Automatic Seizure Detection Using the Pulse Transit Time

TL;DR

This paper proposes a novel algorithm for noninvasive seizure detection using pulse transit time (PTT), addressing challenges like clock drift and subtle symptoms, and demonstrates improved detection with a multimodal approach.

Contribution

The study introduces an algorithm that accounts for clock drift in PTT measurements and explores its use in seizure detection, enhancing accuracy especially for subtle symptoms.

Findings

PTT can be used to detect seizures with a Random Forest classifier.

The algorithm effectively compensates for clock drift in separated sensors.

Multimodal approaches improve detection of subtle seizure symptoms.

Abstract

Documentation of epileptic seizures plays an essential role in planning medical therapy. Solutions for automated epileptic seizure detection can help improve the current problem of incomplete and erroneous manual documentation of epileptic seizures. In recent years, a number of wearable sensors have been tested for this purpose. However, detecting seizures with subtle symptoms remains difficult and current solutions tend to have a high false alarm rate. Seizures can also affect the patient's arterial blood pressure, which has not yet been studied for detection with sensors. The pulse transit time (PTT) provides a noninvasive estimate of arterial blood pressure. It can be obtained by using to two sensors, which are measuring the time differences between arrivals of the pulse waves. Due to separated time chips a clock drift emerges, which is strongly influencing the PTT. In this work, we present an algorithm which responds to alterations in the PTT, considering the clock drift and enabling the noninvasive monitoring of blood pressure alterations using separated sensors. Furthermore we investigated whether seizures can be detected using the PTT. Our results indicate that using the algorithm, it is possible to detect seizures with a Random Forest. Using the PTT along with other signals in a multimodal approach, the detection of seizures with subtle symptoms could thereby be improved.