Heart Rate Analysis
Selected Publications
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Petros Arsenos and George Manis,
‘‘Deceleration capacity of heart rate: Two new methods
of computation,’’ Biomedical Signal Processing and Control,
Elsevier, vol. 14, pp. 158–163, Nov.
2014 [link]
Abstract: Deceleration Capacity (DC) expresses the property of the neural control
of the heart extrinsically to decelerate its rate. For the computation of DC a
mathematical method has been proposed and used. Although this method was proved
of significant prognostic value, it may produce meaningless negative values for
DC, something in contradiction with the principle of inter beat deceleration.
In this paper we propose two new methods of computation, DCsgn
(DC sign) and BBDC (Beat to Beat Deceleration Capacity), which not only give
positive values for DC but could also improve the original method. DCsgn modifies the filtering procedure by totally excluding
from computation segments that include possible artifacts. It also uses
information of four successive beats in order to detect deceleration
(acceleration) segments and not only from the anchor points. BBDC bases all
computations on two and not on four successive beats, detecting in this way
shorter-term relationships. In order to evaluate the proposed methods, a
dataset of 20 young and 20 elderly subjects, all healthy, has been used.
Experimental results verify our theoretical claims and show that the proposed
method can discriminate more efficiently healthy young and elderly subjects
than the original method.
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Argyro Kampouraki, George Manis,
and Christoforos Nikou, ‘‘Heartbeat timeseries classification with support vector
machines,’’ IEEE Transactions on Information Technology
in Biomedicine, vol. 13, no. 4, pp. 512–518, Jul. 2009 [link]
Abstract: In this study,
heartbeat time series are classified using support vector machines (SVMs).
Statistical methods and signal analysis techniques are used to extract features
from the signals. The SVM classifier is favorably compared to other neural
network-based classification approaches by performing leave-one-out cross
validation. The performance of the SVM with respect to other state-of-the-art
classifiers is also confirmed by the classification of signals presenting very
low signal-to-noise ratio. Finally, the influence of the number of features to
the classification rate was also investigated for two real datasets. The first
dataset consists of long-term ECG recordings of young and elderly healthy
subjects. The second dataset consists of long-term ECG recordings of normal
subjects and subjects suffering from coronary artery disease.