Tonic-clonic seizure detection using accelerometry-based wearable sensors: A prospective, video-EEG controlled study

Abstract

A B S T R A C TPurpose: The aim of this prospective, video-electroencephalography (video-EEG) controlled study was to evaluate the performance of an accelerometry-based wearable system to detect tonic-clonic seizures (TCSs) and to investigate the accuracy of different seizure detection algorithms using separate training and test data sets. Methods: Seventy-five epilepsy surgery candidates undergoing video-EEG monitoring were included. The patients wore one three-axis accelerometer on each wrist during video-EEG. The accelerometer data was band-pass filtered and reduced using a movement threshold and mapped to a time-frequency feature space representation. Algorithms based on standard binary classifiers combined with a TCS specific event detection layer were developed and trained using the training set. Their performance was evaluated in terms of sensitivity and false positive (FP) rate using the test set. Results: Thirty-seven available TCSs in 11 patients were recorded and the data was divided into disjoint training (27 TCSs, three patients) and test (10 TCSs, eight patients) data sets. The classification algorithms evaluated were K-nearest-neighbors (KNN), random forest (RF) and a linear kernel support vector machine (SVM). For the TCSs detection performance of the three algorithms in the test set, the highest sensitivity was obtained for KNN (100% sensitivity, 0.05 FP/h) and the lowest FP rate was obtained for RF (90% sensitivity, 0.01 FP/h). Conclusions: The low FP rate enhances the clinical utility of the detection system for long-term reliable seizure monitoring. It also allows a possible implementation of an automated TCS detection in free-living environment, which could contribute to ascertain seizure frequency and thereby better seizure management. Recent advances in accelerometry technology have allowed the development of ambulatory monitoring systems able to detect changes in movement frequency and amplitude associated with motor seizures, and to differentiate these patterns from daily voluntary movements. Accelerometry-based sensors are usually small, portable and easy to use.

Wearable Devices: Current Status and Opportunities in Pain Assessment and Management

“…Wearable technology is rapidly evolving, with newer devices being capable of recording multiple biosignal measurements that could provide a more comprehensive and detailed quantification of the health status of an individual, including measures of sleep [8][9][10], weight management [11], aging [12,13], and mental health [14,15]. Such devices are already used to better contextualize and understand patient health in a variety of medical specialties, including heart rate and EKG analysis [16][17][18], which may facilitate the early diagnosis of arrhythmias in cardiology [19][20][21][22][23], the assessment of Parkinson progression [24], multiple sclerosis [25], epilepsy [26,27], and stroke rehabilitation [28,29] in neurology, and sweatbased glucose monitoring [30] for patients with diabetes [31][32][33], among others. The number and type of measurements are also rapidly expanding, with some devices targeting pH, lactic acid, and electrolytes such as sodium and potassium on the surface of the skin [34].…”

The Challenging Path to Developing a Mobile Health Device for Epilepsy: The Current Landscape and Where We Go From Here

“…They are easily integrated in fashionable wearables, such as bracelets, without displaying the disease and stigmatizing patients. Accelerometers strapped to a limb appropriately identify GTCS, and other seizures with strong motor components (54)(55)(56). Surface EMG on the biceps muscle is particularly useful for the detection of tonic seizures early on in the course of GTCS (57)(58)(59)(60).…”

“…An alternative GTCS detection method using wrist accelerometers has approached a relatively high sensitivity of >89.7% in several phase 2 studies, with the rate of false alarms significantly reduced <0.24 per day (32,55,(95)(96)(97)(98). ACCs are most effective when attached to several areas of a patient's body known to be implicated in the seizure semiology.…”

Detection of Generalized Tonic Clonic Seizures and Falls in Unconstraint Environment Using Smartphone Accelerometer

“…A trial to detect Tonic Clonic seizures was conducted in [38], tri-axial accelerometers were attached to both wrists of the patients who were undergoing Video-EEG. False Positive Rate (FPR) and sensitivity are used as a performance parameter learned through the Linear Kernel Support Vector Machine (LKSVM), Random Forest (RF) and K-Nearest Neighbors (KNN).…”