insensitive and prone to biases, which limits their utility in drug development. Therefore, non-clinician administered measurements of physical disability such as timed 25-foot walk (25FW) and 9-hole peg test (9HPT) or measurements of cognitive functions exemplified by paced auditory serial addition test (PASAT) and symbol digit modalities test (SDMT), are frequently used in clinical trials of neurological diseases such as multiple sclerosis (MS) (12). Especially combining these “functional scales” with clinician-based disability scales such as Expanded Disability Status Scale (EDSS)(3) into EDSS-plus (4) or Combinatorial weight-adjusted disability scale (CombiWISE) (5) enhances sensitivity of clinical trial outcomes. However, these sensitive combinatorial scales are rarely, if ever acquired in clinical practice due to time and expense constrains.

Measuring neurological functions by patients via smartphones (68) may pose a solution for all aforementioned problems, while additionally empowering patients for greater participation in their neurological care. We have previously found comparable sensitivity and specificity of simple, smartphone-amenable measurements of finger and foot taps to 9HPT and 25FW, respectively (9). In this study, we explored iterative development/optimization of smartphone-based measurements of neurological functions by: 1. Exploring clinical utility of new features that can be extracted from finger tapping; 2. Development of “balloon popping” smartphone test that builds on finger tapping by expanding neurological functions necessary for task completion to eye movements and cognitive skills, and 3. By decoding app-collected raw data into secondary (derived) features that may better reflect deficits in specific neurological functions.

 

Materials and Methods

Developing the Smartphone Apps

Tapping and Balloon popping tests were written using Java in the Android Studio integrated development environment. Both tests went through iterative development and optimization following beta testing with developers and then clinical trial testing with patients and healthy volunteers. Each of the individual tests are standalone applications and can be downloaded individually to the phone using an Android Package (APK) emailed to phones or directly installed through USB connection with Android Studio. Installation and initial testing of applications were completed on a variety of personal Android phones, with no particular specifications. Testing in the clinic with patients and longitudinal testing was completed on Google Pixel XL 2017 phones. Android 8.1 Oreo operating system was used for the most recent version of the application, with the intention of keeping the operating system the app runs on up to date with the most recent version released by Android.

For the purposes of this study, we created a front-end application that can flexibly incorporate a variety of test apps. The front-end prompts for user profiles where a testing ID, birth month and year, gender, and dominant hand may be entered so data collected is associated with the user profile. Through a cloud-based spreadsheet, “prescriptions” of test app configurations are set for each user such that they may have a unique combination of tests tailored to their disability level.

The tapping test goal was similar to previously validated non-smartphone administered tapping tests (9), where users had to tap as quickly as possible over a 10 s duration and the final score is the average of two attempts. The test uses touch recognition over a rectangular area covering the bottom half of a vertically oriented phone screen (Figure 1A). Users can tap anywhere in a marked off gray area. The total number of taps for each of two trials and the calculated average is displayed immediately afterwards on the screen. In addition to total taps over the duration of the test, the app also records the duration, Android system time, and pressure for each tap as background data. Pressure for app recording is interpreted from the size of the touch area on each tap, where larger tap area corresponds to a higher pressure reading. Because the pressure function was added later and therefore the data are missing for the majority of current cohort, this function is not investigated in current study.

FIGURE 1
www.frontiersin.orgFigure 1. Smartphone Apps. (A) Tapping Test where user can tap repeatedly anywhere in the gray rectangle over the bottom half of the screen. (B) Popping Test where the dark blue circle will disappear and simultaneously reappear randomly across the screen as soon as the user touches it.

The balloon popping test was conceptually envisioned as an extension of tapping test that expands neurological functions necessary for test completion from pure motoric, to motoric, visual, and cognitive (attention and reaction time). The primary goal for this test is to touch as many randomly generated dark blue circles (balloons) moving across the screen in succession over the 26-s test duration as possible. During optimization of the app we tested 3 sizes of the target balloon and a 100-pixel balloon was selected as optimal based on preliminary results. The analyses of the other two circle sizes are provided as part of sensitivity analyses (Supplementary Figure 1), as conclusions from these tests support data presented in the main text of the paper. There is only one balloon to pop on the screen at a time (Figure 1B) and as soon as the user touches anywhere on the circle, another circle will appear in a random location. The random generation of balloon locations was created by random number functions in Java for both the x and y coordinates of the center of the circle, with the constraint of the entire balloon having to be visible on the screen. If the user taps on a background location, the current balloon stays in the same location and is only moved to a new random location after accurately tapping on the balloon. Following app completion, the total number of balloons popped and calculated average (from two trials) is displayed on the phone for the user. The x and y coordinates of all balloon and background hits, the system time, duration, and pressure (in the same manner as tap pressure) for each tap are also recorded as background data and stored in cloud-based data system.

Following the completion of a tapping or balloon popping test trial, an intermediate message displayed on the screen asks if the users would like to submit their results or retake the most recent trial (Supplementary Videos 12). If the user selects the retake option the collected data for the trial is discarded locally on the phone and not sent to any cloud-based database. This was implemented to avoid noise associated with test interruptions or other unforeseen circumstances that affected test performance. Following selection of the submit option, the data is uploaded immediately to a cloud-based database if the smartphone is connected to WiFi. If the phone is not connected to WiFi, then the submitted test trial results are stored locally on the phone and uploaded to the database as soon as the phone is connected to WiFi.

The app development process is in continuation given user and clinician feedback in addition to integration of more tests into the front-end. User feedback, user’s ability to perform Apps in a “practice mode”, and training videos for individual tests (Supplementary Videos 12) are integrated into the front-end dashboard that manages different tests.[…]

Continue —->  Frontiers | Identifying and Quantifying Neurological Disability via Smartphone | Neurology

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[WEB SITE] What Modern Day Challenges Affect Epilepsy Treatment?

epilepsy

Researchers recently published an article in The Lancet Neurology discussing the difficulties facing seizure detection in patients with epilepsy.

Epilepsy is a neurological disorder that is characterised by short repetitive epileptic seizures.  These seizures can be harmful to the individual depending on the circumstances in which they occur, such as a seizure while driving. This disorder is set apart from other neurological disorders since there is a broad range of different physiological changes that can cause it, leading to a large variation in symptoms and making it difficult to treat. While 70% of sufferers can be treated with pharmacological agents, 30% have no reliable anti-epileptic drugs that are effective for their particular type of epilepsy.

In a recent study, Christian Elger and Christian Hoppe determined that a key challenge facing patients is that over 50% of patients under-report the number of seizures they experience, which has a serious impact on how well doctors are able to determine what treatments are most suitable for them. This also calls into question many of the previously published research on epilepsy treatments. They recently published this report in The Lancet Neurology.

Why are Epileptic Seizures Difficult to Detect?

In this personal view, the writers determined that the cause of under-reporting is primarily due to patients, or their caregivers, being unable to identify when seizures are occurring. Seizures can impair consciousness, may occur at night, or the physical symptoms may be so subtle that they are not easily noticed unless professionally trained to do so.

Technologies for Epilepsy DetectionThe gold standard for epilepsy detection is video-electroencephalography (VEEG), where patients have their brain activity monitored for epilepsy-specific activity and trained technicians can test for impairments to consciousness, cognition, language, and memory. Video footage from the VEEG can also be viewed at a later time to spot slight body movements indicative of a seizure. The limitation of this method is that it requires a hospital visit, increasing associated costs, and is only suitable for identifying how frequent a person has a seizure over a given time, it does not address the issue of a person (or their caregiver) being aware they are having a seizure in real time.

Automated System Required

It is clear that the future of seizure detection requires an automated system,  preferably one that patients can wear over the long-term and that will notify them or a nearby center when a seizure occurs. The main barriers to this technology is that a number of the current ambulatory systems for monitoring brain activity can be limited in monitoring time (72 hours) or require labor-intensive analysis of data, although as algorithms for analyzing brain activity improve this limitation will also decrease.

An analysis of movement via home-based video systems, or of various physical data outputs (i.e. accelerometry, magnetometry, gyroscopy, or pressure data) derived from worn sensors have some promise but so far the results have not been consistent.

Surface Electromyography

It appears that one of the most promising methods, which is also viewed favorably by patients, is surface electromyography (SEMG). This method involves self-adhesive sensors that are attached to muscles in areas of the body affected by seizures. Furthermore, multi-modal approaches that combine SEMG, EEG, and electrocardiography have a detection rate over 85% for the majority of seizure types.

Improved Seizure Detection Necessary

It is clear that improved seizure detection is necessary for ensuring that doctors provide the most appropriate treatment to individual patients, as well as ensuring that patients are protected from life-threatening seizures. Improving wearable, ambulatory technologies and advancements in algorithms for the analysis of seizure data will help provide comprehensive support to both physicians and to the patients that they monitor.

Written by Michael Healy, BSc, MSc

Reference: Elger C.E., Hoppe C. Diagnostic challenges in epilepsy: seizure under-reporting and seizure detection. Lancet Neurol 2018; 17: 279–88.

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[WEB SITE] Can MRI Brain Scans Help Us Understand Epilepsy?

epilepsy

A massive meta-analysis of global MRI imaging data on epilepsy patients seeks to clarify a complicated and mysterious neurological disorder.

Epilepsy is a neurological disorder characterized by seizures, which can vary from mild and almost undetectable to severe, featuring vigorous shaking. Almost 40 million people worldwide are affected by epilepsy. Epileptic seizures are caused by an abnormally high level of activity in nerve cells in the brain. A small number of cases have been tied to a genetic defect, and major trauma to the brain (such as an injury or stroke) can also induce seizures. However, for the majority of cases, the underlying cause of epilepsy is not known. In many instances, epilepsy can be treated with the use of anti-convulsant medication. Some people will experience an improvement in their symptoms to the point of no longer requiring medication, while others will not respond to medication at all. The variability of the disease with regards to physiology and progression makes it difficult to accurately diagnose.

How Does Epilepsy Affect the Brain?

There are multiple types of epilepsies, some more common than others, which affect different parts of the brain cortex. The disorder has been studied by using techniques such as magnetic resonance imaging (MRI), and analyses of brain tissue. The latter requires post-mortem collection of tissue, as biopsies are not routinely performed on living patients’ brains. A brain scan via MRI imaging can provide detail about pathological markers of epilepsy, but the massive amount of data collected worldwide by imaging has not yet been consolidated and analyzed in a robust manner. Gaining an understanding of distinct or shared disease markers for different forms of epilepsy could help clinicians identify targets for therapy and increase the personalization of treatment.

The ENIGMA Study

A recent study published in the journal BRAIN represents the largest neuroimaging analysis of epilepsy conducted to date.This study, called ENIGMA (Enhancing Neuro Imaging Genetics through Meta-Analysis)summarizes contributions from 24 research centers across 14 countries in Europe, North and South America, Asia, and Australia. Similar wide-ranging studies have revealed structural brain abnormalities in other neurological conditions such as schizophrenia, depression, and obsessive-compulsive disorder. The researchers had several goals in putting this meta-analysis together:

  1. To look at distinct types of epilepsy to see whether they share similar structural abnormalities of the brain.
  2. To analyze a well-known specific type of epilepsy, mesial temporal lobe epilepsy (MTLE) for differences between people afflicted with this disorder on different sides of the brain.
  3. To analyze idiopathic generalized epilepsies (IGE), which are thought to have a genetic component to their cause and aren’t often detectable via MRI.

The researchers compiled imaging data from 2,149 people with epilepsy and 1,727 healthy control subjects. The large sample size allowed them to perform high-powered statistical analysis of the data.

For analysis (1), the results showed that a diverse array of epilepsies showed common structural anomalies across several different regions of the brain. This suggested that distinct disease types share a common neuroanatomical signature.

For analysis (2), they found that people with mesial temporal lobe epilepsy on the right side of the hippocampus did not experience damage to the left side, and vice-versa. However, somewhat unexpectedly, they saw that damage extended to areas outside the hippocampus, suggesting that even a region-specific disorder like mesial temporal lobe epilepsy may be a network disease.

In analysis (3), the researchers found that contrary to many reports of a “normal” MRI for patients with idiopathic generalized epilepsy, several structural irregularities were observable over a large number of samples. These included reduced brain volume and thickness in several regions.

One Step Closer to Understanding Epilepsy

The authors noted some limitations to their study, such as the fact that all results were derived from cross-sectional data, meaning that it was not possible to determine whether certain features were the cause of severe brain damage at one point in time, or whether they were the product of progressive trauma. In addition, this study could not account for the possible contribution of other factors, such as medications, seizure type and frequency, and disease severity. However, this wide-scale meta-analysis represents an important step towards understanding how different types of epilepsies affect the brain, and hopefully can lead to more personalized and effective medical interventions.

Written by Adriano Vissa, PhD

Reference: Whelan CD, et al. Structural brain abnormalities in the common epilepsies assessed in a worldwide ENIGMA study. Brain. 2018; 141(2):391-408

 

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