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Table 2: ANN and SVM prediction time in microcontroller.
Algorithm Time (ms)
ANN 0.6
SVM 30
SVM and ANN can reach same accuracy; how-
ever prediction algorithm for SVM takes about 50
times more than ANN in the same microcontroller.
This is shown in Table 2. For this application ANN
is better because the execution time is less than SVM
with the same accuracy.
In this application, there is a time limit of two sec-
onds determined by the time windows used in the fea-
ture extraction stage. Table 2 show that VF/VT could
be detected in real time using both SVM and ANN.
The support vector machine was only imple-
mented in the microcontroller. The implementation of
SVM in an FPGA requires the realization of complex
mathematical operations for calculating the Gaussian
kernel. Its implementation in the FPGA using Sys-
tem Generation is difficult because the multiplications
consumes all the resources of DSP-slices which are
limited. It is necessary the used of a math processor
tha could be implemented in a FPGA.
The microcontroller’s advantages are the low
power consumption and the additional peripherals.
Furthermore, it allows easy programming of complex
mathematical operations because is programmed in C.
Currently, the authors are working on multiclass
classifier that will allow to differentiate between VT
and VF and to detect other types of arrhythmias, such
as premature atrial contractions, premature ventricu-
lar contractions, atrial fibrillation, atrial flutter, among
others. Therefore the develop of robust classifier in
real time is necessary. A mixed platform with a mi-
crocontroller and parallel co-processors implemented
on a FPGA has been developed to implement a real-
time pre-processing, feature extraction and classifica-
tion system.
5 CONCLUSIONS
In this work, two machine learning methods were im-
plemented in an embedded system: SVM and ANN.
Both methods can be used to detect VT/VF in real-
time with FWT scales energy as features and 2 sec-
onds windows analysis.
SVM and ANN are powerful tools for arrhythmias
classification and could be real-time implemented in
both, microcontroller and FPGA. ANN is faster than
SVM, for VT/VF detection is better use ANN because
it has lower execution time than SVM, nevertheless
SVM was implemented considering future work with
other types of arrhythmias, where a robust classifier
will be necessary.
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