Category Archives: Heart

Wall sensor monitors walking speed, stride to track health

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MIT’s Dina KatabiChen-Yu Hsu, and colleagues have developed WiGait, a wall sensor that detects walking speed and stride to monitor health. This builds on previous MIT research which showed that radio signals could track breathing and heart rate, without wearables.

The  system works by transmitting low-power radio signals and analyzing how they reflect off  bodies within a radius of 9 to 12 meters. Machine learning algorithms separated walking periods from other activities and found the stable phase within each walking period.  The sensor, when combined with wearable devices, could also track Parkinson’s and MS symptoms, and help predict health events related to  heart failure,  lung disease, kidney failure, and stroke, as well as the risk of falls. Caregivers could also be notified in emergencies.


Join ApplySci at Wearable Tech + Digital Health + NeuroTech Boston on September 19, 2017 at the MIT Media Lab. Featuring Joi Ito – Ed Boyden – Roz Picard – George Church – Tom Insel – John Rogers – Jamshid Ghajar – Phillip Alvelda – Nathan Intrator

Future hearable sensors could track physical, emotional state

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Apple has filed patent applications describing wireless earbuds that monitor health while a wearer talks on the phone or listens to music.  This has obvious exercise-related implications, but could potentially track the physiological impact of one’s emotional state while making calls, as a mobile mental health tool.

Sensors included in the patent include EKG, ICG, VO2 and GSR.

Click to view patent applications:

Patent 1   |   Patent 2   |   Patent 3


Join ApplySci at Wearable Tech + Digital Health + NeuroTech Boston – Featuring Roz Picard, Tom Insel, John Rogers and Nathan Intrator – September 19, 2017 at the MIT Media Lab

Thin, flexible, insulated sensor could monitor the heart for 70 years

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Northwestern’s John Rogers has developed a sensor that can monitor electrical activity irregularities in the heart for 70 years.  The sensor is much safer and more refined than current technology, which degrades easily, and can harm patients.

An array of 396 voltage sensors are set in a very thin, multi-layer, flexible substrate,  meant to attach to the outside of the heart, covering a significant portion of the organ. Previous sensors arrays picked up signals through direct contact between a metal conductor and human tissue. The new array is covered with an insulating layer of impermeable silicon dioxide. This is a dramatic improvement on metal conductors, which corrode and allow biological fluids to leak through, which can lead to a short circuit and, potentially, ventricular fibrillation and cardiovascular collapse.  Previous attempts at  the adding an  insulating layer have been too thick for the signal to be recorded effectively.

According to Rogers: “You want this layer to be as thin as possible to enable a strong electrical coupling to the surrounding tissue, but you need it to be thick enough to serve as a robust barrier to water penetration.”  He seems to have achieved just this.

Rogers believes that with a larger surface area and more nodes, the sensors could one day cover most of the body’s organs . He will test whether they can both collect data and deliver energy to an organ, such as a pacemaker, or be able to study the underlying function of the brain.

Professor Rogers was a speaker at ApplySci’s recent Wearable Tech + Digital Health + Neurotech Silicon Valley conference, on February 8, 2017, at Stanford University.)  He will  present his latest work our upcoming Wearable Tech + Digital Health + Neurotech Boston conference, on September 19th at the MIT Media Lab.

Machine learning tools predict heart failure

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Declan O’Regan and MRC London Institute of Medical Sciences colleagues believe that AI can predict when pulmonary hypertension patients require more aggressive treatment to prevent death.

In a recent study,  machine learning software automatically analyzed moving images of a patient’s heart, captured during an MRI. It then used  image processing to build a “virtual 3D heart”, replicating how 30,000 points in the heart contract during each beat. The researchers fed the system data from hundreds of previous patients. By linking the data and models, the system learned which attributes of a heart, its shape and structure, put an individual at risk of heart failure.

The software was developed using data from 256 patients with pulmonary hypertension. It correctly predicted those who would still be alive after one year 80% of the time. The figure for doctors is 60%.

The researchers  want to test the technology in other forms of heart failure, including cardiomyopathy, to see when a pacemaker or other form of treatment is needed.

Click to view MRC London video.

ApplySci’s 6th  Digital Health + NeuroTech Silicon Valley  –  February 7-8 2017 @ Stanford   |   Featuring:   Vinod Khosla – Tom Insel – Zhenan Bao – Phillip Alvelda – Nathan Intrator – John Rogers – Roozbeh Ghaffari –Tarun Wadhwa – Eythor Bender – Unity Stoakes – Mounir Zok – Sky Christopherson – Marcus Weldon – Krishna Shenoy – Karl Deisseroth – Shahin Farshchi – Casper de Clercq – Mary Lou Jepsen – Vivek Wadhwa – Dirk Schapeler – Miguel Nicolelis

 

 

Implanted sensors predict heart failure events

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Penn State’s John Boehmer used Boston Scientific’s HeartLogic sensors (retrofitted in already implanted devices) to track heart failure in a study of 900 patients. The goal was continuous monitoring and early event detection and prevention.

Currently, heart failure is (not very successfully) managed by monitoring weight and reported symptoms.   One in five patients are readmitted within 30 days after being hospitalized for the condition.

The 900 patients were followed for one year. Software was uploaded to an implanted defibrillator, allowing it to act as sensors. Heart rate, activity, breathing, heart sounds and electrical activity in the chest were tracked.

70 percent of heart failure events were detected, usually more than a month before their occurance.  False positives were also reported,  which the researchers deemed to be in an ” acceptable range.”


ApplySci’s 6th   Wearable Tech + Digital Health + NeuroTech Silicon Valley  –  February 7-8 2017 @ Stanford   |   Featuring:   Vinod Khosla – Tom Insel – Zhenan Bao – Phillip Alvelda – Nathan Intrator – John Rogers – Mary Lou Jepsen – Vivek Wadhwa – Miguel Nicolelis – Roozbeh Ghaffari –Tarun Wadhwa – Eythor Bender – Unity Stoakes – Mounir Zok – Krishna Shenoy – Karl Deisseroth

 

Tiny sensor monitors the heart, recognizes speech, enables human-machine interfaces

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Northwestern professor John Rogers has released a paper detailing his latest tiny, wearable, flexible, highly accurate health sensor, which monitors the heart, recognizes speech, and can enable human-machine interfaces.  Professor Yonggang Huang is the corresponding author.

The soft, continuous monitor adheres to any part of the body, detecting mechanical waves that propagate through tissues and fluids in physiological activity — revealing acoustical signatures of individual events.  These include the opening and closing of heart valves, vocal cord vibration, and gastrointestinal tract movement.  ECG and EMG  electrodes can also be integrated.

Obvious practical applications include remote health monitoring, enabling seniors to age in place, and battlefield health and robot/drone control.  The vocal cord monitoring feature could also be used to assist the disabled communicate or control a keyboard.

ApplySci is honored to include Professor Rogers as a keynote speaker at Digital Health + NeuroTech Silicon Valley, on February 7-8, 2017, at Stanford University.


ApplySci’s 6th   Wearable Tech + Digital Health + NeuroTech Silicon Valley  –  February 7-8 2017 @ Stanford   |   Featuring:   Vinod Khosla – Tom Insel – Zhenan Bao – Phillip Alvelda – Nathan Intrator – John Rogers – Mary Lou Jepsen – Vivek Wadhwa – Miguel Nicolelis – Roozbeh Ghaffari –Tarun Wadhwa – Eythor Bender – Unity Stoakes – Mounir Zok – Krishna Shenoy – Karl Deisseroth

Registration rates increase today, November 18, 2016

 

 

Voice analysis as a diagnostic tool

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Beyond Verbal recently used its emotion-detecting voice analysis app in an attempt to predict coronary artery disease in 150 study participants, 120 of whom had presented for angiography.  The company claims to have identified 13 voice features  associated with CAD – and one associated with a 19-fold increase in its likelihood.

The researchers said that the voice indicator was strongest when a subject described a negative experience.  As this suggests an emotional connection to heart disease, could a neurotech wearable, that provides brain activity analysis, validate the process?  The company is already studying voice changes in Autism and Parkinson’s disease, but this was the first time they used their voice diagnostic tool in a non-brain disease study.


ApplySci’s 6th   Wearable Tech + Digital Health + NeuroTech Silicon Valley  –  February 7-8 2017 @ Stanford   |   Featuring:   Vinod Khosla – Tom Insel – Zhenan Bao – Phillip Alvelda – Nathan Intrator – John Rogers – Mary Lou Jepsen – Vivek Wadhwa – Miguel Nicolelis – Roozbeh Ghaffari –Tarun Wadhwa – Eythor Bender – Unity Stoakes – Mounir Zok – Krishna Shenoy – Karl Deisseroth

Registration rates increase Friday, November 18th

Verily developing low-power health wearable

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While visiting Verily last week, an MIT Technology Review journalist saw and described the company’s wearable vital tracker, called the “Cardiac and Activity Monitor” by  CTO Brian Otis.  Its novelty is a low-power e-paper display, which will address the universal problem of battery life.  Only with guaranteed continuous measurement can meaningful data be gathered and health analyzed.

The watch is reported to track pulse, heart rythm, skin temperature, light exposure, and noise levels — and perhaps cuffless blood pressure monitoring will be added to the mix.

The device is meant for use in medical research, with the goal of predicting disease.  According to scientific adviser (and former Mass General Physician-in-Chief) Dennis Ausiello: “The watch is one of several hardware activities that have a common goal, which is how to better manage the human condition and interrogate the human organism at scale across health and illness.”


ApplySci’s 6th   Wearable Tech + Digital Health + NeuroTech Silicon Valley  –  February 7-8 2017 @ Stanford   |   Featuring:   Vinod Khosla – Tom Insel – Zhenan Bao – Phillip Alvelda – Nathan Intrator – John Rogers – Mary Lou Jepsen – Vivek Wadhwa – Miguel Nicolelis – Roozbeh Ghaffari –Tarun Wadhwa – Eythor Bender – Unity Stoakes – Mounir Zok – Krishna Shenoy – Karl Deisseroth

Electronic scaffold replaces damaged tissue, stimulates heart

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Charles Lieber and Harvard colleagues have designed nanoscale electronic scaffolds, seeded with cardiac cells to produce a “bionic” patch to replace damaged cardiac tissue.  The flexible electronics can also electrically stimulate the heart, and change the frequency and direction of signal propagation, as tissue feedback is continuously monitored.

Instead of being located on the skin’s surface, electronic components are integrated throughout the tissue, allowing the detection of  early-stage cardiac instabilities and faster intervention. The device operates at  lower, safer voltages than a traditional pacemaker.

The patch could also be used to monitor responses to cardiac drugs, or to help  determine the effectiveness of drugs under development.

Wearable detects cardiac arrest, notifies emergency services

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 iBeat is a wearable emergency response system that continuously monitors the heart.  Meant for seniors, it detects cardiac arrest in real time, provides alerts, and sends regular updates to caregivers.

If cardiac arrest is detected, the wearer receives a call within 10 seconds.  If he/she cannot be reached, an emergency contact and emergency medical services are called.

A user can also activate the device manually in an emergency, such as a fall, car accident, or home invasion.


Wearable Tech + Digital Health NYC – June 7, 2016 @ the New York Academy of Sciences

NeuroTech NYC – June 8, 2016 @ the New York Academy of Sciences