77,074 research outputs found
Leadership Advisory Meeting
Audio recording of the Leadership Advisory Meeting from 6 February 1982
A Message to Landon from the Heartbeat Gang
Audio recording of A Message to Landon from the Heartbeat Gang from September 1981
Heart Beat Characterization from Ballistocardiogram Signals using Extended Functions of Multiple Instances
A multiple instance learning (MIL) method, extended Function of Multiple
Instances (FUMI), is applied to ballistocardiogram (BCG) signals produced by
a hydraulic bed sensor. The goal of this approach is to learn a personalized
heartbeat "concept" for an individual. This heartbeat concept is a prototype
(or "signature") that characterizes the heartbeat pattern for an individual in
ballistocardiogram data. The FUMI method models the problem of learning a
heartbeat concept from a BCG signal as a MIL problem. This approach elegantly
addresses the uncertainty inherent in a BCG signal e. g., misalignment between
training data and ground truth, mis-collection of heartbeat by some
transducers, etc. Given a BCG training signal coupled with a ground truth
signal (e.g., a pulse finger sensor), training "bags" labeled with only binary
labels denoting if a training bag contains a heartbeat signal or not can be
generated. Then, using these bags, FUMI learns a personalized concept of
heartbeat for a subject as well as several non-heartbeat background concepts.
After learning the heartbeat concept, heartbeat detection and heart rate
estimation can be applied to test data. Experimental results show that the
estimated heartbeat concept found by FUMI is more representative and a more
discriminative prototype of the heartbeat signals than those found by
comparison MIL methods in the literature.Comment: IEEE EMBC 2016, pp. 1-
H2B: Heartbeat-based Secret Key Generation Using Piezo Vibration Sensors
We present Heartbeats-2-Bits (H2B), which is a system for securely pairing
wearable devices by generating a shared secret key from the skin vibrations
caused by heartbeat. This work is motivated by potential power saving
opportunity arising from the fact that heartbeat intervals can be detected
energy-efficiently using inexpensive and power-efficient piezo sensors, which
obviates the need to employ complex heartbeat monitors such as
Electrocardiogram or Photoplethysmogram. Indeed, our experiments show that
piezo sensors can measure heartbeat intervals on many different body locations
including chest, wrist, waist, neck and ankle. Unfortunately, we also discover
that the heartbeat interval signal captured by piezo vibration sensors has low
Signal-to-Noise Ratio (SNR) because they are not designed as precision
heartbeat monitors, which becomes the key challenge for H2B. To overcome this
problem, we first apply a quantile function-based quantization method to fully
extract the useful entropy from the noisy piezo measurements. We then propose a
novel Compressive Sensing-based reconciliation method to correct the high bit
mismatch rates between the two independently generated keys caused by low SNR.
We prototype H2B using off-the-shelf piezo sensors and evaluate its performance
on a dataset collected from different body positions of 23 participants. Our
results show that H2B has an overwhelming pairing success rate of 95.6%. We
also analyze and demonstrate H2B's robustness against three types of attacks.
Finally, our power measurements show that H2B is very power-efficient
Neurohormonal modulation of the Limulus heart: amine actions on neuromuscular transmission and cardiac muscle
The responses of Limulus cardiac neuromuscular junctions and cardiac muscle cells to four endogenous amines were determined in order to identify the cellular targets underlying amine modulation of heartbeat amplitude. The amines increased the amplitude of the Limulus heartbeat, with dopamine (DA) being more potent than octopamine, epinephrine or norepinephrine. The effect of DA on heartbeat amplitude was not blocked by phentolamine. DA enhanced the contractility of deganglionated heart muscle, with time course and dose-dependence similar to its effect on the intact heart. The amines also enhanced neuromuscular transmission, with time course and dose-dependence similar to their effects upon the intact heart. The amplitude of unitary excitatory junction potentials (EJPs) and frequency of miniature excitatory junction potentials (mEJPs) were increased by DA, while mEJP amplitude was unchanged. Thus DA, and probably the other amines, had a presynaptic effect. Combined actions upon cardiac muscle and cardiac neuromuscular transmission account for the ability of these amines to increase the amplitude of the Limulus heartbeat
An Internet Heartbeat
Obtaining sound inferences over remote networks via active or passive
measurements is difficult. Active measurement campaigns face challenges of
load, coverage, and visibility. Passive measurements require a privileged
vantage point. Even networks under our own control too often remain poorly
understood and hard to diagnose. As a step toward the democratization of
Internet measurement, we consider the inferential power possible were the
network to include a constant and predictable stream of dedicated lightweight
measurement traffic. We posit an Internet "heartbeat," which nodes periodically
send to random destinations, and show how aggregating heartbeats facilitates
introspection into parts of the network that are today generally obtuse. We
explore the design space of an Internet heartbeat, potential use cases,
incentives, and paths to deployment
A Novel Adaptive Spectrum Noise Cancellation Approach for Enhancing Heartbeat Rate Monitoring in a Wearable Device
This paper presents a novel approach, Adaptive Spectrum Noise Cancellation (ASNC), for motion artifacts removal in Photoplethysmography (PPG) signals measured by an optical biosensor to obtain clean PPG waveforms for heartbeat rate calculation. One challenge faced by this optical sensing method is the inevitable noise induced by movement when the user is in motion, especially when the motion frequency is very close to the target heartbeat rate. The proposed ASNC utilizes the onboard accelerometer and gyroscope sensors to detect and remove the artifacts adaptively, thus obtaining accurate heartbeat rate measurement while in motion. The ASNC algorithm makes use of a commonly accepted spectrum analysis approaches in medical digital signal processing, discrete cosine transform, to carry out frequency domain analysis. Results obtained by the proposed ASNC have been compared to the classic algorithms, the adaptive threshold peak detection and adaptive noise cancellation. The mean (standard deviation) absolute error and mean relative error of heartbeat rate calculated by ASNC is 0.33 (0.57) beatsĀ·min-1 and 0.65%, by adaptive threshold peak detection algorithm is 2.29 (2.21) beatsĀ·min-1 and 8.38%, by adaptive noise cancellation algorithm is 1.70 (1.50) beatsĀ·min-1 and 2.02%. While all algorithms performed well with both simulated PPG data and clean PPG data collected from our Verity device in situations free of motion artifacts, ASNC provided better accuracy when motion artifacts increase, especially when motion frequency is very close to the heartbeat rate
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