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Smart wristband developed to identify and manage atrial fibrillation

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It’s one of the most common conditions affecting those over 65 and left untreated can lead to stroke, blood clots in the veins and, in the most extreme cases, heart failure.

Atrial fibrillation currently affects more than 40 million people worldwide and the incidence and prevalence of the medical condition have increased three-fold in the past 50 years as populations age and survival rates for chronic diseases increase.

Now thought of as a global epidemic, 16 million people in the United States alone are projected to have been diagnosed with the ailment by 2050. In Europe, the figure among the over 55s is expected to reach 14 million by 2060.

It is estimated that by 2050, AF will be diagnosed in at least 72 million individuals in Asia.

One of the most common symptoms of AF is a pounding, fluttering, or quivering heartbeat, more commonly known as heart palpitations. Other signs include dizziness, fatigue, a fast heart rate of more than 100 beats per minute, breathlessness, and chest pain – many of the classic stress or anxiety signs that characterise a panic attack.

It’s one of the reasons that millions of people are walking around unaware that they are suffering from atrial fibrillation. How many times have you heard someone attribute their racing heartbeat to a caffeine-induced surge brought about by having drunk one too many coffees?

Many more are asymptomatic, meaning they are producing and showing no symptoms at all.

Often the condition will only be picked up when a patient undergoes a health check for an unrelated matter.

However, early detection and treatment of AF are paramount if later complications are to be avoided.

Without treatment, people with AF are up to five times more likely to suffer strokes, leading to the risk of severe disability and even premature death.

But new patient-safe monitoring technology to check and manage individual factors provoking atrial fibrillation, has been invented by Lithuanian researchers that could hold the key to earlier diagnosis and outcomes for the potentially serious heart condition.

A smart wrist-worn bracelet has been developed by Lithuanian scientists to identify atrial fibrillation. Credit: KTU

It involves patients wearing a so-called smart bracelet – already an accepted accessory for many – that uses an algorithm that can detect atrial fibrillation.

Traditional methods of diagnosing AF involve patients having to wear intrusive and uncomfortable sensors. But this new technology incorporates complementary sensors and a signal processing algorithm, with patients also being asked to input potential arrhythmia triggers on a mobile app.

The device is the result of a successful collaboration between the Kaunas University of Technology Biomedical Engineering Institute (KTU BMEI) and Vilnius University’s Santaros Clinics.

Researchers at KTU BMEI have been working in the field of atrial fibrillation monitoring technology development for more than a decade. It was several years ago that they developed the bracelet – the patent application for the device was submitted to the Lithuanian State Patent Bureau at the end of 2018 – which is aimed at older people, who can be especially self-conscious when using technologies and smart devices.

Professor Vaidotas Marozas, director of KTU BMEI, told Agetech World: “We are focusing on developing technologies which are needed for the public and contemporary medicine. For example, due to the prevalence of this condition (AF), every person older than 65 should be checked for atrial fibrillation.

“Non-invasive, compact wearable devices are an attractive solution for monitoring the health status of such high-risk groups.”

The disease usually starts with self-terminating so-called ‘paroxysmal episodes’ which, if recognised in time, can be treated by non-medication means.

These episodes may be different for each patient, however. For some, they may last for a short time and recur infrequently. For others, the episodes can be longer and more frequent.

But untreated AF will eventually develop into a persistent condition, which is more complicated to treat.

The smart wristband developed by Lithuanian scientists. Credit: KTU

The KTU-developed smart bracelet – which Lithuanian company, Teltonika, has stepped in to produce – has been used together with other devices in the TriggersAF project supported by the European Regional Development Fund.

The aim of the project coordinated by the Kaunas University of Technology in partnership with Vilnius University, is to develop and test methods that allow patients to identify their individual arrhythmia triggers via a wrist-wearing device.

It is already known that for some patients, atrial fibrillation episodes can be provoked by certain modifiable factors, such as alcohol, increased physical activity, stress, and sleep disturbance.

Identifying and avoiding individual factors would help determine non-pharmaceutical intervention methods to arrhythmia management.

As the project addresses a clinical problem, it has been important to have on board experienced clinicians who deal with AF daily. One of them is Justinas Bacevičius, a cardiologist at VU Hospital Santaros Clinics.

He said: “Although we see a wide variety of atrial fibrillation patients in our hospital, two types can be distinguished. The first group includes older, overweight, diabetic, hypertensive patients or those having sleep apnoea.

“The second group is the complete opposite – often they are young, professional sportspersons, businesspeople or performers who are experiencing a lot of stress.”

Mr Bacevičius said the data from the patients suggests a link between the onset of arrhythmia and sleep disorders.

He added that interestingly, even in patients who are not diagnosed with sleep apnoea, a correlation between snoring during sleep and the onset of atrial fibrillation in the morning, or later in the day, had been identified.

But with no objective methods to identify individual factors influencing the arrythmia in patients, KTU BMEI researchers in collaboration with cardiologists from VU Hospital Santara Clinics and their long-term partner Leif Sörnmo from Lund University in Sweden, have proposed one.

It assumes that arrythmia parameters, such as the relative duration of an episode, increase after an arrythmia-provoking factor.

Vilma Pluščiauskaitė, a PhD student at KTU and a junior researcher on the project, explained: “The essence of our proposed approach is that the patient uses a wearable bio signal-recording device for a set monitoring period, e.g. two weeks, and enters potential triggers for atrial fibrillation into a mobile app.

“For the next two weeks, the patient avoids the identified potential triggers, and the relation is assessed by an equation proposed by KTU BMEI researcher Dr Andrius Petrėnas.

“If a correlation between the influencing factor and the occurrence of arrhythmia is detected, the patient is advised to avoid the specific identified factor.”

The project’s database is the first of its kind in the world. It includes the recorded patients’ physiological signals, such as electrocardiogram and photoplethysmogram (a simple and low-cost technique that sends light pulses through the skin into the blood vessels to detect blood volume changes), and potential arrythmia provoking factors entered in a person’s mobile app.

The database collected by the researchers has allowed them to test the developed method and identify arrythmia-provoking factors in individual patients.

Professor Vaidotas Marozas. Credit: KTU

Project leader, Professor Marozas, is understandably delighted with its success, which will allow further development of the smart bracelet technology.

He said: “The database generated by the project is a unique result. We have managed to interest an international consortium funded by the European Metrology Association in this data. This consortium has invited us to join their new project as a partner and we will continue our work.”

The lack of technology currently available to individually identify arrythmia-provoking factors is probably due to the fact that monitoring has traditionally been inconvenient. Patients usually have to have an electrocardiogram (ECG), which is an electrical recording of their heart rhythm.

If that doesn’t identify a problem, then further monitoring will be needed, involving having to wear a portable ECG recording device for 24 hours or more.

Patients may also be required to fill in numerous questionnaires to pinpoint trigger factors, which can be subject to recall bias, where they either forget about a potential arrhythmia provoking stimulus or are reluctant to acknowledge the presence of certain influences, such as alcohol intake.

Mr Pluščiauskaitė said: “Certain influencing factors for arrythmia, such as increased exercise, stress, or sleep disturbances, can be identified from physiological signals by the dedicated algorithms. However, other influencing factors, such as alcohol consumption, are difficult to identify in the signals, so it is best if the patient has the opportunity to indicate when he or she consumed alcohol.”

He added that it is hoped that in the future, identifying these arrythmia triggers will only require a smart bracelet incorporating complementary sensors and signal processing algorithm.

 

 

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Opinion: How robots could help to ease the social care crisis

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Robear is an animatronic bear that lifts elders with mobility problems, Paro is a fuzzy robotic seal intended to provide a futuristic form of animal therapy, and Pepper is a humanoid with remote-monitoring capabilities and learning tools.

Pet robots have revolutionised and enhanced the standard of care, improving the wellbeing of care home residents. Here Stephen Hayes, managing director at automation tech firm Beckhoff UK, explores the benefits of integrating robots in social care homes.

There is a wide variety of care robots on the market. Some are aimed at physical care, including machines that can assist with mobility and exercise, feed their owner and help them with hygiene tasks. These could greatly benefit caregivers, freeing their time and preventing them from suffering from long-term health conditions or disability due to the physical effort associated with giving care.

Others play the role of a companion, engaging older people emotionally to reduce and even prevent cognitive decline, providing companionship for lonely older people, and making those with cognitive conditions easier for care staff to manage.

Research by the University of Plymouth, conducted in care homes using these pets found decreased neuropsychiatric symptoms such as delusions, depression, anxiety, apathy and occupational disruptiveness because they provided a sense of responsibility and purpose.

Social care vacancies are higher than before the COVID-19 pandemic, and data shows one in ten social care posts are unfilled in a staffing crisis that could have harmful results for residents. In England, 152,000 social care posts remain empty, according to a report released by Skills for Care.

Steve Barclay, Secretary of State for Health and Social Care commented for The Telegraph earlier this year, stating that robots and AI are key to better supporting patients and reducing demand on social care staff. He said that there was a need to adopt an innovative approach to health and attempt to cut NHS waiting times while improving care for the elderly.

However, these robots present limitations, such as superficiality and lack of personalisation. Also, the content of their conversations can be very limited, making them less entertaining with the pass of time.

This was the case of the humanoid Pepper, for which production ceased in 2021 due to a weak demand as care homes did not see the long-term benefit of his interactions. Nevertheless, robots like Paro, which move and respond to touch, have had a positive impact on the wellbeing of care home residents. However, with a cost of £5,000, care homes are looking for more affordable options.

Japan is a pioneer in developing this kind of technology. The nation is facing a ‘greying’ crisis due to the aging of its population, so the country has invested heavily in developing caretech able to serve and provide emotional support.

In the UK, there are currently almost 12 million people who are aged 65 or over, and the number of people coping with illnesses such as arthritis or dementia is expected to increase. In fact, a recent machine learning study by the Journal of Medical Systems suggests that 135 million people might be affected by dementia by 2050.

To allow for continuous innovation in this field, the UK Government announced its commitment to invest at least 2.4 per cent of GDP in R&D by 2027. The programme supports the UK Government’s Ageing Society Grand Challenge and Future of Mobility Grand Challenge, which will ensure we meet the needs of an ageing society.

With this in mind, there are certain technologies that we are likely to see more of in care homes over the coming years, including robots that can connect to each other and other devices.

This includes devices such as oximeters, thermometers, or even thermal cameras, enabling the elderly to have consultations any time of the day, from home, and send out emergency notices to staff or hospitals. However, for care robots to be a success, state-of-the-art control technology is required.

Beckhoff’s Ethernet-based fieldbus system, EtherCAT, has extension modules that are compatible with third party hardware for integration. This platform process data and transports it directly, has a flexible topology and simple line or tree structure that requires no expensive infrastructure components and includes the environments for programming, diagnostics and configuration.

This global standard for real-time Ethernet communication provides workers with real-time information about elders like location, health condition, or learning progress. This data also allows carers focus their time on other urgent tasks, optimise resources and personalise treatment.

The advancement and implementation of robot pets could improve awareness of preventative care, reduce anxiety on disease and enhance stakeholder relationship.

Further research on caretech would tackle functional problems, making these devices an essential asset for any caregiver. By investing in the right control technology now, social care homes will be better prepared to take care of their residents.

Beckhoff provides PC-based control and EtherCAT to connect caretech systems. See more here.

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New AI program could predict likelihood of Alzheimer’s disease

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By analysing speech patterns, a new machine learning model can predict with a high degree of accuracy whether someone with mild cognitive impairment will develop Alzheimer’s-associated dementia within six years.

Trying to figure out whether someone has Alzheimer’s disease usually involves a battery of assessments—interviews, brain imaging, blood and cerebrospinal fluid tests. But, by then, it’s probably already too late: memories have started slipping away, long established personality traits have begun subtly shifting.

If caught early, new pioneering treatments can slow the disease’s remorseless progression, but there’s no surefire way to predict who will develop the dementia associated with Alzheimer’s.

Now, Boston University researchers say they have designed a promising new artificial intelligence computer program, or model, that could one day help change that—just by analysing a patient’s speech.

Their model can predict, with an accuracy rate of 78.5%, whether someone with mild cognitive impairment is likely to remain stable over the next six years—or fall into the dementia associated with Alzheimer’s disease.

While allowing clinicians to peer into the future and make earlier diagnoses, the researchers say their work could also help make cognitive impairment screening more accessible by automating parts of the process—no expensive lab tests, imaging exams, or even office visits required.

The model is powered by machine learning, a subset of AI where computer scientists teach a program to independently analyse data.

“We wanted to predict what would happen in the next six years—and we found we can reasonably make that prediction with relatively good confidence and accuracy,” says Ioannis (Yannis) Paschalidis, director of the BU Rafik B. Hariri Institute for Computing and Computational Science & Engineering. “It shows the power of AI.”

The multidisciplinary team of engineers, neurobiologists, and computer and data scientists published their findings in Alzheimer’s & Dementia, the journal of the Alzheimer’s Association.

“We hope, as everyone does, that there will be more and more Alzheimer’s treatments made available,” says Paschalidis, a BU College of Engineering Distinguished Professor of Engineering and founding member of the Faculty of Computing & Data Sciences.

“If you can predict what will happen, you have more of an opportunity and time window to intervene with drugs, and at least try to maintain the stability of the condition and prevent the transition to more severe forms of dementia.”

Calculating the probability of Alzheimer’s Disease

To train and build their new model, the researchers turned to data from one of the nation’s oldest and longest-running studies—the BU-led Framingham Heart Study. Although the Framingham study is focused on cardiovascular health, participants showing signs of cognitive decline undergo regular neuropsychological tests and interviews, producing a wealth of longitudinal information on their cognitive well-being.

Paschalidis and his colleagues were given audio recordings of 166 initial interviews with people, between ages 63 and 97, diagnosed with mild cognitive impairment—76 who would remain stable for the next six years and 90 whose cognitive function would progressively decline.

They then used a combination of speech recognition tools—similar to the programs powering your smart speaker—and machine learning to train a model to spot connections between speech, demographics, diagnosis, and disease progression.

After training it on a subset of the study population, they tested its predictive prowess on the rest of the participants.

“We combine the information we extract from the audio recordings with some very basic demographics—age, gender, and so on—and we get the final score,” says Paschalidis.

“You can think of the score as the likelihood, the probability, that someone will remain stable or transition to dementia. It had significant predictive ability.”

Rather than using acoustic features of speech, like enunciation or speed, the model is just pulling from the content of the interview—the words spoken, how they’re structured. And Paschalidis says the information they put into the machine learning program is rough around the edges: the recordings, for example, are messy—low-quality and filled with background noise.

“It’s a very casual recording,” he says. “And still, with this dirty data, the model is able to make something out of it.”

That’s important, because the project was partly about testing AI’s ability to make the process of dementia diagnosis more efficient and automated, with little human involvement. In the future, the researchers say, models like theirs could be used to bring care to patients who aren’t near medical centres or to provide routine monitoring through interaction with an at-home app, drastically increasing the number of people who get screened.

According to Alzheimer’s Disease International, the majority of people with dementia worldwide never receive a formal diagnosis, leaving them shut off from treatment and care.

Rhoda Au, a co-author on the paper, says AI has the power to create “equal opportunity science and healthcare.” The study builds on the same team’s previous work, where they found AI could accurately detect cognitive impairment using voice recordings.

“Technology can overcome the bias of work that can only be done by those with resources, or care that has relied on specialized expertise that is not available to everyone,” says Au, a BU Chobanian & Avedisian School of Medicine professor of anatomy and neurobiology.

For her, one of the most exciting findings was “that a method for cognitive assessment that has the potential to be maximally inclusive—possibly independent of age, sex/gender, education, language, culture, income, geography—could serve as a potential screening tool for detecting and monitoring symptoms related to Alzheimer’s disease.”

A dementia diagnosis from home

In future research, Paschalidis would like to explore using data not just from formal clinician-patient interviews—with their scripted questions and predictable back-and-forth—but also from more natural, everyday conversations.

He’s already looking ahead to a project on if AI can help diagnose dementia via a smartphone app, as well as expanding the current study beyond speech analysis—the Framingham tests also include patient drawings and data on daily life patterns—to boost the model’s predictive accuracy.

“Digital is the new blood,” says Au. “You can collect it, analyse it for what is known today, store it, and reanalyse it for whatever new emerges tomorrow.”

This research was funded, in part, by the National Science Foundation, the National Institutes of Health, and the BU Rajen Kilachand Fund for Integrated Life Science and Engineering.

Republishers are kindly reminded to uphold journalistic integrity by providing proper crediting, including a direct link back to the original source URL.

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UK body calls for more ageing research backing

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The British Society for Research on Ageing (BSRA) is calling for more public backing in the UK for research to help people stay healthier for longer, as an alternative to charities that support research on diseases.

The greatest risk factor for disease is ageing, but we have very little charitable support for research into how to slow ageing, the organisation warns.

Many diseases such as cancers and heart disease tragically shorten lives far too early, or like Alzheimer’s and arthritis, destroy quality of life for patients and carers. There is understandably huge public charitable support for more research. However, the greatest risk factor for those diseases, and even infectious diseases like COVID, is ageing.

Yet in comparison there is currently very little support for research to understand how we can slow ageing to prevent disease. This approach may be more productive in the long term to fight disease. Furthermore, keeping people healthier for longer, or avoiding chronic diseases all together, would be the most favourable outcome.

The UK population is ageing fast, putting pressure on the NHS and the economy. Despite this pressing problem all around us, there is no accessible way for people to support research into ageing in the UK. The BSRA aims to change that.

With a very small budget and almost completely run by volunteers, the BSRA has successfully funded several small research projects but progress needs to be accelerated. More funding is needed because it takes years to see the effects of ageing, so studies are long. Also ageing affects individuals in different ways, meaning that large numbers of people must be studied to make firm conclusions.

Therefore, there is an urgency to get studies funded and the BSRA has decided to launch an ambitious fundraising campaign to boost research into ageing. Initially, the Society aims to fund a series of one year research projects at the Masters degree level at universities across the UK and with plans to raise much more in the future to support longer and more ambitious projects that will impact the lives of the general public.

Chair of the BSRA, Prof David Weinkove from Durham University, says “The time is now to really get behind research into the biology of ageing. We have fantastic researchers across the country, but they are held back by a lack of funding. Evidence-based research is needed to understand how we people can stay healthier for longer, and to then we must make that knowledge available to as many people as possible”.

Dr Jed Lye says “This is a great opportunity for the public to help, for corporations to contribute, or philanthropists wanting a large impact with a relatively small donation; every £20,000 we raise can fund an entire year of research into ageing and longevity, and gets a budding scientist their research qualification.”

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