Category: Heart

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AI Heart

Heart attack, stroke, predicted via retinal images

Google’s Lily Peng has developed an algorithm that can predict heart attacks and strokes by analyzing images of the retina.

The system also shows which eye areas lead to successful predictions, which can provide insight into the causes of cardiovascular disease.

The dataset consisted of 48,101 patients from the UK Biobank database and 236,234 patients from EyePACS database.  A study of  12,026 and 999 patients showed a high level of accuracy:

-Retinal images of a smoker from a non-smoker 71 percent of the time, compared to a ~50 percent human  accuracy.

-While doctors can typically distinguish between the retinal images of patients with severe high blood pressure and normal patients, Google AI’s algorithm predicts the systolic blood pressure within 11 mmHg on average for patients overall, including those with and without high blood pressure.

-According to the company the algorithm predicted direct cardiovascular events “fairly accurately, ” statin that “given the retinal image of one patient who (up to 5 years) later experienced a major CV event (such as a heart attack) and the image of another patient who did not, our algorithm could pick out the patient who had the CV event 70% of the time. This performance approaches the accuracy of other CV risk calculators that require a blood draw to measure cholesterol.”

According to Peng: “Given the retinal image of one patient who (up to 5 years) later experienced a major CV event (such as a heart attack) and the image of another patient who did not, our algorithm could pick out the patient who had the CV event 70 percent of the time, This performance approaches the accuracy of other CV risk calculators that require a blood draw to measure cholesterol.”

Join ApplySci at Wearable Tech + Digital Health + Neurotech Silicon Valley on February 26-27, 2018 at Stanford University. Speakers include:  Vinod Khosla – Justin Sanchez – Brian Otis – Bryan Johnson – Zhenan Bao – Nathan Intrator – Carla Pugh – Jamshid Ghajar – Mark Kendall – Robert Greenberg – Darin Okuda – Jason Heikenfeld – Bob Knight – Phillip Alvelda – Paul Nuyujukian –  Peter Fischer – Tony Chahine – Shahin Farshchi – Ambar Bhattacharyya – Adam D’Augelli – Juan-Pablo Mas – Shreyas Shah– Walter Greenleaf – Jacobo Penide  – Peter Fischer – Ed Boyden

**LAST TICKETS AVAILABLE

Categories
Heart Sensors

Contact-free blood pressure, heart and breath rate monitoring

Cornell’s Edwin Kan has developed a contact-free vital sign monitor  using radio-frequency signals and microchip tags. Blood pressure, heart rate and breath rate  are measured when radio waves bounce off the body and internal organs, and are detected by an electronic reader from a location anywhere in the room.  200 people can be monitored simultaneously.

According to Kan, the signal is as accurate as an ECG or blood-pressure cuff.  He believes that the technology could be used to measure bowel movement, eye movement and other internal mechanical motions.

Join ApplySci at Wearable Tech + Digital Health + Neurotech Silicon Valley on February 26-27, 2018 at Stanford University. Speakers include:  Vinod Khosla – Justin Sanchez – Brian Otis – Bryan Johnson – Zhenan Bao – Nathan Intrator – Carla Pugh – Jamshid Ghajar – Mark Kendall – Robert Greenberg – Darin Okuda – Jason Heikenfeld – Bob Knight – Phillip Alvelda – Paul Nuyujukian –  Peter Fischer – Tony Chahine – Shahin Farshchi – Ambar Bhattacharyya – Adam D’Augelli – Juan-Pablo Mas – Michael Eggleston – Walter Greenleaf – Jacobo Penide

Registration rates increase today, Friday, December 22nd.

Categories
Heart Sensors

Single phone sensor tracks heart rate, HR variability, BP, oxygen saturation, ECG, PPG

 Sensio by MediaTek is a  biosensor that monitors  heart rate, heart rate variability,  blood pressure, peripheral oxygen saturation levels, ECG and PPG, from a smartphone, in 60 seconds.  This could allow continuous monitoring with out multiple sensors.

LEDs and a light sensitive sensor measure the absorption of red and infrared light from a  user’s fingertips. Touching a sensor allows the measurement of ECG and PPG waveforms.

Join ApplySci at Wearable Tech + Digital Health + Neurotech Silicon Valley on February 26-27, 2018 at Stanford University. Speakers include:  Vinod Khosla – Justin Sanchez – Brian Otis – Bryan Johnson – Zhenan Bao – Nathan Intrator – Carla Pugh – Jamshid Ghajar – Mark Kendall – Robert Greenberg – Darin Okuda – Jason Heikenfeld – Bob Knight – Phillip Alvelda – Paul Nuyujukian –  Peter Fischer – Tony Chahine – Shahin Farshchi – Ambar Bhattacharyya – Adam D’Augelli – Juan-Pablo Mas – Michael Eggleston – Walter Greenleaf – Jacobo Penide

Categories
Heart

3D coronary artery model analyzes impact of blockages

HeartFlow FFR uses data from a CT scan to create a 3D model of the coronary arteries and analyze the impact that blockages have on heart flow, to determine whether a stent is necessary.  It replaces a test that uses direct measurement with an instrument inserted into the heart.

Standard practice is to push a thin wire  past a blockage in a patient’s coronary artery, using a small sensor on the tip to detect whether the build-up has significantly reduced blood flow. A study of 600,000  patients at 1,100 hospitals showed that  this invasive procedure proves unnecessary about 58 percent of the time. The wire either finds that there is no blockage present or that it is not severe enough to require a stent.

The company has published multiple studies showing that both methods produce similarly accurate results. Heartflow FFR measures blood pressure throughout the coronary arteries rather than in just one location.

Join ApplySci at Wearable Tech + Digital Health + Neurotech Silicon Valley on February 26-27, 2018 at Stanford University. Speakers include:  Vinod Khosla – Justin Sanchez – Brian Otis – Bryan Johnson – Zhenan Bao – Nathan Intrator – Carla Pugh – Jamshid Ghajar – Mark Kendall – Robert Greenberg – Darin Okuda – Jason Heikenfeld – Bob Knight – Phillip Alvelda – Paul Nuyujukian –  Peter Fischer – Tony Chahine – Shahin Farshchi – Ambar Bhattacharyya – Adam D’Augelli – Juan-Pablo Mas – Michael Eggleston

Registration rates increase Friday, December 8th

Categories
Heart Wearables

FDA approved EKG band monitors heart activity via Apple Watch

AliveCor’s Kardia EKG band is the first medical accessory to receive FDA approval for use with the Apple Watch.

Unlike the optical-based sensor built into the Apple Watch, EKG is considered the most accurate way to record heart activity. AliveCor claims that Kardia is a  medical grade heart rate monitor that can identify abnormal heart rhythms such as atrial fibrillation, quickly. It could also detect palpitations, shortness of breath and irregular heart rate, which could be signifiers of stroke.

While wearing the Apple Watch-attached band, users put their fingers on the sensor to receive a report of their heart activity.  This simple interface is easy to use, and the frequent measurements can be sent directly to one’s doctor.

Join ApplySci at Wearable Tech + Digital Health + Neurotech Silicon Valley on February 26-27, 2018 at Stanford University. Speakers include:  Vinod Khosla – Justin Sanchez – Brian Otis – Bryan Johnson – Zhenan Bao – Nathan Intrator – Carla Pugh – Jamshid Ghajar – Mark Kendall – Robert Greenberg – Darin Okuda – Jason Heikenfeld – Bob Knight – Phillip Alvelda – Paul Nuyujukian –  Peter Fischer – Tony Chahine – Shahin Farshchi – Ambar Bhattacharyya – Adam D’Augelli – Juan-Pablo Mas

Registration rates increase Friday, December 1st

Categories
Brain Heart Wearables

Small, foam hearable captures heart data

In a small study, Danilo Mandic from Imperial College London has shown that his hearable can be used to capture heart data. The device detected heart pulse by sensing the dilation and constriction of tiny blood vessels in the ear canal, using the mechanical part of the electro-mechanical sensor. The hearable is made of foam and molds to the shape of the ear. The goal is a comfortable and discreet continuous monitor that will enable physicians to receive extensive data. In addition to the device’s mechanical sensors, Mandic, a signal processing experter, claims that electrical sensors detect brain activity that could  monitor sleep, epilepsy, and drug delivery, and be used in personal authentication and cyber security.

Join ApplySci at Wearable Tech + Digital Health + Neurotech Silicon Valley on February 26-27, 2018 at Stanford University. Speakers include:  Vinod Khosla – Justin Sanchez – Brian Otis – Bryan Johnson – Zhenan Bao – Nathan Intrator – Carla Pugh – Jamshid Ghajar – Mark Kendall – Robert Greenberg – Darin Okuda – Jason Heikenfeld – Bob Knight – Phillip Alvelda – Paul Nuyujukian –  Peter Fischer – Tony Chahine – Shahin Farshchi – Ambar Bhattacharyya – Adam D’Augelli

Registration rates increase November 24th, 2017

 

Categories
Heart

App uses phone’s camera to monitor heart health

Mory Gharib and Caltech colleagues have developed an app which uses a phone camera to monitor heart health.  When held to the neck, it infers  the left ventricular ejection fraction  of the heart by measuring the amount that the carotid artery displaces the skin of the neck as blood pumps through it.

According to Gharib:”What is exciting about this study is that it shows our technique is as accurate as echocardiography at estimating LVEF when both are compared to the gold standard of cardiac MRI. This has the potential to revolutionise how doctors and patients can screen for and monitor heart disease both in the US and the developing world.”

Click to view Caltech video

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 – Nathan Intrator –  Tom Insel – John Rogers – Jamshid Ghajar – Riccardo Sabatini – Phillip Alvelda – Michael Weintraub – Nancy Brown – Steve Kraus – Bill Geary – Mary Lou Jepsen

ANNOUNCING WEARABLE TECH + DIGITAL HEALTH + NEUROTECH SILICON VALLEY – FEBRUARY 26 -27, 2018 @ STANFORD UNIVERSITY –  FEATURING:  ZHENAN BAO – JUSTIN SANCHEZ – BRYAN JOHNSON – NATHAN INTRATOR – VINOD KHOSLA

Categories
Brain Heart Seniors Sensors

Wall sensor monitors walking speed, stride to track health

MIT’s Dina Katabi, Chen-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

Categories
fitness Heart Sensors Wearables

Future hearable sensors could track physical, emotional state

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

Categories
Heart Sensors

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

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.

Categories
AI Heart Machine Learning

Machine learning tools predict heart failure

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

 

 

Categories
Heart Sensors

Implanted sensors predict heart failure events

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