Multi-task seizure detection: addressing intra-patient variation in seizure morphologies

Abstract

The accurate and early detection of epileptic seizures in continuous electroencephalographic (EEG) data has a growing role in the management of patients with epilepsy. Early detection allows for therapy to be delivered at the start of seizures and for caregivers to be notified promptly about potentially debilitating events. The challenge to detecting epileptic seizures, however, is that seizure morphologies exhibit considerable inter-patient and intrapatient variability. While recent work has looked at addressing the issue of variations across different patients (inter-patient variability) and described patient-specific methodologies for seizure detection, there are no examples of systems that can simultaneously address the challenges of inter-patient and intra-patient variations in seizure morphology. In our study, we address this complete goal and describe a multi-task learning approach that trains a classifier to perform well across many kinds of seizures rather than potentially overfitting to the most common seizure types. Our approach increases the generalizability of seizure detection systems and improves the tradeoff between latency and sensitivity versus false positive rates. When compared against the standard approach on the CHB-MIT multi-channel scalp EEG data, our proposed method improved discrimination between seizure and non-seizure EEG for almost 83 % of the patients while reducing false positives on nearly 70 % of the patients studied.

EEG Synchronization Analysis for Seizure Prediction: A Study on Data of Noninvasive Recordings

“…However, in our approach, PLI and WPLI are computed on signals obtained by raw EEG after some preprocessing steps reported in Figure 1 and explained in the following. EEG signals are first filtered in the [8,13] Hz band (such a band has been identified by preliminary experimental campaign in which different bands have been considered): The filtering process allows the selection of the band of frequencies of interest, possibly removing undesired artifacts. Then, signals are differentiated (i.e., the absolute value of the time-derivative of the signal is computed [26]): the differentiation of the signal makes the basic noise nearly flat and sharpens the regions where the signal exhibits its peaks (see [26,27]), which, as shown below, are most likely to be the regions where seizures occur.…”

“…Then, signals are differentiated (i.e., the absolute value of the time-derivative of the signal is computed [26]): the differentiation of the signal makes the basic noise nearly flat and sharpens the regions where the signal exhibits its peaks (see [26,27]), which, as shown below, are most likely to be the regions where seizures occur. As an example, Figure 2 shows the behaviors of PLI and WPLI without and with differentiation on a time span of 1000 s preceding the first seizure of patient PN01 of the database, computed on the channel pair (T5, Pz), and filtered in the [8,13] Hz band. In the figure, the seizure starts (ends) at the first (second) vertical dotted line.…”

“…Seizure prediction is a challenging research topic that has been explored for over 25 years. Many approaches are based on the analysis of electroencephalogram (EEG) signals (e.g., see, in [3,4]), and on the development of machine learning methods [5][6][7][8], usually requiring a large amount of data for training. However, physiological data (e.g., EEG and EKG) including seizures are in general scarce, often including artifacts and noise.…”

Real-Time Automatic Seizure Detection Using Ordinary Kriging Method in an Edge-IoMT Computing Paradigm

“…Training the combined EEG data of 22 patients will be highly computationally expensive due to the training complexity. Most of the works where all 22 patients data were utilized only described the patient-specific approach, in which case training is done for each patient's data as seen in [1,3,41,46]. Furthermore, results obtained from the selected 5 patients is a true representation of the variation in performances from patient to patient when compared to other works where 22 patients have been used.…”

Towards an Online Seizure Advisory System—An Adaptive Seizure Prediction Framework Using Active Learning Heuristics

“…It is a well-established fact that brain signals are inherently uncertain, and pre-ictal signals may vary for different types of seizures [ 16 , 39 , 42 ]. Moreover, pre-ictal signals can temporally vary for the same seizure and even for the same patient.…”