314 research outputs found
Unsupervised Heart-rate Estimation in Wearables With Liquid States and A Probabilistic Readout
Heart-rate estimation is a fundamental feature of modern wearable devices. In
this paper we propose a machine intelligent approach for heart-rate estimation
from electrocardiogram (ECG) data collected using wearable devices. The novelty
of our approach lies in (1) encoding spatio-temporal properties of ECG signals
directly into spike train and using this to excite recurrently connected
spiking neurons in a Liquid State Machine computation model; (2) a novel
learning algorithm; and (3) an intelligently designed unsupervised readout
based on Fuzzy c-Means clustering of spike responses from a subset of neurons
(Liquid states), selected using particle swarm optimization. Our approach
differs from existing works by learning directly from ECG signals (allowing
personalization), without requiring costly data annotations. Additionally, our
approach can be easily implemented on state-of-the-art spiking-based
neuromorphic systems, offering high accuracy, yet significantly low energy
footprint, leading to an extended battery life of wearable devices. We
validated our approach with CARLsim, a GPU accelerated spiking neural network
simulator modeling Izhikevich spiking neurons with Spike Timing Dependent
Plasticity (STDP) and homeostatic scaling. A range of subjects are considered
from in-house clinical trials and public ECG databases. Results show high
accuracy and low energy footprint in heart-rate estimation across subjects with
and without cardiac irregularities, signifying the strong potential of this
approach to be integrated in future wearable devices.Comment: 51 pages, 12 figures, 6 tables, 95 references. Under submission at
Elsevier Neural Network
Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications
In the era when the market segment of Internet of Things (IoT) tops the chart
in various business reports, it is apparently envisioned that the field of
medicine expects to gain a large benefit from the explosion of wearables and
internet-connected sensors that surround us to acquire and communicate
unprecedented data on symptoms, medication, food intake, and daily-life
activities impacting one's health and wellness. However, IoT-driven healthcare
would have to overcome many barriers, such as: 1) There is an increasing demand
for data storage on cloud servers where the analysis of the medical big data
becomes increasingly complex, 2) The data, when communicated, are vulnerable to
security and privacy issues, 3) The communication of the continuously collected
data is not only costly but also energy hungry, 4) Operating and maintaining
the sensors directly from the cloud servers are non-trial tasks. This book
chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog
Computing is a service-oriented intermediate layer in IoT, providing the
interfaces between the sensors and cloud servers for facilitating connectivity,
data transfer, and queryable local database. The centerpiece of Fog computing
is a low-power, intelligent, wireless, embedded computing node that carries out
signal conditioning and data analytics on raw data collected from wearables or
other medical sensors and offers efficient means to serve telehealth
interventions. We implemented and tested an fog computing system using the
Intel Edison and Raspberry Pi that allows acquisition, computing, storage and
communication of the various medical data such as pathological speech data of
individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate
estimation, and Electrocardiogram (ECG)-based Q, R, S detection.Comment: 29 pages, 30 figures, 5 tables. Keywords: Big Data, Body Area
Network, Body Sensor Network, Edge Computing, Fog Computing, Medical
Cyberphysical Systems, Medical Internet-of-Things, Telecare, Tele-treatment,
Wearable Devices, Chapter in Handbook of Large-Scale Distributed Computing in
Smart Healthcare (2017), Springe
Low Power Circuits for Smart Flexible ECG Sensors
Cardiovascular diseases (CVDs) are the world leading cause of death. In-home heart condition monitoring effectively reduced the CVD patient hospitalization rate. Flexible electrocardiogram (ECG) sensor provides an affordable, convenient and comfortable in-home monitoring solution. The three critical building blocks of the ECG sensor i.e., analog frontend (AFE), QRS detector, and cardiac arrhythmia classifier (CAC), are studied in this research.
A fully differential difference amplifier (FDDA) based AFE that employs DC-coupled input stage increases the input impedance and improves CMRR. A parasitic capacitor reuse technique is proposed to improve the noise/area efficiency and CMRR. An on-body DC bias scheme is introduced to deal with the input DC offset. Implemented in 0.35m CMOS process with an area of 0.405mm2, the proposed AFE consumes 0.9W at 1.8V and shows excellent noise effective factor of 2.55, and CMRR of 76dB. Experiment shows the proposed AFE not only picks up clean ECG signal with electrodes placed as close as 2cm under both resting and walking conditions, but also obtains the distinct -wave after eye blink from EEG recording.
A personalized QRS detection algorithm is proposed to achieve an average positive prediction rate of 99.39% and sensitivity rate of 99.21%. The user-specific template avoids the complicate models and parameters used in existing algorithms while covers most situations for practical applications. The detection is based on the comparison of the correlation coefficient of the user-specific template with the ECG segment under detection. The proposed one-target clustering reduced the required loops.
A continuous-in-time discrete-in-amplitude (CTDA) artificial neural network (ANN) based CAC is proposed for the smart ECG sensor. The proposed CAC achieves over 98% classification accuracy for 4 types of beats defined by AAMI (Association for the Advancement of Medical Instrumentation). The CTDA scheme significantly reduces the input sample numbers and simplifies the sample representation to one bit. Thus, the number of arithmetic operations and the ANN structure are greatly simplified. The proposed CAC is verified by FPGA and implemented in 0.18m CMOS process. Simulation results show it can operate at clock frequencies from 10KHz to 50MHz. Average power for the patient with 75bpm heart rate is 13.34W
An Error-Based Approximation Sensing Circuit for Event-Triggered, Low Power Wearable Sensors
Event-based sensors have the potential to optimize energy consumption at
every stage in the signal processing pipeline, including data acquisition,
transmission, processing and storage. However, almost all state-of-the-art
systems are still built upon the classical Nyquist-based periodic signal
acquisition. In this work, we design and validate the Polygonal Approximation
Sampler (PAS), a novel circuit to implement a general-purpose event-based
sampler using a polygonal approximation algorithm as the underlying sampling
trigger. The circuit can be dynamically reconfigured to produce a coarse or a
detailed reconstruction of the analog input, by adjusting the error threshold
of the approximation. The proposed circuit is designed at the Register Transfer
Level and processes each input sample received from the ADC in a single clock
cycle. The PAS has been tested with three different types of archetypal signals
captured by wearable devices (electrocardiogram, accelerometer and respiration
data) and compared with a standard periodic ADC. These tests show that
single-channel signals, with slow variations and constant segments (like the
used single-lead ECG and the respiration signals) take great advantage from the
used sampling technique, reducing the amount of data used up to 99% without
significant performance degradation. At the same time, multi-channel signals
(like the six-dimensional accelerometer signal) can still benefit from the
designed circuit, achieving a reduction factor up to 80% with minor performance
degradation. These results open the door to new types of wearable sensors with
reduced size and higher battery lifetime
Acoustic sensing as a novel approach for cardiovascular monitoring at the wrist
Cardiovascular diseases are the number one cause of deaths globally. An increased cardiovascular risk can be detected by a regular monitoring of the vital signs including the heart rate, the heart rate variability (HRV) and the blood pressure. For a user to undergo continuous vital sign monitoring, wearable systems prove to be very useful as the device can be integrated into the user's lifestyle without affecting the daily activities. However, the main challenge associated with the monitoring of these cardiovascular parameters is the requirement of different sensing mechanisms at different measurement sites. There is not a single wearable device that can provide sufficient physiological information to track the vital signs from a single site on the body. This thesis proposes a novel concept of using acoustic sensing over the radial artery to extract cardiac parameters for vital sign monitoring. A wearable system consisting of a microphone is designed to allow the detection of the heart sounds together with the pulse wave, an attribute not possible with existing wrist-based sensing methods.
Methods: The acoustic signals recorded from the radial artery are a continuous reflection of the instantaneous cardiac activity. These signals are studied and characterised using different algorithms to extract cardiovascular parameters. The validity of the proposed principle is firstly demonstrated using a novel algorithm to extract the heart rate from these signals. The algorithm utilises the power spectral analysis of the acoustic pulse signal to detect the S1 sounds and additionally, the K-means method to remove motion artifacts for an accurate heartbeat detection. The HRV in the short-term acoustic recordings is found by extracting the S1 events using the relative information between the short- and long-term energies of the signal. The S1 events are localised using three different characteristic points and the best representation is found by comparing the instantaneous heart rate profiles. The possibility of measuring the blood pressure using the wearable device is shown by recording the acoustic signal under the influence of external pressure applied on the arterial branch. The temporal and spectral characteristics of the acoustic signal are utilised to extract the feature signals and obtain a relationship with the systolic blood pressure (SBP) and diastolic blood pressure (DBP) respectively.
Results: This thesis proposes three different algorithms to find the heart rate, the HRV and the SBP/ DBP readings from the acoustic signals recorded at the wrist. The results obtained by each algorithm are as follows:
1. The heart rate algorithm is validated on a dataset consisting of 12 subjects with a data length of 6 hours. The results demonstrate an accuracy of 98.78%, mean absolute error of 0.28 bpm, limits of agreement between -1.68 and 1.69 bpm, and a correlation coefficient of 0.998 with reference to a state-of-the-art PPG-based commercial device. A high statistical agreement between the heart rate obtained from the acoustic signal and the photoplethysmography (PPG) signal is observed.
2. The HRV algorithm is validated on the short-term acoustic signals of 5-minutes duration recorded from each of the 12 subjects. A comparison is established with the simultaneously recorded electrocardiography (ECG) and PPG signals respectively. The instantaneous heart rate for all the subjects combined together achieves an accuracy of 98.50% and 98.96% with respect to the ECG and PPG signals respectively. The results for the time-domain and frequency-domain HRV parameters also demonstrate high statistical agreement with the ECG and PPG signals respectively.
3. The algorithm proposed for the SBP/ DBP determination is validated on 104 acoustic signals recorded from 40 adult subjects. The experimental outputs when compared with the reference arm- and wrist-based monitors produce a mean error of less than 2 mmHg and a standard deviation of error around 6 mmHg.
Based on these results, this thesis shows the potential of this new sensing modality to be used as an alternative, or to complement existing methods, for the continuous monitoring of heart rate and HRV, and spot measurement of the blood pressure at the wrist.Open Acces
Real-Time and Secure Wireless Health Monitoring
We present a framework for a wireless health
monitoring system using wireless networks such as ZigBee. Vital
signals are collected and processed using a 3-tiered architecture.
The first stage is the mobile device carried on the body that
runs a number of wired and wireless probes. This device is also
designed to perform some basic processing such as the heart
rate and fatal failure detection. At the second stage, further
processing is performed by a local server using the raw data
transmitted by the mobile device continuously. The raw data is
also stored at this server. The processed data as well as the
analysis results are then transmitted to the service provider
center for diagnostic reviews as well as storage. The main
advantages of the proposed framework are (1) the ability to
detect signals wirelessly within a body sensor network (BSN),
(2) low-power and reliable data transmission through ZigBee
network nodes, (3) secure transmission of medical data over BSN,
(4) efficient channel allocation for medical data transmission over
wireless networks, and (5) optimized analysis of data using an
adaptive architecture that maximizes the utility of processing and
computational capacity at each platform
Wearable System for Biosignal Acquisition and Monitoring Based on Reconfigurable Technologies
Wearable monitoring devices are now a usual commodity in the market, especially for the
monitoring of sports and physical activity. However, specialized wearable devices remain an open
field for high-risk professionals, such as military personnel, fire and rescue, law enforcement, etc.
In this work, a prototype wearable instrument, based on reconfigurable technologies and capable
of monitoring electrocardiogram, oxygen saturation, and motion, is presented. This reconfigurable
device allows a wide range of applications in conjunction with mobile devices. As a proof-of-concept,
the reconfigurable instrument was been integrated into ad hoc glasses, in order to illustrate the
non-invasive monitoring of the user. The performance of the presented prototype was validated
against a commercial pulse oximeter, while several alternatives for QRS-complex detection were
tested. For this type of scenario, clustering-based classification was found to be a very robust option.This work was funded by Banco Santander and Centro Mixto UGR-MADOC through project SIMMA
(code 2/16). The contribution of Víctor Toral was funded by the University of Granada through a grant from the
“Iniciación a la investigación 2016” program. The contribution of Antonio García was partially funded by Spain’s
Ministerio de Educación, Cultura y Deporte (Programa Estatal de Promoción del Talento y su Empleabilidad
en I+D+i, Subprograma Estatal de Movilidad, within Plan Estatal de Investigación Científica y Técnica y de
Innovación 2013-2016) under a “Salvador de Madariaga” grant (PRX17/00287). The contribution of Francisco J.
Romero was funded by Spain’s Ministerio de Educación, Cultura y Deporte under a FPU grant (FPU16/01451).
The contribution of Francisco M. Gómez-Campos was funded by Spain’s Ministerio de Economía, Industria y
Competitividad under Project ENE2016_80944_R
Wrist and Arm Body Surface Bipolar ECG Leads Signal and Sensor Study for Long-term Rhythm Monitoring
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