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Input-Feature Correlated
Asynchronous Analog to Information
Converter for ECG Monitoring
Ritika Agarwal , Student Member ,IEEE , and Sameer R. Sonkusale ,
Member ,IEEEE
IEEE TRANSACTION ON BIOMEDICAL CIRCUITS AND SYSTEMS ,
VOL.5, NO. 5, OCTOBER 2011
學
生:莊凱強
授課老師:王明賢
Outline
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Abstract
Introduction
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Motive
Method
Algorithm for the feature extraction
Experiments
Conclusion
References
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Abstract
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An architectural design of a novel variable intput
feature correlated asynchronous sampling and timeencode digitization approach for source compression
and direct feature extraction from physiological
signals.
The complete architecture represents an analog-toinformation(A2I) converter ,design for ultra-lowpower mixed-signal very-large-scale integrated
implementation.
Simulation results show large source compression in
ECG signal and more than 98% efficiency in the
detection of the Q、R and S wave for challenging ECG
waveforms , all with extremely low-power and
storage requirements.
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Introduction-Motive
With the growing trend toward wearable health monitoring
systems, a large amount of data is continuously collected,
stored, transmitted, and processed to extract essential
information from different physiological signals.
These requirement prove to a big constraint for mobile or
ambulatory applications where low power consumption is
prerequisite.
System which can compress the number of data samples
collected right at the source while simultaneously capturing
the main features of the signal will significantly reduce the
burden on power and storage requirements.
The goal is to provide early warnings to physician in case of
any ectopic heartbeat in order to provide effective timely
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diagnosis
and care to the heart patients.
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Introduction-Method
An adaptive asynchronous sampling approach samples
the input signals base on the slope, and the digital values
are generated every time the signal crosses the predefined
thresholds set by the built-in quantizer.
The thresholds are adaptively adjusted according to the
activity level of the input signal. When the signal is sparse
or has low levels of activity, the signal is sampled at
maximum resolution of the quantizer. However, when the
input signal exhibits higher levels of activity, the
quantization levels are skipped, producing less sampling
point and allowing power to be saved
In Fig.1(b),we show an adaptive asynchronously
sampled
base on the delay-mode processing approach. 5
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Introduction-Method
Although it is an excellent compression mechanism, it
could miss certain key aspects of signal. For feature
extraction from any signal, the slope transition points or
the peak/troughs of the signal are very critical.
We further expand upon the adaptive asynchronous
technique by utilizing it not just for reduction of the
number of samples acquired but to enable direct detection
and capture of the critical points in the waveform.
We call this approach an”input-feature correlated
asynchronous A2I convention”,it can be understood from
Fig.2(c).
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Fig.2.(a) Example of a synchronously sampled signal.
Fig.2.(b) Example of an adaptive asynchronously sampled
modeled after our prior approach.
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Fig.2.(c) Example of an input-feature correlated
asynchronously sampled signal.
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Introduction-Algorithm for the
feature extration
Basically, if Dout(n-2)<Dout(n-1)
and Dout(n-1)>Dout(n);the
feature extraction block
recognizes the occurrence of a
slope transition.
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Introduction-Algorithm for the
feature extration
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The same algorithm is
followed for the
calculation of trough.
These peak and trough
heights obtained then are
used for the calculation of
the top and the buttom
thresholds for adaptive
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technique
Experiments
Fig.(d)
Asynchronous sampling apporach
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Fig.(e)
synchronous sampling
apporach
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Conclusion
The design of input-feature-correlated A2I converter is
proposed for the extration of relevant information and
critical feature from the input signal right at the sensor
output.
The system consumes very low power and is void of all
complexities.
The whole system is highly efficient and can bring a
revolutionary change to today’s world where ambulatory
health monitoring is the demand of the era.
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References
(1) M. S. Manikandan and S. Daudapat, Quality Controlled Wavelet
Compression of ECG Signals by WEDD. Los Alamitos, CA: IEEE
Comput. Soc, 2007.
(2) L. Zhitao, K. Dong Youn, and W. A. Pearlman, “Wavelet compression
of ECG signals by the set partitioning in hierarchical trees algorithm,”
IEEE Trans. Biomed. Eng. , vol. 47, no. 7, pp. 849–856, Jul. 2000.
(3)E. J. Candes, J. Romberg, and T. Tao, “Robust uncertainty principles:
exact signal reconstruction from highly incomplete frequency information,”
IEEE Tran˙s. Inf. Theory, vol. 52, no. 2, pp. 489–509, Feb. 2006.
(4) E. J. Candes and M. B. Wakin, “An introduction to compressive sampling,”
IEEE Signal Process. Mag., vol. 25, no. 2, pp. 21–30, Mar.
2008.
(5) E. J. Candes and T. Tao, “Near-optimal signal recovery from random
projections: Universal encoding strategies?,” IEEE Trans. Inf. Theory,
vol. 52, no. 12, pp. 5406–5425, Dec. 2006.
(6) M. F. Duarte, M. A. Davenport, D. Takhar, J. N. Laska, S. Ting, K.
F. Kelly, and R. G. Baraniuk, “Single-pixel imaging via compressive
sampling,” IEEE Signal Process. Mag., , vol. 25, no. 2, pp. 83–91, Mar.
2008.
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