MOLEC: Anywhere and at Any Time Arrhythmia Classification

The new advances in sensor technology, PDAs and wireless communications favor the development of a new type of monitoring systems that can provide patients with assistance anywhere and at any time. Of particular interest are the monitoring systems designed for people that suffer from heart arrhythmias, due to the increasing number of people with cardiovascular diseases. In this paper, we present MOLEC: a PDA-based system that performs local real-time classification and detects the ECG anomalies in situ. In the actual implementation of MOLEC, the signal is acquired by some ECG sensors (ActiveECG sensors) with a 360Hz frequency. For the preprocessing of the signal the ECGPUWAVE tool and an automata developed by us that identifies the beats are used. We have also developed a beat and rhythm classifiers that determine if there has been an anomalous rhythm, and in that case, an alarm is sent to a hospital via wireless communications. The rhythm detection delay of the MOLEC system is of 6.66 seconds. classifier that can run in real-time into a PDA. In this paper we show first the framework of the system, then in section 3 the process that we followed in order to select a beat and rhythm classifier that provides an accuracy result and in section 4 we show some performance aspects considering PDAs resources. 2. Framework of the MOLEC system In this section we explain the four components that form the global architecture of MOLEC (see figure 1): the ECG sensor, the Monitor Molec, Molec Center and the users of the system (hospital and relative computers). 1

Using Kinect to classify Parkinson's disease stages related to severity of gait impairment

Background: Parkinson’s Disease (PD) is a chronic neurodegenerative disease associated with motor problems such as gait impairment. Different systems based on 3D cameras, accelerometers or gyroscopes have been used in related works in order to study gait disturbances in PD. Kinect has also been used to build these kinds of systems, but contradictory results have been reported: some works conclude that Kinect does not provide an accurate method of measuring gait kinematics variables, but others, on the contrary, report good accuracy results. Methods: In this work, we have built a Kinect-based system that can distinguish between different PD stages, and have performed a clinical study with 30 patients suffering from PD belonging to three groups: early PD patients without axial impairment, more evolved PD patients with higher gait impairment but without Freezing of Gait (FoG), and patients with advanced PD and FoG. Those patients were recorded by two Kinect devices when they were walking in a hospital corridor. The datasets obtained from the Kinect were preprocessed, 115 features identified, some methods were applied to select the relevant features (correlation based feature selection, information gain, and consistency subset evaluation), and different classification methods (decision trees, Bayesian networks, neural networks and K-nearest neighbours classifiers) were evaluated with the goal of finding the most accurate method for PD stage classification. Results: The classifier that provided the best results is a particular case of a Bayesian Network classifier (similar to a Naïve Bayesian classifier) built from a set of 7 relevant features selected by the correlation-based on feature selection method. The accuracy obtained for that classifier using 10-fold cross validation is 93.40%. The relevant features are related to left shin angles, left humerus angles, frontal and lateral bents, left forearm angles and the number of steps during spin. Conclusions: In this paper, it is shown that using Kinect is adequate to build a inexpensive and comfortable system that classifies PD into three different stages related to FoG. Compared to the results of previous works, the obtained accuracy (93.40%) can be considered high. The relevant features for the classifier are: a) movement and position of the left arm, b) trunk position for slightly displaced walking sequences, and c) left shin angle, for straight walking sequences. However, we have obtained a better accuracy (96.23%) for a classifier that only uses features extracted from slightly displaced walking steps and spin walking steps. Finally, the obtained set of relevant features may lead to new rehabilitation therapies for PD patients with gait problems

MOLEC: Anywhere and at Any Time Arrhythmia Classification

The new advances in sensor technology, PDAs and wireless communications favor the development of a new type of monitoring systems that can provide patients with assistance anywhere and at any time. Of particular interest are the monitoring systems designed for people that suffer from heart arrhythmias, due to the increasing number of people with cardiovascular diseases. In this paper, we present MOLEC: a PDA-based system that performs local real-time classification and detects the ECG anomalies in situ. In the actual implementation of MOLEC, the signal is acquired by some ECG sensors (ActiveECG sensors) with a 360Hz frequency. For the preprocessing of the signal the ECGPUWAVE tool and an automata developed by us that identifies the beats are used. We have also developed a beat and rhythm classifiers that determine if there has been an anomalous rhythm, and in that case, an alarm is sent to a hospital via wireless communications. The rhythm detection delay of the MOLEC system is of 6.66 seconds. 1