A Multi-Lead Ecg Classification Based On Random Projection Features

This paper presents a novel method for classification of multi-lead electrocardiogram (ECG) signals. The feature extraction is based on the random projection (RP) concept for dimensionality reduction. Furthermore, the classification is performed by a neuro-fuzzy classifier. Such a model can be easily implemented on portable systems for practical applications in both health monitoring and diagnostic purposes. Moreover, the RP implementation on portable systems is very challenging featuring both energy efficiency and feasibility. The proposed method is tested on a 12-lead ECG database consisting of 20 beats during normal sinus rhythm, 20 beats with myocardial infarction and 20 beats showing cardiomyopathy for 60 different subjects. The experiments give a recognition rate of 100% for a small number of RP coefficients (only 25), i.e. after a considerable dimensionality reduction of the input ECG signal. The results are very promising, not only from the classification performance point of view, but also while targeting a low-complexity feature extraction in terms of computation requirements and memory usage for real-time operation on a wireless wearable sensor platform.

A Multi-Lead ECG Classification Based on Random Projection Features

David Atienza Alonso, Iva Bogdanova Vandergheynst

This paper presents a novel method for classification of multilead electrocardiogram (ECG) signals. The feature extraction is based on the random projection (RP) concept for dimensionality reduction. Furthermore, the classification is performed by a neuro-fuzzy classifier. Such a model can be easily implemented on portable systems for practical applications in both health monitoring and diagnostic purposes. Moreover, the RP implementation on portable systems is very challenging featuring both energy efficiency and feasibility. The proposed method is tested on a 12-lead ECG database consisting of 20 beats during normal sinus rhythm, 20 beats with myocardial infarction and 20 beats showing cardiomyopathy for 60 different subjects. The experiments give a recognition rate of 100% for a small number of RP coefficients (only 25), i.e. after a considerable dimensionality reduction of the input ECG signal. The results are very promising, not only from the classification performance point of view, but also while targeting a low-complexity feature extraction in terms of computation requirements and memory usage for real-time operation on a wireless wearable sensor platform.

IEEE Press2012

Biologically inspired kinematic synergies enable linear balance control of a humanoid robot

Auke Ijspeert

Despite many efforts, balance control of humanoid robots in the presence of unforeseen external or internal forces has remained an unsolved problem. The difficulty of this problem is a consequence of the high dimensionality of the action space of a humanoid robot, due to its large number of degrees of freedom (joints), and of non-linearities in its kinematic chains. Biped biological organisms face similar difficulties, but have nevertheless solved this problem. Experimental data reveal that many biological organisms reduce the high dimensionality of their action space by generating movements through linear superposition of a rather small number of stereotypical combinations of simultaneous movements of many joints, to which we refer as kinematic synergies in this paper. We show that by constructing two suitable non-linear kinematic synergies for the lower part of the body of a humanoid robot, balance control can in fact be reduced to a linear control problem, at least in the case of relatively slow movements. We demonstrate for a variety of tasks that the humanoid robot HOAP-2 acquires through this approach the capability to balance dynamically against unforeseen disturbances that may arise from external forces or from manipulating unknown loads.

2011

Early Classification of Pathological Heartbeats on Wireless Body Sensor Nodes

Giovanni Ansaloni, David Atienza Alonso, Ivan Beretta, Ruben Braojos Lopez

Smart Wireless Body Sensor Nodes (WBSNs) are a novel class of unobtrusive, battery-powered devices allowing the continuous monitoring and real-time interpretation of a subject's bio-signals, such as the electrocardiogram (ECG). These low-power platforms, while able to perform advanced signal processing to extract information on heart conditions, are usually constrained in terms of computational power and transmission bandwidth. It is therefore essential to identify in the early stages which parts of an ECG are critical for the diagnosis and, only in these cases, activate on demand more detailed and computationally intensive analysis algorithms. In this work, we present a comprehensive framework for real-time automatic classification of normal and abnormal heartbeats, targeting embedded and resource-constrained WBSNs. In particular, we provide a comparative analysis of different strategies to reduce the heartbeat representation dimensionality, and therefore the required computational effort. We then combine these techniques with a neuro-fuzzy classification strategy, which effectively discerns normal and pathological heartbeats with a minimal run time and memory overhead. We prove that, by performing a detailed analysis only on the heartbeats that our classifier identifies as abnormal, a WBSN system can drastically reduce its overall energy consumption. Finally, we assess the choice of neuro-fuzzy classification by comparing its performance and workload with respect to other state-of-the-art strategies. Experimental results using the MIT-BIH Arrhythmia database show energy savings of as much as 60% in the signal processing stage, and 63% in the subsequent wireless transmission, when a neuro-fuzzy classification structure is employed, coupled with a dimensionality reduction technique based on random projections.

MDPI2014

Early Classification of Pathological Heartbeats on Wireless Body Sensor Nodes

Giovanni Ansaloni, David Atienza Alonso, Ivan Beretta, Ruben Braojos Lopez

MDPI2014