Characterization of myocardial motion patterns by unsupervised multiple kernel learning.
We propose an independent objective method to characterize different patterns of functional responses to stress in the heart failure with preserved ejection fraction (HFPEF) syndrome by combining multiple temporally-aligned myocardial velocity traces at rest and during exercise, together with temporal information on the occurrence of cardiac events (valves openings/closures and atrial activation). The method builds upon multiple kernel learning, a machine learning technique that allows the combination of data of different nature and the reduction of their dimensionality towards a meaningful representation (output space). The learning process is kept unsupervised, to study the variability of the input traces without being conditioned by data labels. To enhance the physiological interpretation of the output space, the variability that it encodes is analyzed in the space of input signals after reconstructing the velocity traces via multiscale kernel regression. The methodology was applied to 2D sequences from a stress echocardiography protocol from 55 subjects (22 healthy, 19 HFPEF and 14 breathless subjects). The results confirm that characterization of the myocardial functional response to stress in the HFPEF syndrome may be improved by the joint analysis of multiple relevant features.
Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain. Electronic address: sergio.sanchezm@upf.edu.
Sergio
S
Sanchez-Martinez
Inria Asclepios research project, Sophia Antipolis, France.
Nicolas
N
Duchateau
Wales Heart Research Institute, Cardiff University, United Kingdom.
Tamas
T
Erdei
Wales Heart Research Institute, Cardiff University, United Kingdom.
Alan G
AG
Fraser
Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain; ICREA, Barcelona, Spain.
Bart H
BH
Bijnens
Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.
Gemma
G
Piella
D016022:Case-Control Studies; D004452:Echocardiography; D015444:Exercise / Q000502:physiology; D005080:Exercise Test; D006321:Heart / Q000000981:diagnostic imaging* / Q000502:physiology; D006333:Heart Failure / Q000000981:diagnostic imaging* / Q000503:physiopathology*; D006801:Humans; D009038:Motion; D009068:Movement; D015203:Reproducibility of Results; D012146:Rest / Q000502:physiology; D012680:Sensitivity and Specificity; D000069558:Unsupervised Machine Learning
Echocardiography; Multiple kernel learning; Myocardial motion; Pattern analysis