Automated Epileptic Seizure Detection Based on Wearable ECG and PPG in a Hospital Environment

Overview

Journal Sensors (Basel)

Publisher MDPI

Specialty Biotechnology

Date 2017 Oct 14

PMID 29027928

Citations 39

Authors

Kaat Vandecasteele

Thomas De Cooman

Ying Gu

Evy Cleeren

Kasper Claes

Wim Van Paesschen

Sabine Van Huffel

Borbala Hunyadi

Affiliations

Soon will be listed here.

Abstract

Electrocardiography has added value to automatically detect seizures in temporal lobe epilepsy (TLE) patients. The wired hospital system is not suited for a long-term seizure detection system at home. To address this need, the performance of two wearable devices, based on electrocardiography (ECG) and photoplethysmography (PPG), are compared with hospital ECG using an existing seizure detection algorithm. This algorithm classifies the seizures on the basis of heart rate features, extracted from the heart rate increase. The algorithm was applied to recordings of 11 patients in a hospital setting with 701 h capturing 47 (fronto-)temporal lobe seizures. The sensitivities of the hospital system, the wearable ECG device and the wearable PPG device were respectively 57%, 70% and 32%, with corresponding false alarms per hour of 1.92, 2.11 and 1.80. Whereas seizure detection performance using the wrist-worn PPG device was considerably lower, the performance using the wearable ECG is proven to be similar to that of the hospital ECG.

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