Automatic Detection and Classification of Convulsive Psychogenic Nonepileptic Seizures Using a Wearable Device

Abstract

Epilepsy is one of the most common neurological disorders and patients suffer from unprovoked seizures. In contrast, psychogenic nonepileptic seizures (PNES) are another class of seizures that are involuntary events not caused by abnormal electrical discharges but are a manifestation of psychological distress. The similarity of these two types of seizures poses diagnostic challenges that often leads in delayed diagnosis of PNES. Further, the diagnosis of PNES involves high-cost hospital admission and monitoring using video-electroencephalogram machines. A wearable device that can monitor the patient in natural setting is a desired solution for diagnosis of convulsive PNES. A wearable device with an accelerometer sensor is proposed as a new solution in the detection and diagnosis of PNES. The seizure detection algorithm and PNES classification algorithm are developed. The developed algorithms are tested on data collected from convulsive epileptic patients. A very high seizure detection rate is achieved with 100% sensitivity and few false alarms. A leave-one-out error of 6.67% is achieved in PNES classification, demonstrating the usefulness of wearable device in the diagnosis of PNES.

Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: a mixed-methods systematic review

“…In epilepsy, wrist-worn sensors with built-in accelerometers were used for detection and classification of seizures in hospital settings [14–18]. In PD, wearables were used to detect and quantify cardinal motor symptoms including bradykinesia [19], tremor [20, 21], and postural sway [22, 23] as well as medication-evoked adverse symptoms such as dyskinesia [24–27] and motor fluctuations [28, 29].…”

The utility of an automated and ambulatory device for detecting and differentiating epileptic and psychogenic non‐epileptic seizures

“…Another reason for the reduced number of events was the 20‐seconds exclusion criterion, which caused the elimination of a number of potential events that could have been otherwise analyzed. As mentioned earlier, this time limit was utilized when developing the algorithm on the training data set, as stated in Gubbi et al's study . Upon further consulting, clinical specialists recommended the 20‐seconds time limit be used as a threshold to ensure any continuous movement are most likely seizure‐like movements.…”

“…These filtered signals were then pre‐processed. Further details regarding this process can be seen in Gubbi et al's study . The pre‐processing ensured that all subtle movements were excluded from any seizure‐like activity.…”

“…Several time domain features were extracted from these signals, including slope sign changes, waveform length, and variance. Of these, signal power, zero crossing, and standard deviation were selected as key features …”

“…This therefore has potential to be incorporated into seizure alert systems which would have great safety value for patients, particularly those who live alone, are at risk from injury during convulsive seizures, or having nocturnal seizures that are usually unwitnessed. The algorithm incorporated into the automated device has been developed by our group previously, using data acquired from motor seizures, both epileptic and psychogenic . This study aims to evaluate the accuracy of this automated and ambulatory diagnostic system, and validate its ability in differentiating PNES and ES in an independent cohort of patients.…”