Generalized Models for the Classification of Abnormal Movements in Daily Life and its Applicability to Epilepsy Convulsion Recognition

Abstract

The identification and the modeling of epilepsy convulsions during everyday life using wearable devices would enhance patient anamnesis and monitoring. The psychology of the epilepsy patient penalizes the use of user-driven modeling, which means that the probability of identifying convulsions is driven through generalized models. Focusing on clonic convulsions, this pre-clinical study proposes a method for generating a type of model that can evaluate the generalization capabilities. A realistic experimentation with healthy participants is performed, each with a single 3D accelerometer placed on the most affected wrist. Unlike similar studies reported in the literature, this proposal makes use of [Formula: see text] cross-validation scheme, in order to evaluate the generalization capabilities of the models. Event-based error measurements are proposed instead of classification-error measurements, to evaluate the generalization capabilities of the model, and Fuzzy Systems are proposed as the generalization modeling technique. Using this method, the experimentation compares the most common solutions in the literature, such as Support Vector Machines, [Formula: see text]-Nearest Neighbors, Decision Trees and Fuzzy Systems. The event-based error measurement system records the results, penalizing those models that raise false alarms. The results showed the good generalization capabilities of Fuzzy Systems.

An IoT Platform for Epilepsy Monitoring and Supervising

“…As mentioned in the related work section, there are several studies for detecting the focal myoclonic type of seizure, while further study is needed to detect the second focused type. In order to detect the focal myoclonic seizures, the research published in [3,65] is proposed. Further study is needed to solve the second type of seizures.…”

“…The sampling frequency of the sensory system should depend on the physical measurements: accelerometers need sampling frequencies higher than 10 Hz [3]; HR needs smaller sampling frequencies. However, the majority of the commercial Smartwatches or Smartbands do not allow apps to sample data from the sensory system: they only allow access to calculated transformations or induced variables.…”

“…The main part of these platforms has been developed for two different and most frequent kinds of epilepsy crisis: the generalized tonic-clonic seizures and the typical absence seizures [2]. In these two cases, the detection of a seizure can be efficiently faced using wearable sensors (WDs) including a triaxial accelerometer (ACM) and/or a heart rate (HR) sensor: the former type detection has been reported in [3], the latter one has been characterized in [4], and an HR-based detection system has been proposed in [5].…”

“…For instance, a wrist band including an ACM connected to a Smartphone was proposed for MCC-based seizure detection [3,6,49]; no further connection with CC services was considered. ACM, gyroscopes, and magnetometers were proposed in [9] as the BSN, taking advantage of the local Smartphone for analyzing the data and sending e-mails to the medical staff with the patient position.…”

A Type-Based Blocking Technique for Efficient Entity Resolution over Large-Scale Data

“…Fourth, the performance of typical windowing methods (SNM or MPN) depends strongly on the size of sliding window [21], but they often employ a fixed window size. The larger the fixed window size, the more comparisons are executed, and the lower the overall efficiency gets, however, small size may lead to a high number of missed matches (e.g., the closest entities are not placed in the same window) and to low effectiveness [2,[21][22][23].…”