33 research outputs found
Performance metrics for characterization of a seizure detection algorithm for offline and online use
Purpose: To select appropriate previously reported performance metrics to evaluate a new seizure detection algorithm for offline and online analysis, and thus quantify any performance variation between these metrics. Methods: Traditional offline algorithms mark out any EEG section (epoch) of a seizure (event), so that neurologists only analyze the detected and adjacent sections. Thus, offline algorithms could be evaluated using number of correctly detected events, or event-based sensitivity (SEVENT), and epoch-based specificity (percentage of incorrectly detected background epochs). In contrast, online seizure detection (especially, data selection) algorithms select for transmission only the detected EEG sections and hence need to detect the entire duration of a seizure. Thus, online algorithms could be evaluated using percentage of correctly detected seizure duration, or epoch-based sensitivity (SEPOCH), and epoch-based specificity. Here, a new seizure detection algorithm is evaluated using the selected performance metrics for epoch duration ranging from 1s to 60s. Results: For 1s epochs, the area under the event-based sensitivity-specificity curve was 0.95 whilst SEPOCH achieves 0.81. This difference is not surprising, as intuitively, detecting any epoch within a seizure is easier than detecting every epoch - especially as seizures evolve over time. For longer epochs of 30s or 60s, SEVENT falls to 0.84 and 0.82 respectively and SEPOCH reduces to 0.76. Here, decreased SEVENT shows that fewer seizures are detected, possibly due to easy-to-detect short seizure sections being masked by surrounding EEG. However, detecting one long epoch constitutes a larger percentage of a seizure than a shorter one and thus SEPOCH does not decrease proportionately. Conclusions: Traditional offline and online seizure detection algorithms require different metrics to effectively evaluate their performance for their respective applications. Using such metrics, it has been shown that a decrease in performance may be expected when an offline seizure detection algorithm (especially with short epoch duration) is used for online analysis.Accepted versio
A novel phase congruency based algorithm for online data reduction in ambulatory EEG systems
Accepted versio
Improving phase congruency for EEG data reduction.
Published versio
Data reduction algorithms to enable long-term monitoring from low-power miniaturised wireless EEG systems
Objectives: The weight and volume of battery-powered wireless electroencephalography
(EEG) systems are dominated by the batteries. Battery dimensions are in
turn determined by the required energy capacity, which is derived from the system
power consumption and required monitoring time. Data reduction may be carried
out to reduce the amount of data transmitted and thus proportionally reduce
the power consumption of the wireless transmitter, which dominates system power
consumption. This thesis presents two new data selection algorithms that, in addition
to achieving data reduction, also select EEG containing epileptic seizures and
spikes that are important in diagnosis.
Methods: The algorithms analyse short EEG sections, during monitoring, to
determine the presence of candidate seizures or spikes. Phase information from
different frequency components of the signal are used to detect spikes. For seizure
detection, frequencies below 10 Hz are investigated for a relative increase in frequency
and/or amplitude.
Significant attention has also been given to metrics in order to accurately evaluate
the performance of these algorithms for practical use in the proposed system.
Additionally, signal processing techniques to emphasize seizures within the EEG
and techniques to correct for broad-level amplitude variation in the EEG have been
investigated.
Results: The spike detection algorithm detected 80% of spikes whilst achieving
50% data reduction, when tested on 992 spikes from 105 hours of 10-channel scalp
EEG data obtained from 25 adults. The seizure detection algorithm identified 94%
of seizures selecting 80% of their duration for transmission and achieving 79% data
reduction. It was tested on 34 seizures with a total duration of 4158 s in a database
of over 168 hours of 16-channel scalp EEG obtained from 21 adults. These algorithms
show great potential for longer monitoring times from miniaturised wireless
EEG systems that would improve electroclinical diagnosis of patients
A machine learning system for automated whole-brain seizure detection
Epilepsy is a chronic neurological condition that affects approximately 70 million people worldwide. Characterised by sudden bursts of excess electricity in the brain, manifesting as seizures, epilepsy is still not well understood when compared with other neurological disorders. Seizures often happen unexpectedly and attempting to predict them has been a research topic for the last 30 years. Electroencephalograms have been integral to these studies, as the recordings that they produce can capture the brain’s electrical signals. The diagnosis of epilepsy is usually made by a neurologist, but can be difficult to make in the early stages. Supporting para-clinical evidence obtained from magnetic resonance imaging and electroencephalography may enable clinicians to make a diagnosis of epilepsy and instigate treatment earlier. However, electroencephalogram capture and interpretation is time consuming and can be expensive due to the need for trained specialists to perform the interpretation. Automated detection of correlates of seizure activity generalised across different regions of the brain and across multiple subjects may be a solution. This paper explores this idea further and presents a supervised machine learning approach that classifies seizure and non-seizure records using an open dataset containing 342 records (171 seizures and 171 non-seizures). Our approach posits a new method for generalising seizure detection across different subjects without prior knowledge about the focal point of seizures. Our results show an improvement on existing studies with 88% for sensitivity, 88% for specificity and 93% for the area under the curve, with a 12% global error, using the k-NN classifier